A JavaScript library to add search functionality to any Jekyll blog.
You have a blog, built with Jekyll, and want a lightweight search functionality on your blog, purely client-side?
No server configurations or databases to maintain.
Just 5 minutes to have a fully working searchable blog.
npm install simple-jekyll-search
search.jsonPlace the following code in a file called search.json in the root of your Jekyll blog. (You can also get a copy from here)
This file will be used as a small data source to perform the searches on the client side:
---
layout: none
---
[
{
"title" : "Make your ROS & Eigen C++ code compile faster with precompiled headers",
"category" : "",
"tags" : "",
"url" : "/2023/11/26/ros-eigen-compile-times/",
"date" : "2023-11-26 00:00:00 +0000"
} ,
{
"title" : "Steering Towards Success: AI-Navigation/Controls Subteam's Progress on Autonomous Navigation",
"category" : "",
"tags" : "",
"url" : "/2023/11/12/nav-controls-update/",
"date" : "2023-11-12 00:00:00 +0000"
} ,
{
"title" : "ECE Family Fun Night",
"category" : "",
"tags" : "",
"url" : "/2023/10/01/ece-family-fun/",
"date" : "2023-10-01 00:00:00 +0000"
} ,
{
"title" : "Sponsor Highlight: Boeing!",
"category" : "",
"tags" : "",
"url" : "/2023/09/23/boeing-meeting/",
"date" : "2023-09-23 00:00:00 +0000"
} ,
{
"title" : "Ford Motor Company's Immersion Day",
"category" : "",
"tags" : "",
"url" : "/2023/08/29/ford-cars/",
"date" : "2023-08-29 00:00:00 +0000"
} ,
{
"title" : "From High School to Leading UM::Autonomy at University of Michigan",
"category" : "",
"tags" : "",
"url" : "/2023/08/05/asheya/",
"date" : "2023-08-05 00:00:00 +0000"
}
]
SimpleJekyllSearch needs two DOM elements to work:
Here is the code you can use with the default configuration:
You need to place the following code within the layout where you want the search to appear. (See the configuration section below to customize it)
For example in _layouts/default.html:
<!-- HTML elements for search -->
<input type="text" id="search-input" placeholder="Search blog posts..">
<ul id="results-container"></ul>
<!-- or without installing anything -->
<script src="https://unpkg.com/simple-jekyll-search@latest/dest/simple-jekyll-search.min.js"></script>
Customize SimpleJekyllSearch by passing in your configuration options:
var sjs = SimpleJekyllSearch({
searchInput: document.getElementById('search-input'),
resultsContainer: document.getElementById('results-container'),
json: '/search.json'
})
A new instance of SimpleJekyllSearch returns an object, with the only property search.
search is a function used to simulate a user input and display the matching results.
E.g.:
var sjs = SimpleJekyllSearch({ ...options })
sjs.search('Hello')
💡 it can be used to filter posts by tags or categories!
Here is a list of the available options, usage questions, troubleshooting & guides.
The input element on which the plugin should listen for keyboard event and trigger the searching and rendering for articles.
The container element in which the search results should be rendered in. Typically a <ul>.
You can either pass in an URL to the search.json file, or the results in form of JSON directly, to save one round trip to get the data.
The template of a single rendered search result.
The templating syntax is very simple: You just enclose the properties you want to replace with curly braces.
E.g.
The template
var sjs = SimpleJekyllSearch({
searchInput: document.getElementById('search-input'),
resultsContainer: document.getElementById('results-container'),
json: '/search.json',
searchResultTemplate: '<li><a href="https://umautonomy.com{url}">{title}</a></li>'
})
will render to the following
<li><a href="/jekyll/update/2014/11/01/welcome-to-jekyll.html">Welcome to Jekyll!</a></li>
If the search.json contains this data
[
{
"title" : "Welcome to Jekyll!",
"category" : "",
"tags" : "",
"url" : "/jekyll/update/2014/11/01/welcome-to-jekyll.html",
"date" : "2014-11-01 21:07:22 +0100"
}
]
A function that will be called whenever a match in the template is found.
It gets passed the current property name, property value, and the template.
If the function returns a non-undefined value, it gets replaced in the template.
This can be potentially useful for manipulating URLs etc.
Example:
SimpleJekyllSearch({
...
templateMiddleware: function(prop, value, template) {
if (prop === 'bar') {
return value.replace(/^\//, '')
}
}
...
})
See the tests for an in-depth code example
A function that will be used to sort the filtered results.
It can be used for example to group the sections together.
Example:
SimpleJekyllSearch({
...
sortMiddleware: function(a, b) {
var astr = String(a.section) + "-" + String(a.caption);
var bstr = String(b.section) + "-" + String(b.caption);
return astr.localeCompare(bstr)
}
...
})
The HTML that will be shown if the query didn’t match anything.
You can limit the number of posts rendered on the page.
Enable fuzzy search to allow less restrictive matching.
Pass in a list of terms you want to exclude (terms will be matched against a regex, so URLs, words are allowed).
A function called once the data has been loaded.
Limit how many times the search function can be executed over the given time window. This is especially useful to improve the user experience when searching over a large dataset (either with rare terms or because the number of posts to display is large). If no debounceTime (milliseconds) is provided a search will be triggered on each keystroke.
remove_chars as a filter.For example: in search.json, replace
"content": "# [Simple-Jekyll-Search](https://www.npmjs.com/package/simple-jekyll-search)[](https://travis-ci.org/christian-fei/Simple-Jekyll-Search)[](https://david-dm.org/christian-fei/Simple-Jekyll-Search)[](https://david-dm.org/christian-fei/Simple-Jekyll-Search?type=dev)A JavaScript library to add search functionality to any Jekyll blog.## Use caseYou have a blog, built with Jekyll, and want a **lightweight search functionality** on your blog, purely client-side?*No server configurations or databases to maintain*.Just **5 minutes** to have a **fully working searchable blog**.---## Installation### npm```shnpm install simple-jekyll-search```## Getting started### Create `search.json`Place the following code in a file called `search.json` in the **root** of your Jekyll blog. (You can also get a copy [from here](/example/search.json))This file will be used as a small data source to perform the searches on the client side:```yaml---layout: none---[ {% for post in site.posts %} { "title" : "{{ post.title | escape }}", "category" : "{{ post.category }}", "tags" : "{{ post.tags | join: ', ' }}", "url" : "{{ site.baseurl }}{{ post.url }}", "date" : "{{ post.date }}" } {% unless forloop.last %},{% endunless %} {% endfor %}]```## Preparing the plugin### Add DOM elementsSimpleJekyllSearch needs two `DOM` elements to work:- a search input field- a result container to display the results#### Give me the codeHere is the code you can use with the default configuration:You need to place the following code within the layout where you want the search to appear. (See the configuration section below to customize it)For example in **_layouts/default.html**:```html```## UsageCustomize SimpleJekyllSearch by passing in your configuration options:```jsvar sjs = SimpleJekyllSearch({ searchInput: document.getElementById('search-input'), resultsContainer: document.getElementById('results-container'), json: '/search.json'})```### returns { search }A new instance of SimpleJekyllSearch returns an object, with the only property `search`.`search` is a function used to simulate a user input and display the matching results. E.g.:```jsvar sjs = SimpleJekyllSearch({ ...options })sjs.search('Hello')```💡 it can be used to filter posts by tags or categories!## OptionsHere is a list of the available options, usage questions, troubleshooting & guides.### searchInput (Element) [required]The input element on which the plugin should listen for keyboard event and trigger the searching and rendering for articles.### resultsContainer (Element) [required]The container element in which the search results should be rendered in. Typically a ``.### json (String|JSON) [required]You can either pass in an URL to the `search.json` file, or the results in form of JSON directly, to save one round trip to get the data.### searchResultTemplate (String) [optional]The template of a single rendered search result.The templating syntax is very simple: You just enclose the properties you want to replace with curly braces.E.g.The template```jsvar sjs = SimpleJekyllSearch({ searchInput: document.getElementById('search-input'), resultsContainer: document.getElementById('results-container'), json: '/search.json', searchResultTemplate: '{title}'})```will render to the following```htmlWelcome to Jekyll!```If the `search.json` contains this data```json[ { "title" : "Welcome to Jekyll!", "category" : "", "tags" : "", "url" : "/jekyll/update/2014/11/01/welcome-to-jekyll.html", "date" : "2014-11-01 21:07:22 +0100" }]```### templateMiddleware (Function) [optional]A function that will be called whenever a match in the template is found.It gets passed the current property name, property value, and the template.If the function returns a non-undefined value, it gets replaced in the template.This can be potentially useful for manipulating URLs etc.Example:```jsSimpleJekyllSearch({ ... templateMiddleware: function(prop, value, template) { if (prop === 'bar') { return value.replace(/^\//, '') } } ...})```See the [tests](https://github.com/christian-fei/Simple-Jekyll-Search/blob/master/tests/Templater.test.js) for an in-depth code example### sortMiddleware (Function) [optional]A function that will be used to sort the filtered results.It can be used for example to group the sections together.Example:```jsSimpleJekyllSearch({ ... sortMiddleware: function(a, b) { var astr = String(a.section) + "-" + String(a.caption); var bstr = String(b.section) + "-" + String(b.caption); return astr.localeCompare(bstr) } ...})```### noResultsText (String) [optional]The HTML that will be shown if the query didn't match anything.### limit (Number) [optional]You can limit the number of posts rendered on the page.### fuzzy (Boolean) [optional]Enable fuzzy search to allow less restrictive matching.### exclude (Array) [optional]Pass in a list of terms you want to exclude (terms will be matched against a regex, so URLs, words are allowed).### success (Function) [optional]A function called once the data has been loaded.### debounceTime (Number) [optional]Limit how many times the search function can be executed over the given time window. This is especially useful to improve the user experience when searching over a large dataset (either with rare terms or because the number of posts to display is large). If no `debounceTime` (milliseconds) is provided a search will be triggered on each keystroke.---## If search isn't working due to invalid JSON- There is a filter plugin in the _plugins folder which should remove most characters that cause invalid JSON. To use it, add the simple_search_filter.rb file to your _plugins folder, and use `remove_chars` as a filter.For example: in search.json, replace```json"content": "{{ page.content | strip_html | strip_newlines }}"```with```json"content": "{{ page.content | strip_html | strip_newlines | remove_chars | escape }}"```If this doesn't work when using Github pages you can try `jsonify` to make sure the content is json compatible:```js"content": {{ page.content | jsonify }}```**Note: you don't need to use quotes `"` in this since `jsonify` automatically inserts them.**## Enabling full-text searchReplace `search.json` with the following code:```yaml---layout: none---[ {% for post in site.posts %} { "title" : "{{ post.title | escape }}", "category" : "{{ post.category }}", "tags" : "{{ post.tags | join: ', ' }}", "url" : "{{ site.baseurl }}{{ post.url }}", "date" : "{{ post.date }}", "content" : "{{ post.content | strip_html | strip_newlines }}" } {% unless forloop.last %},{% endunless %} {% endfor %} , {% for page in site.pages %} { {% if page.title != nil %} "title" : "{{ page.title | escape }}", "category" : "{{ page.category }}", "tags" : "{{ page.tags | join: ', ' }}", "url" : "{{ site.baseurl }}{{ page.url }}", "date" : "{{ page.date }}", "content" : "{{ page.content | strip_html | strip_newlines }}" {% endif %} } {% unless forloop.last %},{% endunless %} {% endfor %}]```## Development- `npm install`- `npm test`#### Acceptance tests```bashcd example; jekyll serve# in another tabnpm run cypress -- run```## ContributorsThanks to all [contributors](https://github.com/christian-fei/Simple-Jekyll-Search/graphs/contributors) over the years! You are the best :)> [@daviddarnes](https://github.com/daviddarnes)[@XhmikosR](https://github.com/XhmikosR)[@PeterDaveHello](https://github.com/PeterDaveHello)[@mikeybeck](https://github.com/mikeybeck)[@egladman](https://github.com/egladman)[@midzer](https://github.com/midzer)[@eduardoboucas](https://github.com/eduardoboucas)[@kremalicious](https://github.com/kremalicious)[@tibotiber](https://github.com/tibotiber)and many others!## Stargazers over time[](https://starchart.cc/christian-fei/Simple-Jekyll-Search)"
with
"content": "# [Simple-Jekyll-Search](https://www.npmjs.com/package/simple-jekyll-search)[](https://travis-ci.org/christian-fei/Simple-Jekyll-Search)[](https://david-dm.org/christian-fei/Simple-Jekyll-Search)[](https://david-dm.org/christian-fei/Simple-Jekyll-Search?type=dev)A JavaScript library to add search functionality to any Jekyll blog.## Use caseYou have a blog, built with Jekyll, and want a **lightweight search functionality** on your blog, purely client-side?*No server configurations or databases to maintain*.Just **5 minutes** to have a **fully working searchable blog**.---## Installation### npm```shnpm install simple-jekyll-search```## Getting started### Create `search.json`Place the following code in a file called `search.json` in the **root** of your Jekyll blog. (You can also get a copy [from here](/example/search.json))This file will be used as a small data source to perform the searches on the client side:```yaml---layout: none---[ {% for post in site.posts %} { "title" : "{{ post.title | escape }}", "category" : "{{ post.category }}", "tags" : "{{ post.tags | join: ', ' }}", "url" : "{{ site.baseurl }}{{ post.url }}", "date" : "{{ post.date }}" } {% unless forloop.last %},{% endunless %} {% endfor %}]```## Preparing the plugin### Add DOM elementsSimpleJekyllSearch needs two `DOM` elements to work:- a search input field- a result container to display the results#### Give me the codeHere is the code you can use with the default configuration:You need to place the following code within the layout where you want the search to appear. (See the configuration section below to customize it)For example in **_layouts/default.html**:```html```## UsageCustomize SimpleJekyllSearch by passing in your configuration options:```jsvar sjs = SimpleJekyllSearch({ searchInput: document.getElementById('search-input'), resultsContainer: document.getElementById('results-container'), json: '/search.json'})```### returns { search }A new instance of SimpleJekyllSearch returns an object, with the only property `search`.`search` is a function used to simulate a user input and display the matching results. E.g.:```jsvar sjs = SimpleJekyllSearch({ ...options })sjs.search('Hello')```💡 it can be used to filter posts by tags or categories!## OptionsHere is a list of the available options, usage questions, troubleshooting & guides.### searchInput (Element) [required]The input element on which the plugin should listen for keyboard event and trigger the searching and rendering for articles.### resultsContainer (Element) [required]The container element in which the search results should be rendered in. Typically a ``.### json (String|JSON) [required]You can either pass in an URL to the `search.json` file, or the results in form of JSON directly, to save one round trip to get the data.### searchResultTemplate (String) [optional]The template of a single rendered search result.The templating syntax is very simple: You just enclose the properties you want to replace with curly braces.E.g.The template```jsvar sjs = SimpleJekyllSearch({ searchInput: document.getElementById('search-input'), resultsContainer: document.getElementById('results-container'), json: '/search.json', searchResultTemplate: '{title}'})```will render to the following```htmlWelcome to Jekyll!```If the `search.json` contains this data```json[ { "title" : "Welcome to Jekyll!", "category" : "", "tags" : "", "url" : "/jekyll/update/2014/11/01/welcome-to-jekyll.html", "date" : "2014-11-01 21:07:22 +0100" }]```### templateMiddleware (Function) [optional]A function that will be called whenever a match in the template is found.It gets passed the current property name, property value, and the template.If the function returns a non-undefined value, it gets replaced in the template.This can be potentially useful for manipulating URLs etc.Example:```jsSimpleJekyllSearch({ ... templateMiddleware: function(prop, value, template) { if (prop === 'bar') { return value.replace(/^\//, '') } } ...})```See the [tests](https://github.com/christian-fei/Simple-Jekyll-Search/blob/master/tests/Templater.test.js) for an in-depth code example### sortMiddleware (Function) [optional]A function that will be used to sort the filtered results.It can be used for example to group the sections together.Example:```jsSimpleJekyllSearch({ ... sortMiddleware: function(a, b) { var astr = String(a.section) + "-" + String(a.caption); var bstr = String(b.section) + "-" + String(b.caption); return astr.localeCompare(bstr) } ...})```### noResultsText (String) [optional]The HTML that will be shown if the query didn't match anything.### limit (Number) [optional]You can limit the number of posts rendered on the page.### fuzzy (Boolean) [optional]Enable fuzzy search to allow less restrictive matching.### exclude (Array) [optional]Pass in a list of terms you want to exclude (terms will be matched against a regex, so URLs, words are allowed).### success (Function) [optional]A function called once the data has been loaded.### debounceTime (Number) [optional]Limit how many times the search function can be executed over the given time window. This is especially useful to improve the user experience when searching over a large dataset (either with rare terms or because the number of posts to display is large). If no `debounceTime` (milliseconds) is provided a search will be triggered on each keystroke.---## If search isn't working due to invalid JSON- There is a filter plugin in the _plugins folder which should remove most characters that cause invalid JSON. To use it, add the simple_search_filter.rb file to your _plugins folder, and use `remove_chars` as a filter.For example: in search.json, replace```json"content": "{{ page.content | strip_html | strip_newlines }}"```with```json"content": "{{ page.content | strip_html | strip_newlines | remove_chars | escape }}"```If this doesn't work when using Github pages you can try `jsonify` to make sure the content is json compatible:```js"content": {{ page.content | jsonify }}```**Note: you don't need to use quotes `"` in this since `jsonify` automatically inserts them.**## Enabling full-text searchReplace `search.json` with the following code:```yaml---layout: none---[ {% for post in site.posts %} { "title" : "{{ post.title | escape }}", "category" : "{{ post.category }}", "tags" : "{{ post.tags | join: ', ' }}", "url" : "{{ site.baseurl }}{{ post.url }}", "date" : "{{ post.date }}", "content" : "{{ post.content | strip_html | strip_newlines }}" } {% unless forloop.last %},{% endunless %} {% endfor %} , {% for page in site.pages %} { {% if page.title != nil %} "title" : "{{ page.title | escape }}", "category" : "{{ page.category }}", "tags" : "{{ page.tags | join: ', ' }}", "url" : "{{ site.baseurl }}{{ page.url }}", "date" : "{{ page.date }}", "content" : "{{ page.content | strip_html | strip_newlines }}" {% endif %} } {% unless forloop.last %},{% endunless %} {% endfor %}]```## Development- `npm install`- `npm test`#### Acceptance tests```bashcd example; jekyll serve# in another tabnpm run cypress -- run```## ContributorsThanks to all [contributors](https://github.com/christian-fei/Simple-Jekyll-Search/graphs/contributors) over the years! You are the best :)> [@daviddarnes](https://github.com/daviddarnes)[@XhmikosR](https://github.com/XhmikosR)[@PeterDaveHello](https://github.com/PeterDaveHello)[@mikeybeck](https://github.com/mikeybeck)[@egladman](https://github.com/egladman)[@midzer](https://github.com/midzer)[@eduardoboucas](https://github.com/eduardoboucas)[@kremalicious](https://github.com/kremalicious)[@tibotiber](https://github.com/tibotiber)and many others!## Stargazers over time[](https://starchart.cc/christian-fei/Simple-Jekyll-Search)"
If this doesn’t work when using Github pages you can try jsonify to make sure the content is json compatible:
"content": "# [Simple-Jekyll-Search](https://www.npmjs.com/package/simple-jekyll-search)\n\n[](https://travis-ci.org/christian-fei/Simple-Jekyll-Search)\n[](https://david-dm.org/christian-fei/Simple-Jekyll-Search)\n[](https://david-dm.org/christian-fei/Simple-Jekyll-Search?type=dev)\n\nA JavaScript library to add search functionality to any Jekyll blog.\n\n## Use case\n\nYou have a blog, built with Jekyll, and want a **lightweight search functionality** on your blog, purely client-side?\n\n*No server configurations or databases to maintain*.\n\nJust **5 minutes** to have a **fully working searchable blog**.\n\n---\n\n## Installation\n\n### npm\n\n```sh\nnpm install simple-jekyll-search\n```\n\n## Getting started\n\n### Create `search.json`\n\nPlace the following code in a file called `search.json` in the **root** of your Jekyll blog. (You can also get a copy [from here](/example/search.json))\n\nThis file will be used as a small data source to perform the searches on the client side:\n\n```yaml\n---\nlayout: none\n---\n[\n {% for post in site.posts %}\n {\n \"title\" : \"{{ post.title | escape }}\",\n \"category\" : \"{{ post.category }}\",\n \"tags\" : \"{{ post.tags | join: ', ' }}\",\n \"url\" : \"{{ site.baseurl }}{{ post.url }}\",\n \"date\" : \"{{ post.date }}\"\n } {% unless forloop.last %},{% endunless %}\n {% endfor %}\n]\n```\n\n\n## Preparing the plugin\n\n### Add DOM elements\n\nSimpleJekyllSearch needs two `DOM` elements to work:\n\n- a search input field\n- a result container to display the results\n\n#### Give me the code\n\nHere is the code you can use with the default configuration:\n\nYou need to place the following code within the layout where you want the search to appear. (See the configuration section below to customize it)\n\nFor example in **_layouts/default.html**:\n\n```html\n<!-- HTML elements for search -->\n<input type=\"text\" id=\"search-input\" placeholder=\"Search blog posts..\">\n<ul id=\"results-container\"></ul>\n\n<!-- or without installing anything -->\n<script src=\"https://unpkg.com/simple-jekyll-search@latest/dest/simple-jekyll-search.min.js\"></script>\n```\n\n\n## Usage\n\nCustomize SimpleJekyllSearch by passing in your configuration options:\n\n```js\nvar sjs = SimpleJekyllSearch({\n searchInput: document.getElementById('search-input'),\n resultsContainer: document.getElementById('results-container'),\n json: '/search.json'\n})\n```\n\n### returns { search }\n\nA new instance of SimpleJekyllSearch returns an object, with the only property `search`.\n\n`search` is a function used to simulate a user input and display the matching results. \n\nE.g.:\n\n```js\nvar sjs = SimpleJekyllSearch({ ...options })\nsjs.search('Hello')\n```\n\n💡 it can be used to filter posts by tags or categories!\n\n## Options\n\nHere is a list of the available options, usage questions, troubleshooting & guides.\n\n### searchInput (Element) [required]\n\nThe input element on which the plugin should listen for keyboard event and trigger the searching and rendering for articles.\n\n\n### resultsContainer (Element) [required]\n\nThe container element in which the search results should be rendered in. Typically a `<ul>`.\n\n\n### json (String|JSON) [required]\n\nYou can either pass in an URL to the `search.json` file, or the results in form of JSON directly, to save one round trip to get the data.\n\n\n### searchResultTemplate (String) [optional]\n\nThe template of a single rendered search result.\n\nThe templating syntax is very simple: You just enclose the properties you want to replace with curly braces.\n\nE.g.\n\nThe template\n\n```js\nvar sjs = SimpleJekyllSearch({\n searchInput: document.getElementById('search-input'),\n resultsContainer: document.getElementById('results-container'),\n json: '/search.json',\n searchResultTemplate: '<li><a href=\"{{ site.url }}{url}\">{title}</a></li>'\n})\n```\n\nwill render to the following\n\n```html\n<li><a href=\"/jekyll/update/2014/11/01/welcome-to-jekyll.html\">Welcome to Jekyll!</a></li>\n```\n\nIf the `search.json` contains this data\n\n```json\n[\n {\n \"title\" : \"Welcome to Jekyll!\",\n \"category\" : \"\",\n \"tags\" : \"\",\n \"url\" : \"/jekyll/update/2014/11/01/welcome-to-jekyll.html\",\n \"date\" : \"2014-11-01 21:07:22 +0100\"\n }\n]\n```\n\n\n### templateMiddleware (Function) [optional]\n\nA function that will be called whenever a match in the template is found.\n\nIt gets passed the current property name, property value, and the template.\n\nIf the function returns a non-undefined value, it gets replaced in the template.\n\nThis can be potentially useful for manipulating URLs etc.\n\nExample:\n\n```js\nSimpleJekyllSearch({\n ...\n templateMiddleware: function(prop, value, template) {\n if (prop === 'bar') {\n return value.replace(/^\\//, '')\n }\n }\n ...\n})\n```\n\nSee the [tests](https://github.com/christian-fei/Simple-Jekyll-Search/blob/master/tests/Templater.test.js) for an in-depth code example\n\n### sortMiddleware (Function) [optional]\n\nA function that will be used to sort the filtered results.\n\nIt can be used for example to group the sections together.\n\nExample:\n\n```js\nSimpleJekyllSearch({\n ...\n sortMiddleware: function(a, b) {\n var astr = String(a.section) + \"-\" + String(a.caption);\n var bstr = String(b.section) + \"-\" + String(b.caption);\n return astr.localeCompare(bstr)\n }\n ...\n})\n```\n\n### noResultsText (String) [optional]\n\nThe HTML that will be shown if the query didn't match anything.\n\n\n### limit (Number) [optional]\n\nYou can limit the number of posts rendered on the page.\n\n\n### fuzzy (Boolean) [optional]\n\nEnable fuzzy search to allow less restrictive matching.\n\n### exclude (Array) [optional]\n\nPass in a list of terms you want to exclude (terms will be matched against a regex, so URLs, words are allowed).\n\n### success (Function) [optional]\n\nA function called once the data has been loaded.\n\n### debounceTime (Number) [optional]\n\nLimit how many times the search function can be executed over the given time window. This is especially useful to improve the user experience when searching over a large dataset (either with rare terms or because the number of posts to display is large). If no `debounceTime` (milliseconds) is provided a search will be triggered on each keystroke.\n\n---\n\n## If search isn't working due to invalid JSON\n\n- There is a filter plugin in the _plugins folder which should remove most characters that cause invalid JSON. To use it, add the simple_search_filter.rb file to your _plugins folder, and use `remove_chars` as a filter.\n\nFor example: in search.json, replace\n\n```json\n\"content\": \"{{ page.content | strip_html | strip_newlines }}\"\n```\n\nwith\n\n```json\n\"content\": \"{{ page.content | strip_html | strip_newlines | remove_chars | escape }}\"\n```\n\nIf this doesn't work when using Github pages you can try `jsonify` to make sure the content is json compatible:\n\n```js\n\"content\": {{ page.content | jsonify }}\n```\n\n**Note: you don't need to use quotes `\"` in this since `jsonify` automatically inserts them.**\n\n\n## Enabling full-text search\n\nReplace `search.json` with the following code:\n\n```yaml\n---\nlayout: none\n---\n[\n {% for post in site.posts %}\n {\n \"title\" : \"{{ post.title | escape }}\",\n \"category\" : \"{{ post.category }}\",\n \"tags\" : \"{{ post.tags | join: ', ' }}\",\n \"url\" : \"{{ site.baseurl }}{{ post.url }}\",\n \"date\" : \"{{ post.date }}\",\n \"content\" : \"{{ post.content | strip_html | strip_newlines }}\"\n } {% unless forloop.last %},{% endunless %}\n {% endfor %}\n ,\n {% for page in site.pages %}\n {\n {% if page.title != nil %}\n \"title\" : \"{{ page.title | escape }}\",\n \"category\" : \"{{ page.category }}\",\n \"tags\" : \"{{ page.tags | join: ', ' }}\",\n \"url\" : \"{{ site.baseurl }}{{ page.url }}\",\n \"date\" : \"{{ page.date }}\",\n \"content\" : \"{{ page.content | strip_html | strip_newlines }}\"\n {% endif %}\n } {% unless forloop.last %},{% endunless %}\n {% endfor %}\n]\n```\n\n\n\n## Development\n\n- `npm install`\n- `npm test`\n\n#### Acceptance tests\n\n```bash\ncd example; jekyll serve\n\n# in another tab\n\nnpm run cypress -- run\n```\n\n## Contributors\n\nThanks to all [contributors](https://github.com/christian-fei/Simple-Jekyll-Search/graphs/contributors) over the years! You are the best :)\n\n> [@daviddarnes](https://github.com/daviddarnes)\n[@XhmikosR](https://github.com/XhmikosR)\n[@PeterDaveHello](https://github.com/PeterDaveHello)\n[@mikeybeck](https://github.com/mikeybeck)\n[@egladman](https://github.com/egladman)\n[@midzer](https://github.com/midzer)\n[@eduardoboucas](https://github.com/eduardoboucas)\n[@kremalicious](https://github.com/kremalicious)\n[@tibotiber](https://github.com/tibotiber)\nand many others!\n\n## Stargazers over time\n\n[](https://starchart.cc/christian-fei/Simple-Jekyll-Search)\n"
Note: you don’t need to use quotes " in this since jsonify automatically inserts them.
Replace search.json with the following code:
---
layout: none
---
[
{
"title" : "Make your ROS & Eigen C++ code compile faster with precompiled headers",
"category" : "",
"tags" : "",
"url" : "/2023/11/26/ros-eigen-compile-times/",
"date" : "2023-11-26 00:00:00 +0000",
"content" : "We all know the feeling: write your code, save it, catkin_make (or catkin build, or colcon build), and… wait. Even if you just change a number, you might be waiting upwards of 8 seconds. While small in comparison to the time it takes to actually write the code, this can absolutely be distracting when you want to test the change right away. With these changes, I can recompile a ROS node that uses Eigen in less than 1.5 seconds. While I’m using ROS Noetic on Ubuntu 20.04, this is applicable to any codebase using C++.First: a note about compilersA simple trend about compilers: the newer it is, the faster it is. So when Ubuntu 20.04 comes with GCC 9 from 2019, it’s going to be slower than Clang 12, an alternative C++ compiler, released in 2021. While there are newer versions of both GCC and Clang, Clang 12 is the newest compiler available in Ubuntu 20.04 with just a simple sudo apt install. As such, I recommend switching—simply sudo apt install clang-12. To use it, anytime you’d write gcc use clang-12 instead, and g++ can be replaced with clang++-12. There’s no changes needed to your code, as GCC and Clang both implement the C++ standard. To start using it in ROS, you’ll need to tell CMake to use Clang with a few environment variables. You’ll want to set CC to clang-12, CXX to clang++-12, and LD to clang++-12. To do this temporarily, run in a terminal:export CC=clang-12export CXX=clang++-12export LD=clang++-12(note: this will only have effect in that specific terminal, and will go away when you close it). Then, and this is an important step, delete previously compiled files by deleting the build folder in your workspace—there can be some issues when mixing compilers, and CMake won’t switch to the new compiler until you rebuild anyways. Now, just build! It should hopefully be faster. Make sure you set it permanently so it doesn’t switch back in the future.If you’re not on Ubuntu 20.04, you might want to experiment with different compilers! If you’re using GCC, try Clang—it might just happen to be faster.What’s the problem?In C and C++, writing #include will look up that file, and then copy and paste its contents into the original file. As such, files that appear small have to look through an incredibly high amount of code. You can find how much code is included like this by running your compiler with with the -E flag, like g++ -E source.cpp or g++ -E source.cpp. As an example, one ROS node with only 250 lines of code expanded into over 200,000 lines with #includes processed!How precompiled headers helpInstead of re-reading, lexing, and parsing all these lines of headers that never change, you can enable precompiled headers in your compiler. By writing its internal data structures to disk, these steps happen only once! To use precompiled headers, you need to specify which specific headers to preprocess. CMake has an incredibly easy way to do this—the build tool already used by ROS! In your CMakeLists.txt, look for a line that looks like this:add_executable(some_target_name src/some_source_file.cpp)# --- OR ---add_library(some_target_name src/some_source_file.cpp)# --- OR ---cs_add_executable(some_target_name src/some_source_file.cpp)Anywhere after it, just add these lines to precompile headers, just making sure to update some_target_name with the name within add_executable!target_precompile_headers(some_target_name PRIVATE <ros/ros.h> <eigen3/Eigen/Core>)Note that this may use significant disk space. Expect 300MB with GCC 9 (the default on Ubuntu 20.04), and around 60MB with Clang 12.Building multiple nodes in a package?Save some time by sharing precompiled headers! Instead of writingadd_executable(node_1 src/node_1.cpp)add_executable(node_2 src/node_2.cpp)# BAD WAYtarget_precompile_headers(node_1 PRIVATE <ros/ros.h> <eigen3/Eigen/Core>)target_precompile_headers(node_2 PRIVATE <ros/ros.h> <eigen3/Eigen/Core>)you can reuse the precompiled header:add_executable(node_1 src/node_1.cpp)add_executable(node_2 src/node_2.cpp)# GOOD WAYtarget_precompile_headers(node_1 PRIVATE <ros/ros.h> <eigen3/Eigen/Core>)target_precompile_headers(node_2 REUSE_FROM node_1)This will reduce the work needed to precompile headers (since it only needs to happen once) in addition to disk space. Unfortunately, reusing headers may not work if you want to include more headers in only one target—some compilers support this, but CMake doesn’t. This will also not work if one target is an executable and one is a library.But how do I know what headers to precompile?Thankfully, the Clang compiler has a built-in tool to show you what headers take the most time to compile! After making sure you’ve switched to Clang, add this to the CMakeLists.txt for the package you’d like to inspect:add_compile_options(-ftime-trace)If you’re using the compiler directly, you can use clang++ -ftime-trace insteadWhen you recompile, you’ll notice a bunch of json files in your build folder—find them with find build -name "*.cpp.json" when you’re currently in your ROS workspace directory. Head on over to ui.perfetto.dev and Open Trace File on the left panel, selecting that JSON file. You’ll be presented with a graph showing all the time spent compiling this file:Take a look at those turquise blocks labeled “Source”—that’s all time spent reading source files! We can see that a whole 3 seconds is spent on header files. Click on them to see what file is taking a while:That’s 1.4 seconds on ros/ros.h! There’s no need for that. So, after adding that, Eigen, and a few other headers used in this file as a precompiled header, we can check out the graph again:Look how much less time is spent on Sources! Note that the graph always takes up the full screen width, so the sections that now appear bigger just take up the same amount of time as before."
} ,
{
"title" : "Steering Towards Success: AI-Navigation/Controls Subteam's Progress on Autonomous Navigation",
"category" : "",
"tags" : "",
"url" : "/2023/11/12/nav-controls-update/",
"date" : "2023-11-12 00:00:00 +0000",
"content" : "My name is Georgia Zender and I am a fourth year computer science student at the University of Michigan. I am one of the co-leads of the AI-Navigation/Controls subteam, which is responsible for programming autonomous navigation and interfacing with the hardware. So far this semester, the AI-Navigation/Controls subteam has been working on optimizing last year’s code in order to perform better in our simulated environment.On the navigation side, we recently made changes to our costmap so our path planner can map a more ideal path. We first altered how we were weighing the area around obstacles so that the planner would not map a path too close to a buoy. At last year’s competition, our vessel would get too close to buoys at times. To prevent this, we increased the cost of the area around obstacles in the costmap to decrease the likelihood of the planner creating a path that travels near obstacles. Second, we increased the cost of the area behind the vessel so that we don’t end up planning a path where the vessel has to make large turns. Drastically changing directions in our path is unideal because our vessel is not able to make sharp turns efficiently.Our subteam has now transitioned to working on making alterations to our controls code. We have made modifications to the intended velocity of the vessel in order to account for the vessel’s dynamics. Over the past two weeks, we have written code to calculate the change of angle of the vessel’s path and if this angle is within some threshold of 0, the velocity of the vessel will be greater. Otherwise, the vessel will go slower to better navigate turns in the path. We also implemented code so the vessel will slow down as it reaches its goal to decrease the likelihood of overshooting.Finally, I got the opportunity to learn from Ben Shattinger, our AI lead, and write code to interface with the boat’s microcontroller. Whenever we receive a ROS message from the thrusters, we send a byte packet to the microcontroller to set PWM outputs. In the process of writing this code, I learned how to use packed structs in C++ in order to write and send byte packets according to an API provided by the Electrical team.Looking forward, our subteam will be focusing more on controls code optimizations. We want to continue considering the change of angle of the path when determining the vessel’s velocity. We also want to implement code to reverse into the dock for the docking challenge, instead of entering the dock head-on. And finally, in the coming weeks, we expect to test our code on the vessel in the water, instead of the simulated environment. Overall, our subteam has made great progress this semester and we are on-track to achieve all of our goals before competition in February."
} ,
{
"title" : "ECE Family Fun Night",
"category" : "",
"tags" : "",
"url" : "/2023/10/01/ece-family-fun/",
"date" : "2023-10-01 00:00:00 +0000",
"content" : "This past Friday the team had a blast at the ECE Family Fun Night! It was a great chance to share UM::Autonomy’s work with professors and alumni and connect with the larger ECE community."
} ,
{
"title" : "Sponsor Highlight: Boeing!",
"category" : "",
"tags" : "",
"url" : "/2023/09/23/boeing-meeting/",
"date" : "2023-09-23 00:00:00 +0000",
"content" : "Last Wednesday, our team had the great pleasure of meeting with Boeing in the Wilson Center to talk about our team, discuss our performance at the 2023 RoboBoat Competition, and review our boat, the Phoenix!Boeing has been a Gold sponsor for UM::Autonomy since 2017, providing us with not only the financial support to be able to go to competitions every year but also with yearly design reviews and meet & greets with the team. We’re incredibly grateful for their continued support, and the opportunity to be able to share our work with them. We look forward to working with them further as we prepare to set sail at the 2024 RoboBoat Competition in February!"
} ,
{
"title" : "Ford Motor Company's Immersion Day",
"category" : "",
"tags" : "",
"url" : "/2023/08/29/ford-cars/",
"date" : "2023-08-29 00:00:00 +0000",
"content" : "Last week, our very own Ben Bruick and Asheya Naik had the fantastic opportunity to attend Ford Motor Company’s Immersion Day at the Ford Robotics Building on North Campus!This was a great chance to network and connect with industry leaders, partake in resume coaching and diverse team-building workshops, and learn more about the work done at Ford. The team especially enjoyed their product reviews of the F150 Lightning and Mustang and the vehicle walk arounds.Ford is a Platinum sponsor of the team and has historically provided the team with lots of technical support in designing, fabricating, and testing carbon fiber hull forms for our boats every year. In the past, they have provided foam molds for our carbon fiber vacuum infusion process (as shown below).Thank you to Ford for inviting us to this event, and we look forward to another great competition season made possible through their support!"
} ,
{
"title" : "From High School to Leading UM::Autonomy at University of Michigan",
"category" : "",
"tags" : "",
"url" : "/2023/08/05/asheya/",
"date" : "2023-08-05 00:00:00 +0000",
"content" : "As we prepare for a new competition season, I’d like to highlight my team and our work at UM::Autonomy, University of Michigan’s premier autonomous surface vessel team.I was first introduced to the team as a junior at Hagerty High School in Oviedo, Florida, where my school competed against UMich in RoboNation’s annual RoboBoat Competition, formerly in South Daytona, Florida. It was here that my love for robotics and applied engineering was brought to a burgeoning frontier, ripe with learning opportunities and growth - a place to try, test, and fail while encountering real-world challenges on a level incomparable to the student teams and competitions that I’d been exposed to in the past. In this environment of dynamic discovery and engineering is where I encountered this team, a group of students so compassionate and willing to help others, regardless of the atmosphere of competition.I’m glad to say that six years later, I’m leading this same team as the President of UM::Autonomy - though I still have so much more to learn, and so many perilous challenges ahead that I feel anxious to face. What I love is that through it all, the environment of the team and the atmosphere of friendliness and welcoming has never changed, and I want it to remain that way - especially since that is the heart of this team, and speaks volumes about engineering overall.I’ve come to learn that often, the ability to work with people, manage towering projects, and communicate ideas effectively is far more overlooked and important than technical prowess alone. Despite being an incredibly technical field, I have realized that engineering is mainly about the people around you, how they communicate, and how they learn and grow together more than anything. While I have made that realization, I have far from mastered that skill of managing projects and communicating with people effectively, and I’m excited to grow that ability even further.So as our team looks forward to the RoboBoat Competition in Sarasota, FL in 2024, and I look forward to my last year in school, I’m excited to take on another year of hard but rewarding challenges for my team and I - especially knowing that I have a strong, unwavering, and talented group around me."
}
,
{
"title" : "RoboBoat 2023",
"category" : "",
"tags" : "",
"url" : "/roboboat/2023/",
"date" : "",
"content" : " RoboBoat 2023: Ocean Exploration Nathan Benderson Park Sarasota, Florida RoboBoat is an international competition where students design, build, and compete with self-driving robotic boats, in a series of tests aimed at challenging teams through a variety autonomous (self-driving) tasks. In 2023, we were joined by 22 other teams from 4 continents. UM::Autonomy placed 6th overall, and 3rd among American Universities. Jump to Competition Results ↓ Competition Strategy For this competition season, we tested with the 2022 hull (itself a refurbished 2017 hull) for most of the year, since it was already built. A new hull made of carbon fiber was being built concurrently by the mechanical team. At the last minute, days before departure for the competition, it was ready. The team spent the weekend preparing the new boat for the competition, and thanks to simulator testing, this process went smoothly and few code changes were needed. Static Judging Criteria Besides a team's performance autonomously, we are judged on aspects of both our team and the boat. Design Documentation The team must prepare a website, a technical design report, and a video for judges to score. These are evaluated based on how well they introduce the team and its structure as well as design considerations of the boat. Presentation The team must present to the judges live their decisions leading to the design of the boat. Other Judging Criteria Before the boat can participate in an autonomous challenge, several prerequisite activites must be completed. Static Safety Inspection As the boats are very high powered, a runaway boat could damage itself and hurt others. Therefore, competition staff ensure that the boat follows several safety rules: The boat stops when a physical red button is pressed on the boat The boat stops when a remote switch is flipped, or the remote switch loses power or connection The boat does not have any sharp edges so that people in the water can touch the boat safely UM::Autonomy is proud to have been one of the first three teams to pass the safety inspection at the 2023 competition. Boat Thrust-to-Weight Ratio The competition rewards fast and light craft. Therefore, a sliding scale is used where points are lost faster the heavier it gets. The boat is weighed and its thrust is measured every day it is entered in the water. In 2023, UM::Autonomy's boat weighed 55 pounds, the lightest weight class. High Priority Challenges Navigate the Panama Canal Description This challenge is mandatory before attempting other tasks. The ASV needs to pass through two sets of gates (a pair of red and green buoys) and starts autonomous navigation at a minimum of 6 ft before the set of gates. Analysis As it is mandatory, this challenge is of high importance. In 2019, the boat could only successfully pass the navigation channel once out of four qualification runs as a result of a major electrical failure onboard. Goal 14 out of 15 successful runs Post-competition Remarks As one of the simplest tasks, this was completed with high success. However, additional tuning was needed upon arrival to the competition site as the boat's computer vision system would occasionally fail to detect a buoy. Magellan's Route / Count the Manatees & Jellyfish Description The ASV passes through between multiple sets of gates (pairs of red and green buoys) The ASV also avoids intermittent yellow buoys (jelly fish) and black buoys (manatees) of various sizes and counts them. Analysis The challenge requires minimal external hardware or software development and mainly just involves careful navigational operability and fine motor control. This challenge could be tested and fine-tuned early in the development process. Goal 9 out of 10 successful runs Post-competition Remarks This challenge revealed some disadvantages of our indoors testing area. Specifically, our obstacle-avoidance system decided that the best way to avoid hitting buoys was to go outside of the red-green channel! While technically following our instructions, it didn't score many points. Although we didn't have time to fix that during the competition, the boat did go through several red-green pairs on some runs scoring us important points. Northern Passage Challenge Description The ASV enters the gate buoys, maneuvers around the mark buoy, and exits thought the same gate buoys, as quickly as possible. The timer starts when the bow (front) crosses the gate buoys and stops when the stern (back) crosses the gate buoys. The team colloquially refers to this as the "Speed Challenge". Analysis Based on the 2019 score-sheet, a time between 25-45s is needed to remain competitive in the Northern Passage challenge, with the fastest 2019 run coming in at 27 seconds. Goal 9 out of 10 successful runs. We hope for a baseline of 35 seconds, and a goal of 26 seconds. Post-competition Remarks Another disadvantage of indoors testing was highlighted here: buoy interference. Specifically, our computer vision system had a hard time distinguising the blue buoy (marking where to turn around) from the black buoys (marking an obstacle to avoid in the previous challenge, Magellan's Route). When we tested indoors, we only tested one or two challenges at a time, and with different lighting. Medium Priority Challenges Beaching & Inspecting Turtle Nests Description Before the time slot starts, teams are assigned a color and must dock at the bay with the matching color. Once the ASV detects and enters the docking bay, it must report the number of "eggs" (number of circles) in the nest. The team colloquially refers to this challenge as "Docking." Analysis This challenge is a bit more involved in terms of computer and color/shape recognition but does not require external hardware development. Goal 9 out of 10 successful runs Post-competition Remarks This task turned out easier than expected. Matching on color rather than number of dots on the board turned out rather easy. Scoring involved simply hitting the docks. Feed the Fish Description The ASV detects the "feeding table" (purple frame), then lines up and shoot three "pellets" (racquetballs) through the frame into any of the three holes. Points are awarded if the ball is fired into any of the holes but fewer points are awarded for just landing the ball on the deck. The team colloquially refers to this challenge as "Skeeball," the classic arcade game. Analysis As both the Ponce de Leon (see next) and Feeding the Fish are new challenges, UM::Autonomy chose to only focus on completing the Ponce de Leon challenge this year, though work was done throughout the year to complete the Skeeball task in the future. Goal N/A Post-competition Remarks See next task Ponce de Leon / Fountain of Youth Description The ASV detects the target face (blue/white striped) and shoots enough water through the target to raise the ball above the green line in the pipe. The ASV may pump the water from the environment or store it on board. The team colloquially refers to this challenge as "Water Blast." Analysis This is the first season with this challenge and hardware and software development of external mechanisms pushed back actual testing. Therefore, we knew that immediate mastery of this task would be difficult and time consuming, and should only be attempted after other challenges. Goal 3 out of 5 successful runs Post-competition Remarks Although we had planned to focus on shooting water rather than balls, it turned out that water leakage was a big worry: if the pump failed and dumped water in the boat's electronics, we wouldn't be able to compete anymore. As such, we re-focued our effort on Feed the Fish. We finally had a working ball-shooting system while at the competition site, but lack of testing time meant that it couldn't be integrated in time. Both tasks only had three teams that could shoot anything. No teams landed balls in the buckets. Low Priority Challenges Ocean Cleanup Description The ASV detects an underwater pinger which designates the area to collect "debris" (raquetballs) from. The ASV may then use the collected balls as extra balls in the Feed the Fish challenge. Analysis As this task is a new challenge, UM::Autonomy chose to focus on completing the Ponce de Leon challenge and Feeding the Fish challenge for this year, though work was done throughout the year to complete the Ocean Cleanup task in the future. Goal N/A Post-competition Remarks This was one of the hardest challenges this year. Only a couple teams even had the hardware to participate, and none scored points. We will need to evaluate again whether this task is worth pursuing next year. UM::Autonomy extends a special thanks to Cole Biesemeyer from the Open Source Robotics Foundation for the 3D model of Nathan Benderson Park and Gdańsk University of Technology's SimLE: SeaSentinel team for creating 3D models of various competition elements. Competition Results UM::Autonomy placed 6th overall, and 3rd among American Universities. Static Judging UM::Autonomy placed 1st in the technical report, 2nd in the video, and 1st in the presentation for an overall 3rd place in design documentation. Autonomous Challenge UM::Autonomy placed 9th. 13 teams scored no points in the autonomous challenge. The Team The team feels very confident for the 2024 season. Many new members attended competition for the first time, creating an exciting environment where many members are familiar with competition practices allowing us to achieve further success in future years. "
} ,
{
"title" : "The Team",
"category" : "",
"tags" : "",
"url" : "/team/2023/",
"date" : "",
"content" : " The Team Get to know our talented members from 2022-23! Leadership Saheth Edupuganti President LinkedIn Graduating Winter 2023 Asheya Ashok Naik Systems Engineering Lead President in 2023-2024 LinkedIn Graduating Winter 2024 Tom Gao Vice President and ACT Lead LinkedIn Graduating Fall 2023 Ben Bruick Vice President in 2023-2024 LinkedIn Graduating Winter 2026 Elaina Mann Treasurer in 2023-2024 Graduating Winter 2026 Isabella Minkin Business Lead in 2023-2024 LinkedIn Graduating Winter 2023 Raghav Varshney Electrical Co-Lead LinkedIn Graduating Winter 2024 Aayush Shah Electrical Lead in 2023-2024 Graduating Winter 2025 Aayushi Nakum Electrical Co-Lead Email Graduating Winter 2023 Frances Truong Mechanical Co-Lead Email Graduating Winter 2024 Lucas Mitsch Mechanical Co-Lead LinkedIn Graduating Winter 2024 Gordon Fream Chief Naval Architect in 2023-2024 LinkedIn Graduating Winter 2025 Chad Rakoczy AI Co-Lead / CV Lead LinkedIn Graduating Winter 2023 Ben Schattinger AI Co-Lead AI Lead in 2023-2024 LinkedIn Graduating Winter 2026 Murphy Justian Task Planning Co-Lead Task Planning Co-Lead in 2023-2024 LinkedIn Graduating Winter 2024 Georgia Zender Navigation Co-Lead Navigation & Controls Co-Lead in 2023-2024 Email Graduating Winter 2025 Zain Eazad Task Planning Co-Lead LinkedIn Graduating Winter 2023 Christopher Briggs Navigation & Controls Co-Lead in 2023-2024 LinkedIn Graduating Fall 2024 Arya Patil Navigation Co-Lead LinkedIn Graduating Winter 2024 Vanessa Lulla Controls Lead Email Graduating Winter 2025 Dimitrios Dikos Business Lead LinkedIn Graduating Winter 2023 Kyle Gross Task Planning Co-Lead in 2023-2024 Graduating Winter 2025 Artificial Intelligence Chad Rakoczy AI Co-Lead / CV Lead LinkedIn Graduating Winter 2023 Ben Schattinger Controls & AI Co-Lead LinkedIn Graduating Winter 2026 Murphy Justian Task Planning Co-Lead LinkedIn Graduating Winter 2024 Georgia Zender Navigation Co-Lead Email Graduating Winter 2025 Christopher Briggs Navigation Member LinkedIn Graduating Fall 2024 Arya Patil Navigation Co-Lead LinkedIn Graduating Winter 2024 Vanessa Lulla Controls Lead Email Graduating Winter 2025 Kyle Gross Task Planning Member Graduating Winter 2025 Zain Eazad Task Planning Co-Lead LinkedIn Graduating Winter 2024 Hevwar Shahab CV Member LinkedIn Graduating Winter 2024 Amirali Danai CV Member LinkedIn Graduating Fall 2026 Sonika Potnis Task Planning Member Email Graduating Fall 2025 Sushrita Rakshit Task Planning Member LinkedIn Graduating Winter 2025 Lada Protcheva Task Planning Member LinkedIn Graduating Winter 2024 Nathan Buckwalter Task Planning Member Email Graduating Winter 2023 Frank Sun Navigation Member Email Graduating Winter 2024 Advanced Capabilities Tom Gao Advanced Capabilities Software Lead LinkedIn Graduating Fall 2023 Ben Bruick Advanced Capabilities Hardware Lead LinkedIn Graduating Winter 2026 Broderick Riopelle Member LinkedIn Graduating Winter 2024 Ryan Chua Member LinkedIn Graduating Winter 2024 Joshua Beatty Member LinkedIn Graduating Winter 2024 Business Elaina Mann Member Graduating Winter 2026 Isabella Minkin Member LinkedIn Graduating Winter 2023 Zain Eazad Web Dev Team Lead LinkedIn Graduating Winter 2023 Dimitrios Dikos Business Lead LinkedIn Graduating Winter 2023 Chelsea Sun Member LinkedIn Graduating Winter 2025 Electrical Raghav Varshney Electrical Co-Lead LinkedIn Graduating Winter 2024 Aayush Shah Member Graduating Winter 2025 Aayushi Nakum Electrical Co-Lead Email Graduating Winter 2023 Zamir North Member Email Graduating Winter 2025 Mechanical Frances Truong Mechanical Co-Lead Email Graduating Winter 2024 Lucas Mitsch Mechanical Co-Lead LinkedIn Graduating Winter 2024 Gordon Fream Member LinkedIn Graduating Winter 2025 Chelsea Sun Member LinkedIn Graduating Winter 2025 Anthony Tan Member Graduating Winter 2025 Cameron Muzall Member Email Graduating Winter 2024 Jackson Brown Member LinkedIn Graduating Winter 2023 Kye Dembinski Member Email Graduating Winter 2023 Systems Engineering Asheya Ashok Naik Systems Engineering lead LinkedIn Graduating Winter 2024 Axel Avram Member LinkedIn Graduating Winter 2024 "
} ,
{
"title" : "The Phoenix: 2023 Boat",
"category" : "",
"tags" : "",
"url" : "/history/2023/",
"date" : "",
"content" : " 2023: The Phoenix Competition strategy and team progress Learn more ▼ The Phoenix Detailed Technical Report ↓ Rising from the ashes of defeat and disorder following the pandemic, the phoenix soars to competition this year with renewed pride, a greater sense of togetherness, and a singular focus: to compete hard while having fun. After a hard-fought competition the previous year, the team chose to focus on four principles for this year: decoupling, reliability, maintainability, and performance. Not only was the AI pipeline decoupled and tested separately, but nearly all major components of the boat could be separated, making it extremely modular and maintainable. It was the first year that the team tested in the Marine Hydrodynamics Laboratory, and did so for over 100 hours. In addition, it was the first year that the boat was made out of carbon fiber from scratch - from the painstaking mold making, preparation, and vacuum resin infusion - in order to produce a boat that weighed less than 60 pounds while still delivering impeccable results. Mechanical Details The Phoenix weighs 55 pounds, has a length of 56”, a beam (width) of 30”, and has an overall height of 26”. It can generate thrusts of more than 25 pounds. A hull and superstructure made of carbon fiber allows for weight reduction which has the benefits of an increased thrust to weight ratio and ease of transportability. Electrical Details An AMD Ryzen 5 5600X on a ASUS ROG Strix X570-I motherboard with 32 GB RAM provides the main logic of the boat. A Ubiquiti Rocket point-to-point transceiver allows for a reliable, fast data connection to land control. An Arduino Mega is used for computer control of the two Blue Robotics T500 thrusters. A Velodyne Puck LiDAR paired with a Logitech webcam is used for the boat's sensing. Software Details A Docker-based software environment configured with ROS 1 Noetic ensures that the exact same software is present when testing on a computer and on the boat—allowing team members to use their choice of Linux, Windows, or macOS on their personal computers without differences. The team uses Git as our version control system. Most of the team's code is written in C++, with several Python scripts for small tasks. Computer Vision is done with the YOLOv4 object detection system, and an upgrade to YOLOv8 is planned for the 2023-2024 year. The team plans to migrate to ROS 2 as well this year. "
} ,
{
"title" : "RoboBoat 2024",
"category" : "",
"tags" : "",
"url" : "/roboboat/2024/",
"date" : "",
"content" : " Photo from RoboBoat 2023: Ocean Exploration RoboBoat 2024: Ducks Overboard Nathan Benderson Park Sarasota, Florida RoboNation RoboBoat is an international competition where students design, build, and compete with self-driving robotic boats, in a series of tests aimed at challenging teams through a variety autonomous (self-driving) tasks. We will be joined by 18 teams from 3 continents. Competition Strategy Due to the short build cycle this year—just 5 months from competition details to competition—we decided to reuse our 2023 hull, The Phoenix, albeit with major structural improvements. We focused on simplicity in design and workflow in order to further ensure a reliable, maintainable, and modular system. Understanding the tighter time constraints of this season, we put a greater emphasis was placed on design validation and in-water testing, which was facilitated by maintaining an operable vessel and reducing design complexity from last year. We moved to testing strategy that relocated the team permanently into its testing environment, made testing a weekly process from the start of the season, and allowed for multiple modes of testing to guarantee success. Scroll down for more details ↓ Static Judging Criteria Besides a team's performance autonomously, we are judged on aspects of both our team and the boat. Design Documentation The team must prepare a website, a technical design report, and a video for judges to score. These are evaluated based on how well they introduce the team and its structure as well as design considerations of the boat. Presentation The team must present to the judges live their decisions leading to the design of the boat. Other Judging Criteria Before the boat can participate in an autonomous challenge, several prerequisite activites must be completed. Static Safety Inspection As the boats are very high powered, a runaway boat could damage itself and hurt others. Therefore, competition staff ensure that the boat follows several safety rules: The boat stops when a physical red button is pressed on the boat The boat stops when a remote switch is flipped, or the remote switch loses power or connection The boat does not have any sharp edges so that people in the water can touch the boat safely UM::Autonomy is proud to have been one of the first three teams to pass the safety inspection at the 2023 competition. We have continued this spirit throughout the 2023-2024 year: safety is considered highly in all situations, from battery and testing site training to ensuring the vessel is safe at all times. Boat Thrust-to-Weight Ratio The competition rewards fast and light craft. Therefore, a sliding scale is used where points are lost faster the heavier it gets. The boat is weighed and its thrust is measured every day it is entered in the water. The Phoenix weighs 40 pounds, the lightest weight class. Autonomy Challenge The main part of the challenge is the tasks that the vessel must perform autonomously. However, we must first qualify before we perform autonomous operation on the full course. Qualification Teams will be given opportunities to practice, display their capabilities, and earn their spot in the final round. They will have access to three duplicate courses, each containing the eight tasks. Throughout this time, teams can choose to test strategies, gather data, or qualify for tasks. A certain level of completion—"minimum performance criteria"—is required to complete a task. Once a team has qualified for enough tasks, they enter the Semi-Final rounds. Semi-Finals Teams must score some number of points in Semi-Finals rounds to progress to Finals. Finals Teams who have successfully qualified will have access to the finals courses. Importantly, each vessel must operate autonomously for the entire duration of the run; remote-controlled survey runs are not allowed. This means teams cannot manually control their vessels to gather data or assess the field before or during their time slot, emphasizing the critical importance of pre-run preparation and programming accuracy. They will be required to navigate through initial gates, attempt a series of tasks in their chosen sequence, and finally return to the home base to conclude the run. All teams making it to the final round will secure a higher final ranking over those who do not reach this stage of the competition. High Priority Challenges Navigation Channel Description This challenge is mandatory before attempting other tasks. The ASV needs to pass through two sets of gates (a pair of red and green buoys) and starts autonomous navigation at a minimum of 6 ft before the set of gates. Analysis As it is mandatory, this challenge is of high importance. In 2019, the boat could only successfully pass the navigation channel once out of four qualification runs as a result of a major electrical failure onboard. In 2023, it was completed with high success due to our extensive testing in water and in simulation. However, additional tuning was needed upon arrival to the competition site as the boat's computer vision system would occasionally fail to detect a buoy. Goal 14 out of 15 successful runs Follow the Path Description The ASV passes through between multiple sets of gates (pairs of red and green buoys) The ASV also avoids intermittent yellow and black buoys of various sizes and counts them. Analysis The challenge requires minimal external hardware or software development and mainly just involves careful navigational operability and fine motor control. This challenge should be tested and fine-tuned early in the development process. In 2023, we found that this challenge revealed some disadvantages of our indoors testing area. Specifically, our obstacle-avoidance system decided that the best way to avoid hitting buoys was to go outside of the red-green channel! While technically following our instructions, it didn't score many points. We have since revised our navigation algorithm to take this restriction into account. Goal 9 out of 10 successful runs Speed Challenge Description The ASV enters the gate buoys, maneuvers around the yellow marker buoy, and exits thought the same gate buoys, as quickly as possible. The timer starts when the bow (front) crosses the gate buoys and stops when the stern (back) crosses the gate buoys. Analysis Based on the 2023 score sheet, teams that completed this challenge did so in 50, 75, and 100 seconds. This is significantly higher than the 2019 score sheet which revealed that a time between 25-45s is needed to remain competitive, with the fastest 2019 run coming in at 27 seconds. Nevertheless, we plan to beat the 2023 times and remain competitive with those that competed in 2019. This year introduces an obstacle buoy. We do not expect a challenge with this because our navigation algorithm is already very familiar with avoiding buoys, and we do not expect to even need any code changes. Goal 9 out of 10 successful runs. We hope for a baseline of 45 seconds, and a goal of 35 seconds. Docking Description Before the time slot starts, teams are assigned a color and must dock at the bay with the matching color. To dock, contact must simply be made with the dock. Analysis This challenge is a bit more involved in terms of computer and color/shape recognition but does not require external hardware development. We plan to train our computer vision model on color only, as it was all but guaranteed that each poster will be a unique color. We had success with this strategy in 2023, and plan to do it again. Goal 9 out of 10 successful runs Medium Priority Challenges Duck Wash Description The ASV detects the target duck poster and shoots water on it for 5 seconds continuously. The ASV may pump the water from the environment or store it on board. Analysis This task follows a similar task from last year, also involving water blasts. However, the task is simplified this year as the target area is far bigger. We think that it will not be difficult to achieve this task, although we may run into issues with spacing: we had hoped to simply push into the dock to keep us in place, but that may interfere with our water blast depending on its mounting position. Goal 3 out of 5 successful runs Lower Priority Challenges Delivery Octagon Description The ASV detects the different posters around the center and delivers ducks and "beavers" (racquetballs) to various areas. A set number of racquetballs are provided as preload so that the ASV may complete this challenge without collecting additional objects. Analysis We expect this challenge to be somewhat difficult, although we still plan to complete it. We are designing a simple mechanism to deliver the preloaded balls to this challenge. We expect a challenge in detecting the specific areas to deliver balls to. Goal 2/5 successful deliveries Not Attempted Challenges Collection Octagon Description The ASV detects the triangular posters which designates the area to collect raquetballs and ducks from. The ASV may then use them as extra objects to deliver to the Delivery Octagon. Analysis UM::Autonomy decided that the mechanisms to grab and retrieve objects will be extremely difficult to produce. As such, we decided to put this challenge off. After the competition, we may work on this challenge more in case it appears again in 2025. Goal N/A UM::Autonomy extends a special thanks to Cole Biesemeyer from the Open Source Robotics Foundation for the 3D model of Nathan Benderson Park and Gdańsk University of Technology's SimLE: SeaSentinel team for creating 3D models of various competition elements. Competition Results UM::Autonomy placed 4th in Autonomy, and 1st among American Universities. Static Judging UM::Autonomy placed 1st in the technical report, 5th in the video, and 1st in the presentation for an overall 1st place in design documentation. Autonomous Challenge UM::Autonomy placed 4th, beating all other American universities. The Team The team feels very confident for the 2025 season. Many new members attended competition for the first time, creating an exciting environment where many members are familiar with competition practices allowing us to achieve further success in future years. "
} ,
{
"title" : "The Team",
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"tags" : "",
"url" : "/team/2024/",
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"content" : " The Team Get to know our talented members from 2023-24! LeadershipThe Leadership team at UM::Autonomy is responsible for steering the overall direction of the project. Combining strategic foresight with tactical knowledge, they coordinate all subteams and resources towards a common goal. Ensuring clear communication, effective decision-making, and the overall well-being of the team, their role is essential to the successful execution of the project. Their responsibilities extend beyond project management to include cultivating a supportive, inclusive, and motivated environment for all team members to thrive. Asheya Ashok Naik President LinkedIn Graduating Winter 2024 Ben Bruick Vice President President in 2024-2025 LinkedIn Graduating Winter 2026 Elaina Mann Treasurer Graduating Winter 2026 Isabella Minkin Business Lead LinkedIn Graduating Winter 2023 Aayush Shah Electrical Lead Electrical Lead in 2024-2025 Graduating Winter 2025 Gordon Fream Chief Naval Architect Chief Naval Architect in 2024-2025 LinkedIn Graduating Winter 2025 Ben Schattinger AI Lead AI Lead in 2024-2025 LinkedIn Graduating Winter 2026 Murphy Justian Task Planning Co-Lead LinkedIn Graduating Winter 2024 Georgia Zender Navigation & Controls Co-Lead AI Lead in 2024-2025 Email Graduating Winter 2025 Christopher Briggs Navigation & Controls Co-Lead Navigation & Controls Co-Lead in 2024-2025 LinkedIn Graduating Fall 2024 Kyle Gross Task Planning Co-Lead Graduating Winter 2025 Alice Ivanitskiy Mechanical Lead in 2024-2025 Graduating Winter 2026 Sparsh Bahadur Computer Vision Lead in 2024-2025 Email Graduating Winter 2026 Lani Quach Vice President in 2024-2025 Email Graduating Winter 2026 Siming Tang Computer Vision Lead in 2024-2025 LinkedIn Graduating Winter 2027 Jackson Donaldson Navigation & Controls Co-Lead in 2024-2025 Email Graduating Winter 2025 Tulasi Anantharamakrishnan Business Lead in 2024-2025 Graduating Winter 2027 Archita Saraiya Mechanical Lead in 2024-2025 Email Graduating Winter 2027 BusinessThe Business team plays an integral role in managing and propelling the project forward. Leveraging their skills in communication, finance, and logistics, they ensure that necessary resources are received and allocated effectively. The team is responsible for fostering relationships with sponsors, organizing outreach events, and maintaining the flow of information both internally and externally. Their efforts are key in enabling the technical teams to function and ensuring continual progress towards goals. Elaina Mann Treasurer Graduating Winter 2026 Isabella Minkin Business Lead LinkedIn Graduating Winter 2023 Siri Edupuganti Graduating Winter 2027 Tulasi Anantharamakrishnan Business Lead in 2024-2025 Graduating Winter 2027 Artificial IntelligenceThe AI team uses a modular approach to break down complex challenges into manageable parts. This strategy enhances the reliability, robustness, and overall efficiency of autonomous decision-making systems. It allows for parallel and individual development of each module, improving the overall productivity of our team.PerceptionOur Perception team plays a vital role in object identification. Through the application of advanced computer vision techniques and deep learning models, we optimally detect buoy shapes and colors with a high degree of accuracy. To recognize objects and gauge their distances, we utilize the latest sensor technologies, providing highly accurate mapping and a comprehensive understanding of the environment around the vessel.Task PlanningThe Task Planning team is responsible for strategizing the next move of the vessel. By generating waypoints that direct the vessel’s navigation, we are effective in navigating through complex obstacles and challenges. Our dedicated decision-making systems allow us to adapt to various navigational tasks, thereby improving the efficiency and productivity of our operations.Navigation & ControlsThe Navigation & Controls subteam uses the waypoints generated by the Task Planning team and information from the Computer Vision team to create the best path for the vessel. By employing path-finding algorithms and various sensors, we ensure a smooth, accurate, and efficient path is laid out. We also factor in environmental conditions such as wind and waves, guaranteeing the vessel’s stable and safe movement along the desired path. Our team makes sure to maintain the vessel’s position, velocity, and acceleration regardless of environmental factors. Sparsh Bahadur Computer Vision Member Computer Vision Lead in 2024-2025 Email Graduating Winter 2026 Lani Quach Task Planning Member Vice President in 2024-2025 Email Graduating Winter 2026 Ben Schattinger AI Lead AI Lead in 2024-2025 LinkedIn Graduating Winter 2026 Murphy Justian Task Planning Co-Lead LinkedIn Graduating Winter 2024 Georgia Zender Navigation & Controls Co-Lead AI Lead in 2024-2025 Email Graduating Winter 2025 Christopher Briggs Navigation & Controls Co-Lead Navigation & Controls Co-Lead in 2024-2025 LinkedIn Graduating Fall 2024 Kyle Gross Task Planning Co-Lead Graduating Winter 2025 Shruti Shah Computer Vision Lead Email Graduating Winter 2027 Siming Tang Computer Vision Lead Computer Vision Lead in 2024-2025 LinkedIn Graduating Winter 2027 Jackson Donaldson Task Planning Member Navigation & Controls Co-Lead in 2024-2025 Email Graduating Winter 2025 Aman Sikka Computer Vision Member Email Graduating Winter 2027 Kirthi Yadavalli Computer Vision Member Email Graduating Winter 2027 Jack Bacarra Graduating Winter 2027 Jonah Cohen Graduating Winter 2026 Ayaan Attassery Graduating Winter 2028 Emily Lin Graduating Winter 2028 Francisco Yan Graduating Winter 2028 Jason Lin Graduating Winter 2026 Rose Seidl Graduating Winter 2025 MechanicalThe Mechanical subteam focuses on designing and fabricating a competitive vessel each year, striving for a balance between hull stability, system accessibility, modularity in design, and a strategic thrust-to-weight ratio to achieve the team’s goals for autonomy at every competition. They not only fabricate the waterproof, hydrodynamic carbon fiber hullforms that house necessary equipment and sensors onboard, but they also carefully plan their vessel design to ensure the effective integration and mounting of these sensors, and any robotic mechanisms necessary for advanced capabilities. Gordon Fream Chief Naval Architect Chief Naval Architect in 2024-2025 LinkedIn Graduating Winter 2025 Jackson Brown Mechanical Member Email Graduating Fall 2023 Archita Saraiya Mechanical Member Mechanical Lead in 2024-2025 Email Graduating Winter 2027 Ethan Weathersby Mechanical Member Email Graduating Winter 2027 Alice Ivanitskiy Mechanical Lead in 2024-2025 Graduating Winter 2026 Arianna Ponce Graduating Winter 2028 Ethan Weathersby Graduating Winter 2027 Natalie Miesak Graduating Winter 2027 Ulrico Zampa Graduating Winter 2028 ElectricalThe Electrical team at UM::Autonomy is committed to the development and refinement of the vessel’s electrical systems. With an emphasis on reliability, safety, and maintainability, they focus on integrating failsafes, using off-the-shelf components, and enhancing system modularity.The team handles the crucial task of setting up a control system that competently converts commands into signals that the vessel’s propulsion system can utilize. The balance between responsiveness and safety in this system plays a significant role in the vessel’s overall performance.High on their priority list is the establishment of an efficient emergency stop system. The team constructs this system to account for various potential failure modes, ensuring that a safe and reliable method to halt the vessel is always available, thereby enhancing the reliability of operations.In sync with their commitment to maintainability and modularity, the Electrical team designs systems that feature standardized, removable connections where possible. This strategy facilitates swift troubleshooting and easier hardware replacement or upgrade, keeping the vessel competition-ready at all times. Aayush Shah Electrical Lead Electrical Lead in 2024-2025 Graduating Winter 2025 Colin Crespo Electrical Member Email Graduating Winter 2027 Gabriel Maglione Electrical Member Email Graduating Winter 2027 Hudson Hall Electrical Member Email Graduating Winter 2025 Mick Gordinier Electrical Member Email Graduating Winter 2025 Connor Stein Graduating Jack Stevens Graduating Winter 2027 Matthew Williams Graduating Winter 2026 Advanced CapabilitiesThe Advanced Capabilities team designs solutions to tackle the more intricate challenges of the competition that extend beyond fundamental navigation. Balancing the constraints of deck space and system compatibility, they concentrate on crafting reliable, simple, and accurate mechanisms that can be developed and integrated onto the vessel separately.UM::Autonomy pays meticulous attention to maintaining a consistent system performance under various operating conditions. To handle specific competition tasks, the team prefers solutions that are straightforward yet effective. Every design choice is guided by a commitment to simplicity and reliability, ensuring that the vessel’s operations are robust and consistent.The integration of their designs is done in a manner that reduces system complexity, while enabling unified control. The end goal is always to enhance the operational robustness and reliability of the vessel. Ben Bruick Advanced Capabilities Lead President in 2024-2025 LinkedIn Graduating Winter 2026 Joshua Beatty Advanced Capabilities Member LinkedIn Graduating Winter 2024 Jackson Brown Advanced Capabilities Member Email Graduating Fall 2023 Archita Saraiya Advanced Capabilities Member Mechanical Lead in 2024-2025 Email Graduating Winter 2027 "
} ,
{
"title" : "The Phoenix: 2024 Boat",
"category" : "",
"tags" : "",
"url" : "/history/2024/",
"date" : "",
"content" : " 2024: The Phoenix Returns Boat Design and Techical Information Learn more ▼ Learn about past seasons The Phoenix Returns Detailed Technical Report ↓ At the 2024 RoboBoat Competition, The Phoenix sets sail once more—emerging stronger from the trials and tribulations of its past, UM::Autonomy’s 2024 vessel focuses on simplicity in design and workflow in order to further ensure a reliable, maintainable, and modular system. Understanding the tighter time constraints of the 2024 season, a greater emphasis was placed on design validation and in-water testing, which was facilitated by maintaining an operable vessel and reducing design complexity from last year. This design strategy was complemented with a testing strategy that relocated the team permanently into its testing environment, made testing a weekly process from the start of the season, and allowed for multiple modes of testing to guarantee success. Trimaran Design The Phoenix takes advantage of a trimaran design to maximize stability and minimize water plane area. This design significantly reduces water resistance and enhances speed and maneuverability. At the 2023 RoboBoat competition, Phoenix demonstrated the exceptional strength of this design by successfully navigating high winds - a challenge that other teams were unable to meet. The Phoenix weighs 40 pounds, has a length of 56”, a beam (width) of 30”, and has an overall height of 26”. It can generate thrusts of more than 30 pounds. Constructed from carbon fiber, Phoenix's hull combines robustness and reduced weight thanks to this material's superior strength-to-weight ratio. The carbon fiber ensures increased durability of the hull and contributes to the lighter overall design, boosting the vessel's performance in various conditions. Camera The Phoenix's camera provides the main input to the boat's autonomous capabilities. Using the YOLOv8 computer vision algorithm, all competition elements are detected. LiDAR The Velodyne Puck (previously known as VLP-16) provides the boat with a 3D map of its surroundings. Using detection data from the camera, the LiDAR provides supplemental depth data to determine objects' distance. GPS Antennas Dual antennas enable the boat to ascertain its heading with remarkable sub-degree accuracy. This precision reduces reliance on magnetic sensors, thereby mitigating any potential inaccuracies or inconsistencies due to magnetic disturbances or anomalies in the environment. This positions the vessel for superior navigation performance under a variety of conditions. T500 Thrusters Two Blue Robotics T500 Thrusters provide the Phoenix with powerful thrust. By placing the thrusters below the hulls to allow clean water flow at the inlets, ventilation is avoided. Ubiquiti Rocket A high-performance, high-range WiFi system allows high speed (around 500Mbps) communication between the boat and shore. Electrical Box Our vessel's electrical parts are housed in the electrical box. It is easily removable to allow servicing outside the hull. 6-cell Lithium Battery A 22.2 Volt, 222 Watt-hour battery provides the boat with a powerful electricity source. Custom Printed Circuit Board Motor control is provided by the team's custom PCB. This board is responsible for receiving motor control signals from the computer and remote controller. It also acts as the remote emergency stop by cutting power to motors if remote control signal is lost. To ensure safety and reliability, its firmware is written in Rust, a low-level but memory safe language. VN-300 GPS and IMU The VN-300 provides inertial sensors, dual GPS receivers, and sophisticated Kalman filtering algorithms. This combination provides extremely accurate estimates of the vessel's position, velocity, and attitude. It ensures unmatched performance under both static and dynamic conditions, providing high precision direction data for the vessel's navigation tasks. Beelink SEi12 Featuring an Intel i5-12450H processor and 16GB RAM, this compact computer outperforms many similar-sized alternatives. Despite its superior performance, it maintains impressive efficiency with an idle power draw of just 5 Watts. The processor's integrated graphics enhance computer vision tasks, delivering outstanding results. The computer is outfitted with a 1 Terabyte drive to store data from competition runs. A Docker-based software environment configured with ROS 1 Noetic ensures that the exact same software is present when testing on a computer and on the boat—allowing team members to use their choice of Linux, Windows, or macOS on their personal computers without differences. The team uses Git as our version control system. Most of the team's code is written in C++, with several Python scripts for small tasks. "
} ,
{
"title" : "RoboBoat 2025",
"category" : "",
"tags" : "",
"url" : "/roboboat/2025/",
"date" : "",
"content" : " Photo from RoboBoat 2023: Ocean Exploration RoboBoat 2025: Guardians of the Waters Nathan Benderson Park Sarasota, Florida RoboNation RoboBoat is an international competition where students design, build, and compete with self-driving robotic boats, in a series of tests aimed at challenging teams through a variety autonomous (self-driving) tasks. We will be joined by 27 other teams from 4 continents. Competition Strategy Due to the short build cycle this year—just 5 months from competition details to competition—we decided to reuse our 2023 hull, The Phoenix, albeit with major structural improvements. We focused on simplicity in design and workflow in order to further ensure a reliable, maintainable, and modular system. Understanding the tighter time constraints of this season, we put a greater emphasis was placed on design validation and in-water testing, which was facilitated by maintaining an operable vessel and reducing design complexity from last year. We moved to testing strategy that relocated the team permanently into its testing environment, made testing a weekly process from the start of the season, and allowed for multiple modes of testing to guarantee success. Scroll down for more details ↓ Static Judging Criteria Besides a team's performance autonomously, we are judged on aspects of both our team and the boat. Design Documentation The team must prepare a website, a technical design report, and a video for judges to score. These are evaluated based on how well they introduce the team and its structure as well as design considerations of the boat. Presentation The team must present to the judges live their decisions leading to the design of the boat. Other Judging Criteria Before the boat can participate in an autonomous challenge, several prerequisite activites must be completed. Static Safety Inspection As the boats are very high powered, a runaway boat could damage itself and hurt others. Therefore, competition staff ensure that the boat follows several safety rules: The boat stops when a physical red button is pressed on the boat The boat stops when a remote switch is flipped, or the remote switch loses power or connection The boat does not have any sharp edges so that people in the water can touch the boat safely UM::Autonomy is proud to have been one of the first three teams to pass the safety inspection at the 2023 competition. We have continued this spirit throughout the 2023-2024 year: safety is considered highly in all situations, from battery and testing site training to ensuring the vessel is safe at all times. Boat Thrust-to-Weight Ratio The competition rewards fast and light craft. Therefore, a sliding scale is used where points are lost faster the heavier it gets. The boat is weighed and its thrust is measured every day it is entered in the water. The Phoenix weighs 40 pounds, the lightest weight class. Autonomy Challenge The main part of the challenge is the tasks that the vessel must perform autonomously. However, we must first qualify before we perform autonomous operation on the full course. Qualification Teams will be given opportunities to practice, display their capabilities, and earn their spot in the final round. They will have access to three duplicate courses, each containing the eight tasks. Throughout this time, teams can choose to test strategies, gather data, or qualify for tasks. A certain level of completion—"minimum performance criteria"—is required to complete a task. Once a team has qualified for enough tasks, they enter the Semi-Final rounds. Semi-Finals Teams must score some number of points in Semi-Finals rounds to progress to Finals. Finals Teams who have successfully qualified will have access to the finals courses. Importantly, each vessel must operate autonomously for the entire duration of the run; remote-controlled survey runs are not allowed. This means teams cannot manually control their vessels to gather data or assess the field before or during their time slot, emphasizing the critical importance of pre-run preparation and programming accuracy. They will be required to navigate through initial gates, attempt a series of tasks in their chosen sequence, and finally return to the home base to conclude the run. All teams making it to the final round will secure a higher final ranking over those who do not reach this stage of the competition. High Priority Challenges Navigation Channel Description This challenge is mandatory before attempting other tasks. The ASV needs to pass through two sets of gates (a pair of red and green buoys) and starts autonomous navigation at a minimum of 6 ft before the set of gates. Analysis As it is mandatory, this challenge is of high importance. In 2019, the boat could only successfully pass the navigation channel once out of four qualification runs as a result of a major electrical failure onboard. In 2023, it was completed with high success due to our extensive testing in water and in simulation. However, additional tuning was needed upon arrival to the competition site as the boat's computer vision system would occasionally fail to detect a buoy. Goal 14 out of 15 successful runs Follow the Path Description The ASV passes through between multiple sets of gates (pairs of red and green buoys) The ASV also avoids intermittent yellow and black buoys of various sizes and counts them. Analysis The challenge requires minimal external hardware or software development and mainly just involves careful navigational operability and fine motor control. This challenge should be tested and fine-tuned early in the development process. In 2023, we found that this challenge revealed some disadvantages of our indoors testing area. Specifically, our obstacle-avoidance system decided that the best way to avoid hitting buoys was to go outside of the red-green channel! While technically following our instructions, it didn't score many points. We have since revised our navigation algorithm to take this restriction into account. Goal 9 out of 10 successful runs Speed Challenge Description The ASV enters the gate buoys, maneuvers around the yellow marker buoy, and exits thought the same gate buoys, as quickly as possible. The timer starts when the bow (front) crosses the gate buoys and stops when the stern (back) crosses the gate buoys. Analysis Based on the 2023 score sheet, teams that completed this challenge did so in 50, 75, and 100 seconds. This is significantly higher than the 2019 score sheet which revealed that a time between 25-45s is needed to remain competitive, with the fastest 2019 run coming in at 27 seconds. Nevertheless, we plan to beat the 2023 times and remain competitive with those that competed in 2019. This year introduces an obstacle buoy. We do not expect a challenge with this because our navigation algorithm is already very familiar with avoiding buoys, and we do not expect to even need any code changes. Goal 9 out of 10 successful runs. We hope for a baseline of 45 seconds, and a goal of 35 seconds. Docking Description Before the time slot starts, teams are assigned a color and must dock at the bay with the matching color. To dock, contact must simply be made with the dock. Analysis This challenge is a bit more involved in terms of computer and color/shape recognition but does not require external hardware development. We plan to train our computer vision model on color only, as it was all but guaranteed that each poster will be a unique color. We had success with this strategy in 2023, and plan to do it again. Goal 9 out of 10 successful runs Medium Priority Challenges Duck Wash Description The ASV detects the target duck poster and shoots water on it for 5 seconds continuously. The ASV may pump the water from the environment or store it on board. Analysis This task follows a similar task from last year, also involving water blasts. However, the task is simplified this year as the target area is far bigger. We think that it will not be difficult to achieve this task, although we may run into issues with spacing: we had hoped to simply push into the dock to keep us in place, but that may interfere with our water blast depending on its mounting position. Goal 3 out of 5 successful runs Lower Priority Challenges Delivery Octagon Description The ASV detects the different posters around the center and delivers ducks and "beavers" (racquetballs) to various areas. A set number of racquetballs are provided as preload so that the ASV may complete this challenge without collecting additional objects. Analysis We expect this challenge to be somewhat difficult, although we still plan to complete it. We are designing a simple mechanism to deliver the preloaded balls to this challenge. We expect a challenge in detecting the specific areas to deliver balls to. Goal 2/5 successful deliveries Not Attempted Challenges Collection Octagon Description The ASV detects the triangular posters which designates the area to collect raquetballs and ducks from. The ASV may then use them as extra objects to deliver to the Delivery Octagon. Analysis UM::Autonomy decided that the mechanisms to grab and retrieve objects will be extremely difficult to produce. As such, we decided to put this challenge off. After the competition, we may work on this challenge more in case it appears again in 2025. Goal N/A UM::Autonomy extends a special thanks to Cole Biesemeyer from the Open Source Robotics Foundation for the 3D model of Nathan Benderson Park and Gdańsk University of Technology's SimLE: SeaSentinel team for creating 3D models of various competition elements. Competition Results UM::Autonomy placed 4th in Autonomy, and 1st among American Universities. Static Judging UM::Autonomy placed 1st in the technical report, 5th in the video, and 1st in the presentation for an overall 1st place in design documentation. Autonomous Challenge UM::Autonomy placed 4th, beating all other American universities. The Team The team feels very confident for the 2025 season. Many new members attended competition for the first time, creating an exciting environment where many members are familiar with competition practices allowing us to achieve further success in future years. "
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"content" : " The Team Get to know our talented members from 2025-26! LeadershipThe Leadership team at UM::Autonomy is responsible for steering the overall direction of the project. Combining strategic foresight with tactical knowledge, they coordinate all subteams and resources towards a common goal. Ensuring clear communication, effective decision-making, and the overall well-being of the team, their role is essential to the successful execution of the project. Their responsibilities extend beyond project management to include cultivating a supportive, inclusive, and motivated environment for all team members to thrive. Alice Ivanitskiy Co-President Graduating Winter 2026 Sparsh Bahadur Co-President Email Graduating Winter 2026 Jack Bacarra AI Lead Graduating Winter 2027 Jonah Cohen AI Lead Graduating Winter 2026 Siming Tang Computer Vision Lead LinkedIn Graduating Winter 2027 Ayaan Attassery Task Planning Lead Graduating Winter 2028 Jason Lin Computer Vision Lead Graduating Winter 2026 Rose Seidl Navigation & Controls Lead Graduating Winter 2025 Tulasi Anantharamakrishnan Business Lead Graduating Winter 2027 Colin Crespo Electrical Lead Email Graduating Winter 2027 Henry Vergowven Electrical Lead Archita Saraiya Mechanical Lead Email Graduating Winter 2027 Arianna Ponce Advanced Capabilities Lead Graduating Winter 2028 BusinessThe Business team plays an integral role in managing and propelling the project forward. Leveraging their skills in communication, finance, and logistics, they ensure that necessary resources are received and allocated effectively. The team is responsible for fostering relationships with sponsors, organizing outreach events, and maintaining the flow of information both internally and externally. Their efforts are key in enabling the technical teams to function and ensuring continual progress towards goals. Siri Edupuganti Business Member Graduating Winter 2027 Tulasi Anantharamakrishnan Business Lead Graduating Winter 2027 Artificial IntelligenceThe AI team uses a modular approach to break down complex challenges into manageable parts. This strategy enhances the reliability, robustness, and overall efficiency of autonomous decision-making systems. It allows for parallel and individual development of each module, improving the overall productivity of our team.PerceptionOur Perception team plays a vital role in object identification. Through the application of advanced computer vision techniques and deep learning models, we optimally detect buoy shapes and colors with a high degree of accuracy. To recognize objects and gauge their distances, we utilize the latest sensor technologies, providing highly accurate mapping and a comprehensive understanding of the environment around the vessel.Task PlanningThe Task Planning team is responsible for strategizing the next move of the vessel. By generating waypoints that direct the vessel’s navigation, we are effective in navigating through complex obstacles and challenges. Our dedicated decision-making systems allow us to adapt to various navigational tasks, thereby improving the efficiency and productivity of our operations.Navigation & ControlsThe Navigation & Controls subteam uses the waypoints generated by the Task Planning team and information from the Computer Vision team to create the best path for the vessel. By employing path-finding algorithms and various sensors, we ensure a smooth, accurate, and efficient path is laid out. We also factor in environmental conditions such as wind and waves, guaranteeing the vessel’s stable and safe movement along the desired path. Our team makes sure to maintain the vessel’s position, velocity, and acceleration regardless of environmental factors. Sparsh Bahadur Computer Vision Member Email Graduating Winter 2026 Jack Bacarra AI Lead Graduating Winter 2027 Jonah Cohen AI Lead Graduating Winter 2026 Siming Tang Computer Vision Lead LinkedIn Graduating Winter 2027 Ayaan Attassery Task Planning Lead Graduating Winter 2028 Emily Lin Computer Vision Member Graduating Winter 2028 Francisco Yan Task Planning Member Graduating Winter 2028 Jason Lin Computer Vision Lead Graduating Winter 2026 Rose Seidl Navigation & Controls Lead Graduating Winter 2025 MechanicalThe Mechanical subteam focuses on designing and fabricating a competitive vessel each year, striving for a balance between hull stability, system accessibility, modularity in design, and a strategic thrust-to-weight ratio to achieve the team’s goals for autonomy at every competition. They not only fabricate the waterproof, hydrodynamic carbon fiber hullforms that house necessary equipment and sensors onboard, but they also carefully plan their vessel design to ensure the effective integration and mounting of these sensors, and any robotic mechanisms necessary for advanced capabilities. Alice Ivanitskiy Mechanical Member Graduating Winter 2026 Ben Bruick Mechanical Member LinkedIn Graduating Winter 2026 Archita Saraiya Mechanical Lead Email Graduating Winter 2027 Ethan Weathersby Member Graduating Winter 2027 Natalie Miesak Member Graduating Winter 2027 Ulrico Zampa Chief Naval Architect Graduating Winter 2028 ElectricalThe Electrical team at UM::Autonomy is committed to the development and refinement of the vessel’s electrical systems. With an emphasis on reliability, safety, and maintainability, they focus on integrating failsafes, using off-the-shelf components, and enhancing system modularity.The team handles the crucial task of setting up a control system that competently converts commands into signals that the vessel’s propulsion system can utilize. The balance between responsiveness and safety in this system plays a significant role in the vessel’s overall performance.High on their priority list is the establishment of an efficient emergency stop system. The team constructs this system to account for various potential failure modes, ensuring that a safe and reliable method to halt the vessel is always available, thereby enhancing the reliability of operations.In sync with their commitment to maintainability and modularity, the Electrical team designs systems that feature standardized, removable connections where possible. This strategy facilitates swift troubleshooting and easier hardware replacement or upgrade, keeping the vessel competition-ready at all times. Colin Crespo Electrical Lead Email Graduating Winter 2027 Henry Vergowven Electrical Lead Jack Stevens Electrical Member Graduating Winter 2027 Matthew Williams Electrical Member Graduating Winter 2026 Advanced CapabilitiesThe Advanced Capabilities team designs solutions to tackle the more intricate challenges of the competition that extend beyond fundamental navigation. Balancing the constraints of deck space and system compatibility, they concentrate on crafting reliable, simple, and accurate mechanisms that can be developed and integrated onto the vessel separately.UM::Autonomy pays meticulous attention to maintaining a consistent system performance under various operating conditions. To handle specific competition tasks, the team prefers solutions that are straightforward yet effective. Every design choice is guided by a commitment to simplicity and reliability, ensuring that the vessel’s operations are robust and consistent.The integration of their designs is done in a manner that reduces system complexity, while enabling unified control. The end goal is always to enhance the operational robustness and reliability of the vessel. Arianna Ponce Advanced Capabilities Lead Graduating Winter 2028 "
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"title" : "Orca: 2025 Boat",
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"content" : " 2025: Orca Kills The Competition Boat Design and Technical Information Learn more ▼ Learn about past seasons The Orca Detailed Technical Report ↓ FIX At the 2024 RoboBoat Competition, The Phoenix sets sail once more—emerging stronger from the trials and tribulations of its past, UM::Autonomy’s 2024 vessel focuses on simplicity in design and workflow in order to further ensure a reliable, maintainable, and modular system. Understanding the tighter time constraints of the 2024 season, a greater emphasis was placed on design validation and in-water testing, which was facilitated by maintaining an operable vessel and reducing design complexity from last year. This design strategy was complemented with a testing strategy that relocated the team permanently into its testing environment, made testing a weekly process from the start of the season, and allowed for multiple modes of testing to guarantee success. Trimaran Design The Phoenix takes advantage of a trimaran design to maximize stability and minimize water plane area. This design significantly reduces water resistance and enhances speed and maneuverability. At the 2023 RoboBoat competition, Phoenix demonstrated the exceptional strength of this design by successfully navigating high winds - a challenge that other teams were unable to meet. The Phoenix weighs 40 pounds, has a length of 56”, a beam (width) of 30”, and has an overall height of 26”. It can generate thrusts of more than 30 pounds. Constructed from carbon fiber, Phoenix's hull combines robustness and reduced weight thanks to this material's superior strength-to-weight ratio. The carbon fiber ensures increased durability of the hull and contributes to the lighter overall design, boosting the vessel's performance in various conditions. Camera The Phoenix's camera provides the main input to the boat's autonomous capabilities. Using the YOLOv8 computer vision algorithm, all competition elements are detected. LiDAR The Velodyne Puck (previously known as VLP-16) provides the boat with a 3D map of its surroundings. Using detection data from the camera, the LiDAR provides supplemental depth data to determine objects' distance. GPS Antennas Dual antennas enable the boat to ascertain its heading with remarkable sub-degree accuracy. This precision reduces reliance on magnetic sensors, thereby mitigating any potential inaccuracies or inconsistencies due to magnetic disturbances or anomalies in the environment. This positions the vessel for superior navigation performance under a variety of conditions. T500 Thrusters Two Blue Robotics T500 Thrusters provide the Phoenix with powerful thrust. By placing the thrusters below the hulls to allow clean water flow at the inlets, ventilation is avoided. Ubiquiti Rocket A high-performance, high-range WiFi system allows high speed (around 500Mbps) communication between the boat and shore. Electrical Box Our vessel's electrical parts are housed in the electrical box. It is easily removable to allow servicing outside the hull. 6-cell Lithium Battery A 22.2 Volt, 222 Watt-hour battery provides the boat with a powerful electricity source. Custom Printed Circuit Board Motor control is provided by the team's custom PCB. This board is responsible for receiving motor control signals from the computer and remote controller. It also acts as the remote emergency stop by cutting power to motors if remote control signal is lost. To ensure safety and reliability, its firmware is written in Rust, a low-level but memory safe language. VN-300 GPS and IMU The VN-300 provides inertial sensors, dual GPS receivers, and sophisticated Kalman filtering algorithms. This combination provides extremely accurate estimates of the vessel's position, velocity, and attitude. It ensures unmatched performance under both static and dynamic conditions, providing high precision direction data for the vessel's navigation tasks. Beelink SEi12 Featuring an Intel i5-12450H processor and 16GB RAM, this compact computer outperforms many similar-sized alternatives. Despite its superior performance, it maintains impressive efficiency with an idle power draw of just 5 Watts. The processor's integrated graphics enhance computer vision tasks, delivering outstanding results. The computer is outfitted with a 1 Terabyte drive to store data from competition runs. A Docker-based software environment configured with ROS 1 Noetic ensures that the exact same software is present when testing on a computer and on the boat—allowing team members to use their choice of Linux, Windows, or macOS on their personal computers without differences. The team uses Git as our version control system. Most of the team's code is written in C++, with several Python scripts for small tasks. "
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"content" : " Contact Us We would love to hear from you! More details ▼ Sponsors, Information, and Other Requests General Contact: umautonomy@umich.edu Looking to Join UM::Autonomy? Join Our Mailing List to Learn More Join our mailing list and we'll let you know when our next meeting is! We'll also offer one-on-one meetings with teams you're interested in. Specific Members Have a specific team member you'd like to contact? Find them on our Team page. Social Media Facebook Twitter LinkedIn Instagram "
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"title" : "FAQ",
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"url" : "/faq/",
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"content" : " FAQ Frequently Asked Questions Learn more ▼ FAQs Do I need any experience or technical background to join? Nope! We welcome everyone to build and learn together—no matter if you are a freshman or a graduate student; majoring in economics or engineering. Come check us out! What is the time commitment? We are flexible: commit as much as you are willing and able to. Generally, members commit around 2-4 hours a week but we definitely understand that throughout the semester, priorities and workload could change. When does the team recruit? How do I become a member? We recruit all year round! Just join our mailing list to get periodic updates on team activity or send us an email to get added to our Slack channel if you're interested in contributing to the team as a member. Feel free to send us an email if you want to learn more about the projects we do or have any questions. Is the team active during the summer? Yes we are! We are less active than during the Fall/Winter semesters but we typically do have some projects for members to work on throughout the summer. Can I earn credit through UM::Autonomy? Yes! Starting your second semester of involvement in our project team, the work you do can be counted towards MDP Academic Credit. Read more about MDP here: https://mdp.engin.umich.edu Are there any fees to join? Nope. Nada. $0. Our team obtains materials and equipment from the generous support of the college, industry sponsors, alumni and friends. View the list of our sponsors here: https://umautonomy.com/sponsors How do I support the team? Send us an email at umautonomy@umich.edu! Some ways you can support the team include providing funding, mentorship, materials or equipment. We greatly appreciate any support our team gets. Did we miss a question you had? Send us an email at umautonomy@umich.edu! "
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"content" : " Our History See UMA's past achievements and accomplishments! Learn more ▼ 2025: Orca FIX At the 2024 RoboBoat Competition, The Phoenix sets sail once more—emerging stronger from the trials and tribulations of its past, UM::Autonomy’s 2024 vessel focuses on simplicity in design and workflow in order to further ensure a reliable, maintainable, and modular system. Understanding the tighter time constraints of the 2024 season, a greater emphasis was placed on design validation and in-water testing, which was facilitated by maintaining an operable vessel and reducing design complexity from last year. This design strategy was complemented with a testing strategy that relocated the team permanently into its testing environment, made testing a weekly process from the start of the season, and allowed for multiple modes of testing to guarantee success. Learn More Technical Design Report Video Competition Details The 2025 Team 2024: The Phoenix Returns At the 2024 RoboBoat Competition, The Phoenix sets sail once more—emerging stronger from the trials and tribulations of its past, UM::Autonomy’s 2024 vessel focuses on simplicity in design and workflow in order to further ensure a reliable, maintainable, and modular system. Understanding the tighter time constraints of the 2024 season, a greater emphasis was placed on design validation and in-water testing, which was facilitated by maintaining an operable vessel and reducing design complexity from last year. This design strategy was complemented with a testing strategy that relocated the team permanently into its testing environment, made testing a weekly process from the start of the season, and allowed for multiple modes of testing to guarantee success. Learn More Technical Design Report Video Competition Details The 2024 Team 2023: The Phoenix Rising from the ashes of defeat and disorder following the pandemic, the phoenix soars to competition this year with renewed pride, a greater sense of togetherness, and a singular focus: to compete hard while having fun. After a hard-fought competition the previous year, the team chose to focus on four principles for this year: decoupling, reliability, maintainability, and performance. Not only was the AI pipeline decoupled and tested separately, but nearly all major components of the boat could be separated, making it extremely modular and maintainable. It was the first year that the team tested in the Marine Hydrodynamics Laboratory, and did so for over 100 hours. In addition, it was the first year that the boat was made out of carbon fiber from scratch - from the painstaking mold making, preparation, and vacuum resin infusion - in order to produce a boat that weighed less than 60 pounds while still delivering impeccable results. Learn More Technical Design Report Video Competition Details The 2023 Team 2022: Flying Sloth 2 The team’s monumental return to the RoboBoat Competition following the COVID-19 pandemic was met with triumphs as well as hardships. The team decided to refurbish the Flying Sloth from the 2017 competition in order to focus on testing and learning skills that were lost due to the lack of in-person opportunities. With the Flying Sloth providing a strong foundation for the accommodation of new software and hardware development thanks to the new water blast and skeeball challenges, a new superstructure made of carbon fiber was fitted onto the boat. The goal was to use the strong foundation to design a fast system that provided lots of room for mounting electronics and hardware while being easily accessible. Technical Design Report 2021: C-3PO Joined by our drone, R2-D2 C-3PO is named in reference to the trimaran (three hulls) design of our boat. In previous years, our boat has typically used a monohull design but this year, the trimaran design is used to improve boat stability. With the continuation of the current roboboat competition virtual format and the uncertainty of the global pandemic, the team wanted to revise the previous iteration of the boat/drone which was unfortunately never completed due to Covid-19. This meant keeping and making small improvements to areas of strength such as the sensor suite, electrical box system, carbon fiber fiber infusion process, and much of our software while also experimenting with more innovative approaches such as the new hull form, hydrophone system, and addition of a CV/Lidar Deep learning approach. These changes allowed the team to better tackle the RoboBoat competitions challenges and ultimately push the organization to greater heights. Technical Design Report 2020: Pass Joined by our drone, “No Record COVID” Due to the COVID-19 pandemic, we experienced a shutdown and saw a shift in the 2020 RoboBoat competition format to a virtual one. Although our development cycle and competition period was disappointedly cut short, we believe we have made many important improvements to our design. With new hardware, a reworked sensor suite, and a modified hull form, we looked to bring our autonomous boat design to the next level of professional design. We believe these changes have greatly improved our ability to tackle the challenges of the RoboBoat competition and have enabled future improvements and successes. Technical Design Report Video 2019: Daedelus First Carbon Fiber boat, joined by our first custom drone, Icarus We came out of 2018 with the right balance of optimism and frustration with the result of competition. We wanted to make a change and Daedalus is the culmination of every sub team going above and beyond anything we have ever attempted on this team. Daedalus is the first Carbon Fiber boat in the team’s history, it’s the first time we have used a custom implementation of Robot Operation System (ROS), and the first time overhauling almost every piece of hardware we have both on, and inside the boat. Joined by the team’s first entirely custom drone, Icarus, our entry into RoboBoat 2019 is perhaps our most daring attempt yet. Detroit as whole, and our biggest supporters in Michigan are making a comeback, we’ve stepped up to do our part too. Technical Design Report Video 2018: John Sea Na 3rd among American Universities 2018 gave our team a glimpse of our potential for the future. As a predominantly Sophomore and Junior team we recognized the need to target specific areas in order to return the team to success. We focused on team organization, system engineering and collaboration across groups. By making targeted improvements as a team, we were able to make significant progress at competition. After previously not making it into finals the year prior, we were headed into finals in 4th and finished 3rd among American Universities. We see this year as an incredible opportunity to build on the experience of a majority upperclassmen team and strive to win the 2019 RoboBoat competition. Technical Design Report Video 2017: Flying Sloth First trimaran and first entirely SLAM dependent system The 2017 competition was a year of firsts for UM::Autonomy: first trimaran, first entirely SLAM dependent system and first total overhaul of our code base. Our team, almost entirely composed of underclassmen, was awarded 2nd place in the static competition. We achieved this result despite constant hardware breakdowns, which prevented the AI team from doing adequate testing before the preliminary competition. As a result, we could not effectively tune our boat’s thrusters for the strong competition winds and were forced to manually tune during competition runs. Moving forward we look to simplify the electrical box in order to make processing signal data more efficient, and avoid time-intensive repairs. Technical Design Report Video 2016: Thurman Change from catamaran to monohull design Thurman took home 6th place in the 9th annual AUVSI RoboBoat competition. In a departure from previous designs, Thurman has a monohull instead of a catamaran design. This change provided a large internal volume, which was able to house all of the electronics. The thrusters were mounted to outriggers which were added for increased stability with the change in hull design. One addition to the docking challenge this year was attaching a velcro strip to the dock, the ramming mechanism on the front of the hull was designed to deposit this velcro strip. This year the hull was done especially early which allowed for greater testing time before mock competition. Technical Design Report Video 2015: Sharkbyte Waterproofing and water cooling system After a strong qualifying run, SharkByte qualified for the finals of the 8th annual RoboBoat competition in first place. "SharkByte took home 3rd place in the finals. A major design change between SharkByte and previous boats was waterproofing. By using fully waterproof electrical connectors and a water cooling system, SharkByte was more than just splash-proof. The electrical system was new this year to accommodate this. After moving Hugh Jackman’s (Sharkbyte's predecessor) deck higher off the water with its taller hulls, it was decided that the waterproofing of the electrical systems would allow the deck to sit much lower to the water. This lowered center of gravity provided increased stability. The main goals of UM::Autonomy this year were the waterproofing and improved hull design. Technical Design Report 2014: Hugh Jackman Taller hulls for protection from water splash Hugh Jackman, named after the equally impressive actor, acquired 8th place in the 2014 RoboBoat competition. Even though Hugh Jackman’s performance did not reach our high standards, this boat had several improvements. Hugh has completely new, taller hulls which allows our electronics to be more protected from water splash. Additionally, with a brand new electric box, Hugh is more organized and more efficient than ever. With all of these revisions, Hugh has successfully paved the way for future iterations to perform exceedingly well. UM::Autonomy’s 2014 goal was to practice multiple, full-scale tests including our own mock competition and general testing in the Lurie Fountain. Technical Design Report Video 2013: Eve Accompanied by WALL-E, a small land vehicle Eve took 4th place in the 6th annual RoboBoat competition. The main differences between Eve and her predecessors include a major camera upgrade, as well as a new hull shape and propulsion configuration that allowed for much greater maneuverability, though she used the same electrical system as her predecessor Serenity. Eve was also accompanied by a small land vehicle named WALL-E who was designed to handle the amphibious landing challenge. Eve also featured a NERF gun that was to be used for a shooting challenge. The main focus of UM::Autonomy during this year was quality manufacturing and thorough testing. Technical Design Report 2012: Serenity 1st place at the 5th annual RoboBoat competition Serenity consistently completed the entire buoy course with some major software upgrades and attempted the “Hot Sign” and “Poker Chip” challenges. At 106 Lbs Serenity weighed in as UM::Autonomy’s heaviest boat to date. Serenity’s most notable differences from her predecessor Wolvemarine include an entirely new electrical system and vision system. Serenity did however use the same hulls as her predecessor Wolvemarine. Serenity was equipped with an Infrared Camera as well as a Water Cannon. The main focus of the team this year was the completion of the buoy course. Technical Design Report 2011: Wolvemarine First boat to create 3D Point Clouds of its environment Wolvemarine was built to compete in the 4th annual RoboBoat competition. Distinctive for its innovative panning Panning Lidar, Wolvemarine was the first boat in competition history to create 3D Point Clouds of its environment. Wolvemarine also featured a new Hull shape that allowed for greater speed through the water. Although UM::Autonomy took 1st place in the static judging portion of the competition we experienced several technical issues, causing us to do very poorly during the rest of the competition. However, the team did receive the “Edge of the Envelope” award for the major technological advances that were developed. Technical Design Report 2010: Mjolnir First boat to feature custom made fiberglass hulls and Lidar Mjolnir was created to compete in the 3rd annual RoboBoat competition and succeeded in claiming 1st place in the Static Judging as well as competition as a whole. Named after the hammer of the Norse god Thor; Mjolnir was a major achievement for UM::Autonomy. Mjolnir was the first boat made by UM::Autonomy to feature custom made fiberglass hulls and the electrical system was created entirely from scratch. Mjolnir was the first boat in RoboBoat history to feature a laser range finder, AKA Lidar. "
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"title" : "Blog",
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"content" : " The latest news Hear our latest activities, events and progress! ✎ Add Post Make your ROS & Eigen C++ code compile faster with precompiled headers Using Clang to profile time spent per file November 26, 2023 Read More Steering Towards Success: AI-Navigation/Controls Subteam's Progress on Autonomous Navigation Optimizing Navigation Pathways, Adapting Control Code, and Enhancing Autonomous Operation November 12, 2023 Read More ECE Family Fun Night October 01, 2023 Read More Sponsor Highlight: Boeing! September 23, 2023 Read More Ford Motor Company's Immersion Day August 29, 2023 Read More From High School to Leading UM::Autonomy at University of Michigan August 05, 2023 Read More "
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"content" : " Looking to Join UM::Autonomy? Join Our Mailing List to Learn More Join our mailing list and we'll let you know when our next meeting is! We'll also offer one-on-one meetings with teams you're interested in. UM::Autonomy is an award-winning student project team from the University of Michigan. Each year we build an autonomous boat to participate in the RoboNation RoboBoat competition. The boat is completely designed and programmed by students and must complete a wide range of challenges that are designed to test its autonomous abilities. We aim to further the study of autonomous robotics at the University of Michigan by providing students with hands-on multidisciplinary design experience in artificial intelligence, image processing, navigation, naval architecture, and mechanical design. Each year we strive to push the physical and conceptual bounds of our autonomous vehicle. The Competition The RoboBoat competition is an international competition where students design, build, and compete with self-driving robotic boats, in a series of tests aimed at challenging teams through a variety autonomous (self-driving) tasks. We will be joined by 27 other teams from 4 continents. The Boat Rising from the ashes of defeat and disorder following the pandemic, UM::Autonomy's 2024 boat, The Phoenix, will soar to competition with renewed pride, a greater sense of togetherness, and a singular focus: to compete hard while having fun. Made entirely out of carbon fiber to reduce weight, the boat features two large thrusters for speed and an Intel i5-12450H CPU to make autonomous decisions. Understanding the tighter time constraints of the 2024 season, a greater emphasis was placed on design validation and in-water testing, which was facilitated by maintaining an operable vessel and reducing design complexity from last year. This design strategy was complemented with a testing strategy that relocated the team permanently into its testing environment, made testing a weekly process from the start of the season, and allowed for multiple modes of testing to guarantee success. The Team UM::Autonomy has around 60 members across many different majors, including Computer Science, Electrical Engineering, Robotics, Naval Architecture, Art & Design, and Business. We are organized into several teams: Artificial Intelligence, Electrical, Mechanical, Advanced Capabilities, and Business. The Artificial Intelligence team is broken up into distinct subteams, which are Navigation & Controls, Task Planning, and Computer Vision. Interested in joining? Each year we look for motivated, talented individuals looking to make a splash in the world of autonomous vehicles. UMA has no selection process or time commitment requirements. This year, we have bite-sized projects for new members to onboard more smoothly and get a glimpse on the type of work we do. If our team and our opportunities float your boat, sign up for our mailing list, and we'll provide you with more information about our meetings! If you have any more questions, check out our FAQ and don't be afraid to reach out to us at umautonomy@umich.edu! Join Our Mailing List to Learn More Join our mailing list and we'll let you know when our next meeting is! We'll also offer one-on-one meetings with teams you're interested in. Sponsors Thank you to our wonderful sponsors for your contributions! Platinum Gold Lastly, thank you to our alumni and friends for your donations during Giving Blue Day! If you are interested in sponsoring UM::Autonomy, please email our 2023-24 administration team for more information. "
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"title" : "Projects",
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"content" : " Projects See what our teams have been up to! Learn more ▼ Artificial Intelligence Building off of a Robot Operating System architecture, AI tasks range from integrating COTS peripherals, to object detection, navigation, top-level task reasoning, and simulating the boat’s performance in a virtual competition course. Tech stack: Docker-based environment, Git for version-control, ROS Noetic (with an upgrade planned) for the architecture, and C++ & Python for the majority of the source code. Upgrade to ROS 2 Currently, the team uses ROS 1 Noetic, the final version in the ROS 1 series. Because it will be End-of-Life in 2025, it's important to plan a migration to a newer, supported version. CV/LIDAR deep learning The goal for CV deep learning is to implement deep learning models to accurately assign various classifications to objects detected by our boat’s camera. Upon completion, this new detection method will be integrated into our boat’s architecture, along with the development of a streamlined process for creating and training new models. Members will gain hands-on experience with one or more steps of the deep learning process, including model selection, design, training, and validation, as well as data annotation and preprocessing. Additionally, members may optionally also seek to gain a more theoretical understanding of deep learning models (and by extension, machine learning as a whole). LIDAR quantification The Lidar quantification project’s goal is to explore metrics to evaluate our current LIDAR detection algorithm. These metrics give a more precise, fine-grained measure on the quality of the LIDAR detection than our current method of using a visualizer to holistically observe the results of the detection. Members will gain experience with researching and conceptualizing various evaluation metrics, then evaluating the “informativeness” of each metric using a small, annotated 3D point cloud dataset. Potential metrics include those typically used in the fields of machine learning or data science, as well as custom metrics that we may formulate ourselves. Task Planning The task planning team acts as the brains of our boat - it analyzes perception data in order to direct the movements of path-planning and controls. This will include creating an advanced movement library that interfaces with the path-planner/controller, enabling our boat to strafe, activate anchor mode, maneuver the boat relative to an obstacle, and circle around an obstacle. Expanding the boat’s current high-level logic capabilities, this new library interfaces with perception, path-planning, controls, and simulation. Task-planning members will achieve a broad understanding of all of the boat’s sub-teams. This team is great for someone who likes abstract, critical thinking without having to spend lots of team reading through ROS documentation, academic articles, or complicated math. Task Sim team The Task Sim team’s goal is to build a simulated version of the RoboBoat competition tasks with Gazebo in order to test our boat code without an actual boat or water. This will involve testing the boat code as a single unit in its ability to traverse course obstacles, as well as constructing metrics to quantify and compare the performance of specific sections of the boat. Since we have a working simulator and some foundational code for path verification from previous semesters, existing code will be used and improved upon. Members of this team will gain experience with each section of the codebase, from perception to motion-modelling, by interfacing with and testing them, as well as experiencing simulation/physics engines. Electrical The Electrical Team is responsible for the allocation of power to the boat and management of cables and wires from the battery to the computer, sensors, and motors. Electrical Box Reorganization Our main focus this year was to make the box more organized, neat, and reliable. To achieve these goals, we introduced numerous new parts and custom PCB’s. For example, this year we purchased a breakout board, which splits an ATX cable into its derivative voltage supplies. We also overhauled nearly every single electrical splice connection and adopted a more crimp-oriented approach as opposed to soldering everything. New Electrical Box Over the past couple of years, we have been utilizing custom-cut polycarbonate to construct the main “shell” of the electrical box. This year, we used a pelican box - a rigid, waterproof, and air-tight crate. In order to drill holes in the box for some inputs and outputs, we needed to expose members to different tools needed to penetrate the thick material. Although this process was time consuming, all of the sub-team members were able to contribute, learn a new skill, and the electrical box was completed almost two months ahead of where it was last year. We also built our electrical box based on a CAD model designed and completed last summer. Hulls & Systems H&S Team works on the design and construction of the team’s boat. This includes designing around the boat’s electrical system, accompanying drone, and sensor specifications required by the boat’s autonomous systems. Hull Design Decisions In recent years, the team has used a trimaran hull design which offers incredible stability. However, turning performance is limited due to the resistance offered by the hulls. The team will evaluate whether to keep or change the design in 2023. Vacuum Infusion In previous years, the team utilized pre-resin-impregnated, high-temperature curing carbon fiber cured in an autoclave to construct the hulls. This year, the hulls were constructed using a carbon fiber vacuum resin infusion process. Each demi-hull was fabricated using a machine-milled high-density foam mold produced for the team by the Ford Motor Company. Advanced Capabilities Team The Advanced Capabilities Team creates systems for competition elements that involve more than the base boat. Currently, we're tasked with creating a controllable water cannon and a mechanism that efficiently retrieves balls and places them into a storage bin. This involves using Computer-Aided Design (CAD) to physically build these systems, followed by designing the electrical circuits and sensors these components will use to function optimally. Finally, we'll create a communication platform to control these new features effectively. Duck Wash This challenge will consist of three major steps: target recognition, calculating the water trajectory, and actuation. For target recognition, we will use camera-based computer vision system to detect and pinpoint target locations accurately. When it comes to trajectory calculation, we will calculate based on the position and orientation of both our vessel and the target.The actuation step will involve using the data gathered from the previous steps to properly align our water cannon. Once the cannon has been aligned, the water will be launched.To maintain “station keeping”—keeping our vessel still during the water launch—we will use force estimates to apply an opposite force via thrusters. Collection and Delivery The boat will identify the collection zone, which is an octagon floating on the water’s surface. Once the area is detected, the ASV will then proceed to collect designated items—floating rubber ducks and red racquetballs. The detection will be mainly facilitated by our camera-based computer vision system.After successfully collecting the items, the ASV will navigate towards the Delivery Octagon. Here, the vehicle’s primary task is to safely deliver the collected items. Points are awarded for not only delivering the items inside the octagon but also if items are delivered to the correct ‘nests’.Throughout the process, the ASV needs to maintain efficient object handling and accurate navigational capabilities, which will be key to succeeding in this demanding challenge. The design and functionality of our vehicle will rely heavily on optimal strategies and technological advancements. Business The Business Team secures funding for the team, processes reimbursement requests, maintains sponsor relationships and is responsible for social media engagement. The subteam also creates and maintains the team website. Reimbursement Request Tracking We currently have a Google Form that requires separately uploading receipts for member reimbursement requests. While far better than the previous system involving manually notifying a team officer, we hope to introduce a streamlined system that allows receipt and description uploading quickly, which then immediately notifies the relevant person for purchase approval. Social Engagement We hope to expand our social events even further than the previous year. In 2022-2023, we brought team members to a board game night, go-karting and arcade, and many team dinners after even work sessions. We also started a Minecraft server for members to play over the summer. However, we aim to have more frequent events, such as movie nights every other week. UMA Website With an effort to boost recruitment in 2023, several pages of the website have been redesigned to be more informative and appealing. Redesigns of several more pages are planned, and more content will be produced to further recruitment efforts, increase appeal to sponsors, and score more points in the RoboBoat website judging. Members: Ben Schattinger "
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"title" : "Sponsors",
"category" : "",
"tags" : "",
"url" : "/sponsors/",
"date" : "",
"content" : " Sponsors Thank you to our wonderful sponsors for your contributions! Learn more ▼ Platinum Gold Lastly, thank you to our alumni and friends for your donations during Giving Blue Day! If you are interested in sponsoring UM::Autonomy, please email our 2023-24 business leads for more information: President: Asheya Naik | user = 'asheya'; site = 'umich.edu'; document.write(''); document.write(user + '@' + site + ''); Treasurer: Elaina Mann | user = 'elainamn'; site = 'umich.edu'; document.write(''); document.write(user + '@' + site + ''); "
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"title" : "v1.0.3 Short Fuse",
"category" : "",
"tags" : "",
"url" : "/node_modules/fuzzysearch/CHANGELOG/",
"date" : "",
"content" : "v1.0.3 Short Fuse Improved circuit-breaker when needle and haystack length are equalv1.0.2 Vodka Tonic Slightly updated circuit-breaker that tests for equal length first Doubled method performance (see jsperf tests)v1.0.1 Circuit Breaker Introduced a circuit-breaker where queries longer than the searched string will return false Introduced a circuit-breaker where queries identical to the searched string will return true Introduced a circuit-breaker where text containing the entire query will return truev1.0.0 IPO Initial Public Release"
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"title" : "fuzzysearch",
"category" : "",
"tags" : "",
"url" : "/node_modules/fuzzysearch/",
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"content" : "fuzzysearch Tiny and blazing-fast fuzzy search in JavaScriptFuzzy searching allows for flexibly matching a string with partial input, useful for filtering data very quickly based on lightweight user input.DemoTo see fuzzysearch in action, head over to bevacqua.github.io/horsey, which is a demo of an autocomplete component that uses fuzzysearch to filter out results based on user input.InstallFrom npmnpm install --save fuzzysearchfuzzysearch(needle, haystack)Returns true if needle matches haystack using a fuzzy-searching algorithm. Note that this program doesn’t implement levenshtein distance, but rather a simplified version where there’s no approximation. The method will return true only if each character in the needle can be found in the haystack and occurs after the preceding character.fuzzysearch('twl', 'cartwheel') // <- truefuzzysearch('cart', 'cartwheel') // <- truefuzzysearch('cw', 'cartwheel') // <- truefuzzysearch('ee', 'cartwheel') // <- truefuzzysearch('art', 'cartwheel') // <- truefuzzysearch('eeel', 'cartwheel') // <- falsefuzzysearch('dog', 'cartwheel') // <- falseAn exciting application for this kind of algorithm is to filter options from an autocomplete menu, check out horsey for an example on how that might look like.But! RegExps…!LicenseMIT"
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"title" : "Simple-Jekyll-Search",
"category" : "",
"tags" : "",
"url" : "/node_modules/simple-jekyll-search/",
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"content" : "# [Simple-Jekyll-Search](https://www.npmjs.com/package/simple-jekyll-search)[](https://travis-ci.org/christian-fei/Simple-Jekyll-Search)[](https://david-dm.org/christian-fei/Simple-Jekyll-Search)[](https://david-dm.org/christian-fei/Simple-Jekyll-Search?type=dev)A JavaScript library to add search functionality to any Jekyll blog.## Use caseYou have a blog, built with Jekyll, and want a **lightweight search functionality** on your blog, purely client-side?*No server configurations or databases to maintain*.Just **5 minutes** to have a **fully working searchable blog**.---## Installation### npm```shnpm install simple-jekyll-search```## Getting started### Create `search.json`Place the following code in a file called `search.json` in the **root** of your Jekyll blog. (You can also get a copy [from here](/example/search.json))This file will be used as a small data source to perform the searches on the client side:```yaml---layout: none---[ {% for post in site.posts %} { "title" : "{{ post.title | escape }}", "category" : "{{ post.category }}", "tags" : "{{ post.tags | join: ', ' }}", "url" : "{{ site.baseurl }}{{ post.url }}", "date" : "{{ post.date }}" } {% unless forloop.last %},{% endunless %} {% endfor %}]```## Preparing the plugin### Add DOM elementsSimpleJekyllSearch needs two `DOM` elements to work:- a search input field- a result container to display the results#### Give me the codeHere is the code you can use with the default configuration:You need to place the following code within the layout where you want the search to appear. (See the configuration section below to customize it)For example in **_layouts/default.html**:```html```## UsageCustomize SimpleJekyllSearch by passing in your configuration options:```jsvar sjs = SimpleJekyllSearch({ searchInput: document.getElementById('search-input'), resultsContainer: document.getElementById('results-container'), json: '/search.json'})```### returns { search }A new instance of SimpleJekyllSearch returns an object, with the only property `search`.`search` is a function used to simulate a user input and display the matching results. E.g.:```jsvar sjs = SimpleJekyllSearch({ ...options })sjs.search('Hello')```💡 it can be used to filter posts by tags or categories!## OptionsHere is a list of the available options, usage questions, troubleshooting & guides.### searchInput (Element) [required]The input element on which the plugin should listen for keyboard event and trigger the searching and rendering for articles.### resultsContainer (Element) [required]The container element in which the search results should be rendered in. Typically a ``.### json (String|JSON) [required]You can either pass in an URL to the `search.json` file, or the results in form of JSON directly, to save one round trip to get the data.### searchResultTemplate (String) [optional]The template of a single rendered search result.The templating syntax is very simple: You just enclose the properties you want to replace with curly braces.E.g.The template```jsvar sjs = SimpleJekyllSearch({ searchInput: document.getElementById('search-input'), resultsContainer: document.getElementById('results-container'), json: '/search.json', searchResultTemplate: '{title}'})```will render to the following```htmlWelcome to Jekyll!```If the `search.json` contains this data```json[ { "title" : "Welcome to Jekyll!", "category" : "", "tags" : "", "url" : "/jekyll/update/2014/11/01/welcome-to-jekyll.html", "date" : "2014-11-01 21:07:22 +0100" }]```### templateMiddleware (Function) [optional]A function that will be called whenever a match in the template is found.It gets passed the current property name, property value, and the template.If the function returns a non-undefined value, it gets replaced in the template.This can be potentially useful for manipulating URLs etc.Example:```jsSimpleJekyllSearch({ ... templateMiddleware: function(prop, value, template) { if (prop === 'bar') { return value.replace(/^\//, '') } } ...})```See the [tests](https://github.com/christian-fei/Simple-Jekyll-Search/blob/master/tests/Templater.test.js) for an in-depth code example### sortMiddleware (Function) [optional]A function that will be used to sort the filtered results.It can be used for example to group the sections together.Example:```jsSimpleJekyllSearch({ ... sortMiddleware: function(a, b) { var astr = String(a.section) + "-" + String(a.caption); var bstr = String(b.section) + "-" + String(b.caption); return astr.localeCompare(bstr) } ...})```### noResultsText (String) [optional]The HTML that will be shown if the query didn't match anything.### limit (Number) [optional]You can limit the number of posts rendered on the page.### fuzzy (Boolean) [optional]Enable fuzzy search to allow less restrictive matching.### exclude (Array) [optional]Pass in a list of terms you want to exclude (terms will be matched against a regex, so URLs, words are allowed).### success (Function) [optional]A function called once the data has been loaded.### debounceTime (Number) [optional]Limit how many times the search function can be executed over the given time window. This is especially useful to improve the user experience when searching over a large dataset (either with rare terms or because the number of posts to display is large). If no `debounceTime` (milliseconds) is provided a search will be triggered on each keystroke.---## If search isn't working due to invalid JSON- There is a filter plugin in the _plugins folder which should remove most characters that cause invalid JSON. To use it, add the simple_search_filter.rb file to your _plugins folder, and use `remove_chars` as a filter.For example: in search.json, replace```json"content": "{{ page.content | strip_html | strip_newlines }}"```with```json"content": "{{ page.content | strip_html | strip_newlines | remove_chars | escape }}"```If this doesn't work when using Github pages you can try `jsonify` to make sure the content is json compatible:```js"content": {{ page.content | jsonify }}```**Note: you don't need to use quotes `"` in this since `jsonify` automatically inserts them.**## Enabling full-text searchReplace `search.json` with the following code:```yaml---layout: none---[ {% for post in site.posts %} { "title" : "{{ post.title | escape }}", "category" : "{{ post.category }}", "tags" : "{{ post.tags | join: ', ' }}", "url" : "{{ site.baseurl }}{{ post.url }}", "date" : "{{ post.date }}", "content" : "{{ post.content | strip_html | strip_newlines }}" } {% unless forloop.last %},{% endunless %} {% endfor %} , {% for page in site.pages %} { {% if page.title != nil %} "title" : "{{ page.title | escape }}", "category" : "{{ page.category }}", "tags" : "{{ page.tags | join: ', ' }}", "url" : "{{ site.baseurl }}{{ page.url }}", "date" : "{{ page.date }}", "content" : "{{ page.content | strip_html | strip_newlines }}" {% endif %} } {% unless forloop.last %},{% endunless %} {% endfor %}]```## Development- `npm install`- `npm test`#### Acceptance tests```bashcd example; jekyll serve# in another tabnpm run cypress -- run```## ContributorsThanks to all [contributors](https://github.com/christian-fei/Simple-Jekyll-Search/graphs/contributors) over the years! You are the best :)> [@daviddarnes](https://github.com/daviddarnes)[@XhmikosR](https://github.com/XhmikosR)[@PeterDaveHello](https://github.com/PeterDaveHello)[@mikeybeck](https://github.com/mikeybeck)[@egladman](https://github.com/egladman)[@midzer](https://github.com/midzer)[@eduardoboucas](https://github.com/eduardoboucas)[@kremalicious](https://github.com/kremalicious)[@tibotiber](https://github.com/tibotiber)and many others!## Stargazers over time[](https://starchart.cc/christian-fei/Simple-Jekyll-Search)"
} ,
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npm installnpm testcd example; jekyll serve
# in another tab
npm run cypress -- run
Thanks to all contributors over the years! You are the best :)
@daviddarnes @XhmikosR @PeterDaveHello @mikeybeck @egladman @midzer @eduardoboucas @kremalicious @tibotiber and many others!
