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SigSent

This project is the Senior Design group 11 for the University of Central Florida College of Electrical and Computer Engineering

Team: Josh Franco John Millner Jeff Strange Richie Wales

Side View

SigSent is an intelligent, multi-terrain hexapod robot built for robust, reliable security solutions. Using an evolved Neural Network using NeuroEvolution of Augementing Topologies (NEAT), SigSent determines when the road is getting rough and automatically transition from a four-legged drive mode into a walking mode using all six of its legs. This allows base operators to work at a higher level, simply providing GPS routes to the robot and leaving it to figure out the rest itself.

SigSent's gait is generated using a Genetic Algorithm to find the most optimal, stable walking motion under its given load.

SigSent features a computer vision module that runs a basic pedestrian detection algorithm so that base operators can be notified if an anomalous figure has appeared that needs addressing.

The base operator can also TeleOp with a provided joystick, listen to audio from the robot's integrated microphone, and also use a headset of their own to output audio from SigSent's own speakers for two-way communication. Diagnostic information regarding things like battery life, servo pose information, and CV/NEAT outputs are provided as well in the GUI application. The interface was created using PyQt5, Python bindings for Qt5.

Top View

This robot runs on a raspberry pi 3 uses the following sensors: Sparkfun Razor IMU 9DoF 14001 Sparkfun Venus638FLPx RasPi Cam Hokuyo UTM-30lx LIDAR

To begin the robot first launch roscore on a basestation then launch local.launch from the Raspberry Pi followed by launching remote.launch from the basestation

ensure that environment variables such as ROS_MASTER_URI and ROS_PI are properly set and that the package is properly sourced on both computers source ~/SigSent/catkin_ws/devel/setup.bash ROS_MASTER_URI=http://192.168.1.110:11311 ROS_IP=192.168.1.110

Sensors can be easily visualized using RVIZ rosrun rviz rviz once launcheed rviz should use the rviz configuration file found in the root directory of this github

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EEL4660 Final Project

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MIT license
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3 watching

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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SigSent
This project is the Senior Design group 11 for the University of Central Florida College of Electrical and Computer Engineering
Team:
Josh Franco
John Millner
Jeff Strange
Richie Wales
Side View
SigSent is an intelligent, multi-terrain hexapod robot built for robust, reliable security solutions. Using an evolved Neural Network using NeuroEvolution of Augementing Topologies (NEAT), SigSent determines when the road is getting rough and automatically transition from a four-legged drive mode into a walking mode using all six of its legs. This allows base operators to work at a higher level, simply providing GPS routes to the robot and leaving it to figure out the rest itself.
SigSent's gait is generated using a Genetic Algorithm to find the most optimal, stable walking motion under its given load.
SigSent features a computer vision module that runs a basic pedestrian detection algorithm so that base operators can be notified if an anomalous figure has appeared that needs addressing.
The base operator can also TeleOp with a provided joystick, listen to audio from the robot's integrated microphone, and also use a headset of their own to output audio from SigSent's own speakers for two-way communication. Diagnostic information regarding things like battery life, servo pose information, and CV/NEAT outputs are provided as well in the GUI application. The interface was created using PyQt5, Python bindings for Qt5.
Top View
This robot runs on a raspberry pi 3 uses the following sensors:
Sparkfun Razor IMU 9DoF 14001
Sparkfun Venus638FLPx
RasPi Cam
Hokuyo UTM-30lx LIDAR
To begin the robot first launch roscore on a basestation
then launch local.launch from the Raspberry Pi
followed by launching remote.launch from the basestation
ensure that environment variables such as ROS_MASTER_URI and ROS_PI are properly set and that the package is properly sourced on both computers
source ~/SigSent/catkin_ws/devel/setup.bash
ROS_MASTER_URI=http://192.168.1.110:11311
ROS_IP=192.168.1.110
Sensors can be easily visualized using RVIZ
rosrun rviz rviz
once launcheed rviz should use the rviz configuration file found in the root directory of this github
About
EEL4660 Final Project
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
3 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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This repository was archived by the owner on Jul 11, 2020. It is now read-only.

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SigSent
This project is the Senior Design group 11 for the University of Central Florida College of Electrical and Computer Engineering
Team:
Josh Franco
John Millner
Jeff Strange
Richie Wales
Side View
SigSent is an intelligent, multi-terrain hexapod robot built for robust, reliable security solutions. Using an evolved Neural Network using NeuroEvolution of Augementing Topologies (NEAT), SigSent determines when the road is getting rough and automatically transition from a four-legged drive mode into a walking mode using all six of its legs. This allows base operators to work at a higher level, simply providing GPS routes to the robot and leaving it to figure out the rest itself.
SigSent's gait is generated using a Genetic Algorithm to find the most optimal, stable walking motion under its given load.
SigSent features a computer vision module that runs a basic pedestrian detection algorithm so that base operators can be notified if an anomalous figure has appeared that needs addressing.
The base operator can also TeleOp with a provided joystick, listen to audio from the robot's integrated microphone, and also use a headset of their own to output audio from SigSent's own speakers for two-way communication. Diagnostic information regarding things like battery life, servo pose information, and CV/NEAT outputs are provided as well in the GUI application. The interface was created using PyQt5, Python bindings for Qt5.
Top View
This robot runs on a raspberry pi 3 uses the following sensors:
Sparkfun Razor IMU 9DoF 14001
Sparkfun Venus638FLPx
RasPi Cam
Hokuyo UTM-30lx LIDAR
To begin the robot first launch roscore on a basestation
then launch local.launch from the Raspberry Pi
followed by launching remote.launch from the basestation
ensure that environment variables such as ROS_MASTER_URI and ROS_PI are properly set and that the package is properly sourced on both computers
source ~/SigSent/catkin_ws/devel/setup.bash
ROS_MASTER_URI=http://192.168.1.110:11311
ROS_IP=192.168.1.110
Sensors can be easily visualized using RVIZ
rosrun rviz rviz
once launcheed rviz should use the rviz configuration file found in the root directory of this github
About
EEL4660 Final Project
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
3 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

This repository was archived by the owner on Jul 11, 2020. It is now read-only.

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SigSent
This project is the Senior Design group 11 for the University of Central Florida College of Electrical and Computer Engineering
Team:
Josh Franco
John Millner
Jeff Strange
Richie Wales
Side View
SigSent is an intelligent, multi-terrain hexapod robot built for robust, reliable security solutions. Using an evolved Neural Network using NeuroEvolution of Augementing Topologies (NEAT), SigSent determines when the road is getting rough and automatically transition from a four-legged drive mode into a walking mode using all six of its legs. This allows base operators to work at a higher level, simply providing GPS routes to the robot and leaving it to figure out the rest itself.
SigSent's gait is generated using a Genetic Algorithm to find the most optimal, stable walking motion under its given load.
SigSent features a computer vision module that runs a basic pedestrian detection algorithm so that base operators can be notified if an anomalous figure has appeared that needs addressing.
The base operator can also TeleOp with a provided joystick, listen to audio from the robot's integrated microphone, and also use a headset of their own to output audio from SigSent's own speakers for two-way communication. Diagnostic information regarding things like battery life, servo pose information, and CV/NEAT outputs are provided as well in the GUI application. The interface was created using PyQt5, Python bindings for Qt5.
Top View
This robot runs on a raspberry pi 3 uses the following sensors:
Sparkfun Razor IMU 9DoF 14001
Sparkfun Venus638FLPx
RasPi Cam
Hokuyo UTM-30lx LIDAR
To begin the robot first launch roscore on a basestation
then launch local.launch from the Raspberry Pi
followed by launching remote.launch from the basestation
ensure that environment variables such as ROS_MASTER_URI and ROS_PI are properly set and that the package is properly sourced on both computers
source ~/SigSent/catkin_ws/devel/setup.bash
ROS_MASTER_URI=http://192.168.1.110:11311
ROS_IP=192.168.1.110
Sensors can be easily visualized using RVIZ
rosrun rviz rviz
once launcheed rviz should use the rviz configuration file found in the root directory of this github
About
EEL4660 Final Project
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
3 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content

This repository was archived by the owner on Jul 11, 2020. It is now read-only.

BranchesTags
Repository files navigation
SigSent
This project is the Senior Design group 11 for the University of Central Florida College of Electrical and Computer Engineering
Team:
Josh Franco
John Millner
Jeff Strange
Richie Wales
Side View
SigSent is an intelligent, multi-terrain hexapod robot built for robust, reliable security solutions. Using an evolved Neural Network using NeuroEvolution of Augementing Topologies (NEAT), SigSent determines when the road is getting rough and automatically transition from a four-legged drive mode into a walking mode using all six of its legs. This allows base operators to work at a higher level, simply providing GPS routes to the robot and leaving it to figure out the rest itself.
SigSent's gait is generated using a Genetic Algorithm to find the most optimal, stable walking motion under its given load.
SigSent features a computer vision module that runs a basic pedestrian detection algorithm so that base operators can be notified if an anomalous figure has appeared that needs addressing.
The base operator can also TeleOp with a provided joystick, listen to audio from the robot's integrated microphone, and also use a headset of their own to output audio from SigSent's own speakers for two-way communication. Diagnostic information regarding things like battery life, servo pose information, and CV/NEAT outputs are provided as well in the GUI application. The interface was created using PyQt5, Python bindings for Qt5.
Top View
This robot runs on a raspberry pi 3 uses the following sensors:
Sparkfun Razor IMU 9DoF 14001
Sparkfun Venus638FLPx
RasPi Cam
Hokuyo UTM-30lx LIDAR
To begin the robot first launch roscore on a basestation
then launch local.launch from the Raspberry Pi
followed by launching remote.launch from the basestation
ensure that environment variables such as ROS_MASTER_URI and ROS_PI are properly set and that the package is properly sourced on both computers
source ~/SigSent/catkin_ws/devel/setup.bash
ROS_MASTER_URI=http://192.168.1.110:11311
ROS_IP=192.168.1.110
Sensors can be easily visualized using RVIZ
rosrun rviz rviz
once launcheed rviz should use the rviz configuration file found in the root directory of this github
About
EEL4660 Final Project
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
3 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

This repository was archived by the owner on Jul 11, 2020. It is now read-only.

BranchesTags
Repository files navigation
SigSent
This project is the Senior Design group 11 for the University of Central Florida College of Electrical and Computer Engineering
Team:
Josh Franco
John Millner
Jeff Strange
Richie Wales
Side View
SigSent is an intelligent, multi-terrain hexapod robot built for robust, reliable security solutions. Using an evolved Neural Network using NeuroEvolution of Augementing Topologies (NEAT), SigSent determines when the road is getting rough and automatically transition from a four-legged drive mode into a walking mode using all six of its legs. This allows base operators to work at a higher level, simply providing GPS routes to the robot and leaving it to figure out the rest itself.
SigSent's gait is generated using a Genetic Algorithm to find the most optimal, stable walking motion under its given load.
SigSent features a computer vision module that runs a basic pedestrian detection algorithm so that base operators can be notified if an anomalous figure has appeared that needs addressing.
The base operator can also TeleOp with a provided joystick, listen to audio from the robot's integrated microphone, and also use a headset of their own to output audio from SigSent's own speakers for two-way communication. Diagnostic information regarding things like battery life, servo pose information, and CV/NEAT outputs are provided as well in the GUI application. The interface was created using PyQt5, Python bindings for Qt5.
Top View
This robot runs on a raspberry pi 3 uses the following sensors:
Sparkfun Razor IMU 9DoF 14001
Sparkfun Venus638FLPx
RasPi Cam
Hokuyo UTM-30lx LIDAR
To begin the robot first launch roscore on a basestation
then launch local.launch from the Raspberry Pi
followed by launching remote.launch from the basestation
ensure that environment variables such as ROS_MASTER_URI and ROS_PI are properly set and that the package is properly sourced on both computers
source ~/SigSent/catkin_ws/devel/setup.bash
ROS_MASTER_URI=http://192.168.1.110:11311
ROS_IP=192.168.1.110
Sensors can be easily visualized using RVIZ
rosrun rviz rviz
once launcheed rviz should use the rviz configuration file found in the root directory of this github
About
EEL4660 Final Project
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
3 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

This repository was archived by the owner on Jul 11, 2020. It is now read-only.

BranchesTags
Repository files navigation
SigSent
This project is the Senior Design group 11 for the University of Central Florida College of Electrical and Computer Engineering
Team:
Josh Franco
John Millner
Jeff Strange
Richie Wales
Side View
SigSent is an intelligent, multi-terrain hexapod robot built for robust, reliable security solutions. Using an evolved Neural Network using NeuroEvolution of Augementing Topologies (NEAT), SigSent determines when the road is getting rough and automatically transition from a four-legged drive mode into a walking mode using all six of its legs. This allows base operators to work at a higher level, simply providing GPS routes to the robot and leaving it to figure out the rest itself.
SigSent's gait is generated using a Genetic Algorithm to find the most optimal, stable walking motion under its given load.
SigSent features a computer vision module that runs a basic pedestrian detection algorithm so that base operators can be notified if an anomalous figure has appeared that needs addressing.
The base operator can also TeleOp with a provided joystick, listen to audio from the robot's integrated microphone, and also use a headset of their own to output audio from SigSent's own speakers for two-way communication. Diagnostic information regarding things like battery life, servo pose information, and CV/NEAT outputs are provided as well in the GUI application. The interface was created using PyQt5, Python bindings for Qt5.
Top View
This robot runs on a raspberry pi 3 uses the following sensors:
Sparkfun Razor IMU 9DoF 14001
Sparkfun Venus638FLPx
RasPi Cam
Hokuyo UTM-30lx LIDAR
To begin the robot first launch roscore on a basestation
then launch local.launch from the Raspberry Pi
followed by launching remote.launch from the basestation
ensure that environment variables such as ROS_MASTER_URI and ROS_PI are properly set and that the package is properly sourced on both computers
source ~/SigSent/catkin_ws/devel/setup.bash
ROS_MASTER_URI=http://192.168.1.110:11311
ROS_IP=192.168.1.110
Sensors can be easily visualized using RVIZ
rosrun rviz rviz
once launcheed rviz should use the rviz configuration file found in the root directory of this github
About
EEL4660 Final Project
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
3 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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This repository was archived by the owner on Jul 11, 2020. It is now read-only.

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SigSent
This project is the Senior Design group 11 for the University of Central Florida College of Electrical and Computer Engineering
Team:
Josh Franco
John Millner
Jeff Strange
Richie Wales
Side View
SigSent is an intelligent, multi-terrain hexapod robot built for robust, reliable security solutions. Using an evolved Neural Network using NeuroEvolution of Augementing Topologies (NEAT), SigSent determines when the road is getting rough and automatically transition from a four-legged drive mode into a walking mode using all six of its legs. This allows base operators to work at a higher level, simply providing GPS routes to the robot and leaving it to figure out the rest itself.
SigSent's gait is generated using a Genetic Algorithm to find the most optimal, stable walking motion under its given load.
SigSent features a computer vision module that runs a basic pedestrian detection algorithm so that base operators can be notified if an anomalous figure has appeared that needs addressing.
The base operator can also TeleOp with a provided joystick, listen to audio from the robot's integrated microphone, and also use a headset of their own to output audio from SigSent's own speakers for two-way communication. Diagnostic information regarding things like battery life, servo pose information, and CV/NEAT outputs are provided as well in the GUI application. The interface was created using PyQt5, Python bindings for Qt5.
Top View
This robot runs on a raspberry pi 3 uses the following sensors:
Sparkfun Razor IMU 9DoF 14001
Sparkfun Venus638FLPx
RasPi Cam
Hokuyo UTM-30lx LIDAR
To begin the robot first launch roscore on a basestation
then launch local.launch from the Raspberry Pi
followed by launching remote.launch from the basestation
ensure that environment variables such as ROS_MASTER_URI and ROS_PI are properly set and that the package is properly sourced on both computers
source ~/SigSent/catkin_ws/devel/setup.bash
ROS_MASTER_URI=http://192.168.1.110:11311
ROS_IP=192.168.1.110
Sensors can be easily visualized using RVIZ
rosrun rviz rviz
once launcheed rviz should use the rviz configuration file found in the root directory of this github
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EEL4660 Final Project
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