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AutonomousSystem_Project2 @ University of California, Irvine

This project has two parts:

  • Part 1 : Mapping using Hector SLAM - manual control
    • In this problem, I'm going to use TurtleBot3 simulator to build a map for the environment. I manually move the robot until the robot build the map. First, install theHector SLAM package as follows:
    sudo apt-get install ros-melodic-hector-mapping
    
    • Now, in three different terminals, launch the following launch files:
    roslaunch turtlebot3_gazebo turtlebot3_stage_4.launch
    roslaunch turtlebot3_slam turtlebot3_slam.launchslam_methods:=hector
    roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch
    
    • Using the keyboard to move the robot around the environment. Observe the map that is beingupdated by the Hector SLAM node. Continue to drive the robot manually until the whole map isbuilt. Once the map is ready, save the map using the map_server node as follows:
    rosrun map_server map_saver -f ~/map
    
  • Part 2 : Automatic mapping using Hector SLAM - automatic control
    • In this part, I write a node called build_map_automatic that drivesthe robot in the environment to build its map. The node will subscribe to the following topics:
      • /scan : this topic contains the information from the LiDAR scanner
      • /slam_out_pose : this topic contains the “estimated” pose of the robot from thelocalization algorithm
      • /map : this topic contains the occupancy map estimated by the SLAM algorithm
    • This node should then publish to the topic:
      • /cmd_vel : this topic is used to control the linear and the angular velocities of the robot.
    • This node should perform the following steps:
      1. Read the current occupancy map. Determine if more areas of the map need to beexplored.
      2. Pick a point that is on the edge of what is currently explored. Move the robot to that point. Make sure the robot does not hit an obstacle while it moves to that point (you will need the LiDAR information to correct the course of the robot to avoid hitting the obstacles).
      3. Repeat steps 1 and 2 until the whole map is obtained. Save the map and printout thetime used to build the map.

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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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AutonomousSystem_Project2 @ University of California, Irvine

This project has two parts:

  • Part 1 : Mapping using Hector SLAM - manual control
    • In this problem, I'm going to use TurtleBot3 simulator to build a map for the environment. I manually move the robot until the robot build the map. First, install theHector SLAM package as follows:
    sudo apt-get install ros-melodic-hector-mapping
    
    • Now, in three different terminals, launch the following launch files:
    roslaunch turtlebot3_gazebo turtlebot3_stage_4.launch
    roslaunch turtlebot3_slam turtlebot3_slam.launchslam_methods:=hector
    roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch
    
    • Using the keyboard to move the robot around the environment. Observe the map that is beingupdated by the Hector SLAM node. Continue to drive the robot manually until the whole map isbuilt. Once the map is ready, save the map using the map_server node as follows:
    rosrun map_server map_saver -f ~/map
    
  • Part 2 : Automatic mapping using Hector SLAM - automatic control
    • In this part, I write a node called build_map_automatic that drivesthe robot in the environment to build its map. The node will subscribe to the following topics:
      • /scan : this topic contains the information from the LiDAR scanner
      • /slam_out_pose : this topic contains the “estimated” pose of the robot from thelocalization algorithm
      • /map : this topic contains the occupancy map estimated by the SLAM algorithm
    • This node should then publish to the topic:
      • /cmd_vel : this topic is used to control the linear and the angular velocities of the robot.
    • This node should perform the following steps:
      1. Read the current occupancy map. Determine if more areas of the map need to beexplored.
      2. Pick a point that is on the edge of what is currently explored. Move the robot to that point. Make sure the robot does not hit an obstacle while it moves to that point (you will need the LiDAR information to correct the course of the robot to avoid hitting the obstacles).
      3. Repeat steps 1 and 2 until the whole map is obtained. Save the map and printout thetime used to build the map.

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University of California, Irvine

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, '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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AutonomousSystem_Project2 @ University of California, Irvine

This project has two parts:

  • Part 1 : Mapping using Hector SLAM - manual control
    • In this problem, I'm going to use TurtleBot3 simulator to build a map for the environment. I manually move the robot until the robot build the map. First, install theHector SLAM package as follows:
    sudo apt-get install ros-melodic-hector-mapping
    
    • Now, in three different terminals, launch the following launch files:
    roslaunch turtlebot3_gazebo turtlebot3_stage_4.launch
    roslaunch turtlebot3_slam turtlebot3_slam.launchslam_methods:=hector
    roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch
    
    • Using the keyboard to move the robot around the environment. Observe the map that is beingupdated by the Hector SLAM node. Continue to drive the robot manually until the whole map isbuilt. Once the map is ready, save the map using the map_server node as follows:
    rosrun map_server map_saver -f ~/map
    
  • Part 2 : Automatic mapping using Hector SLAM - automatic control
    • In this part, I write a node called build_map_automatic that drivesthe robot in the environment to build its map. The node will subscribe to the following topics:
      • /scan : this topic contains the information from the LiDAR scanner
      • /slam_out_pose : this topic contains the “estimated” pose of the robot from thelocalization algorithm
      • /map : this topic contains the occupancy map estimated by the SLAM algorithm
    • This node should then publish to the topic:
      • /cmd_vel : this topic is used to control the linear and the angular velocities of the robot.
    • This node should perform the following steps:
      1. Read the current occupancy map. Determine if more areas of the map need to beexplored.
      2. Pick a point that is on the edge of what is currently explored. Move the robot to that point. Make sure the robot does not hit an obstacle while it moves to that point (you will need the LiDAR information to correct the course of the robot to avoid hitting the obstacles).
      3. Repeat steps 1 and 2 until the whole map is obtained. Save the map and printout thetime used to build the map.

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University of California, Irvine

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1 watching

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, '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('^' + ".*" + '
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AutonomousSystem_Project2 @ University of California, Irvine

This project has two parts:

  • Part 1 : Mapping using Hector SLAM - manual control
    • In this problem, I'm going to use TurtleBot3 simulator to build a map for the environment. I manually move the robot until the robot build the map. First, install theHector SLAM package as follows:
    sudo apt-get install ros-melodic-hector-mapping
    
    • Now, in three different terminals, launch the following launch files:
    roslaunch turtlebot3_gazebo turtlebot3_stage_4.launch
    roslaunch turtlebot3_slam turtlebot3_slam.launchslam_methods:=hector
    roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch
    
    • Using the keyboard to move the robot around the environment. Observe the map that is beingupdated by the Hector SLAM node. Continue to drive the robot manually until the whole map isbuilt. Once the map is ready, save the map using the map_server node as follows:
    rosrun map_server map_saver -f ~/map
    
  • Part 2 : Automatic mapping using Hector SLAM - automatic control
    • In this part, I write a node called build_map_automatic that drivesthe robot in the environment to build its map. The node will subscribe to the following topics:
      • /scan : this topic contains the information from the LiDAR scanner
      • /slam_out_pose : this topic contains the “estimated” pose of the robot from thelocalization algorithm
      • /map : this topic contains the occupancy map estimated by the SLAM algorithm
    • This node should then publish to the topic:
      • /cmd_vel : this topic is used to control the linear and the angular velocities of the robot.
    • This node should perform the following steps:
      1. Read the current occupancy map. Determine if more areas of the map need to beexplored.
      2. Pick a point that is on the edge of what is currently explored. Move the robot to that point. Make sure the robot does not hit an obstacle while it moves to that point (you will need the LiDAR information to correct the course of the robot to avoid hitting the obstacles).
      3. Repeat steps 1 and 2 until the whole map is obtained. Save the map and printout thetime used to build the map.

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University of California, Irvine

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, '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" + '
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AutonomousSystem_Project2 @ University of California, Irvine

This project has two parts:

  • Part 1 : Mapping using Hector SLAM - manual control
    • In this problem, I'm going to use TurtleBot3 simulator to build a map for the environment. I manually move the robot until the robot build the map. First, install theHector SLAM package as follows:
    sudo apt-get install ros-melodic-hector-mapping
    
    • Now, in three different terminals, launch the following launch files:
    roslaunch turtlebot3_gazebo turtlebot3_stage_4.launch
    roslaunch turtlebot3_slam turtlebot3_slam.launchslam_methods:=hector
    roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch
    
    • Using the keyboard to move the robot around the environment. Observe the map that is beingupdated by the Hector SLAM node. Continue to drive the robot manually until the whole map isbuilt. Once the map is ready, save the map using the map_server node as follows:
    rosrun map_server map_saver -f ~/map
    
  • Part 2 : Automatic mapping using Hector SLAM - automatic control
    • In this part, I write a node called build_map_automatic that drivesthe robot in the environment to build its map. The node will subscribe to the following topics:
      • /scan : this topic contains the information from the LiDAR scanner
      • /slam_out_pose : this topic contains the “estimated” pose of the robot from thelocalization algorithm
      • /map : this topic contains the occupancy map estimated by the SLAM algorithm
    • This node should then publish to the topic:
      • /cmd_vel : this topic is used to control the linear and the angular velocities of the robot.
    • This node should perform the following steps:
      1. Read the current occupancy map. Determine if more areas of the map need to beexplored.
      2. Pick a point that is on the edge of what is currently explored. Move the robot to that point. Make sure the robot does not hit an obstacle while it moves to that point (you will need the LiDAR information to correct the course of the robot to avoid hitting the obstacles).
      3. Repeat steps 1 and 2 until the whole map is obtained. Save the map and printout thetime used to build the map.

About

University of California, Irvine

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0 stars

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1 watching

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, '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('^' + ".*" + '
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AutonomousSystem_Project2 @ University of California, Irvine

This project has two parts:

  • Part 1 : Mapping using Hector SLAM - manual control
    • In this problem, I'm going to use TurtleBot3 simulator to build a map for the environment. I manually move the robot until the robot build the map. First, install theHector SLAM package as follows:
    sudo apt-get install ros-melodic-hector-mapping
    
    • Now, in three different terminals, launch the following launch files:
    roslaunch turtlebot3_gazebo turtlebot3_stage_4.launch
    roslaunch turtlebot3_slam turtlebot3_slam.launchslam_methods:=hector
    roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch
    
    • Using the keyboard to move the robot around the environment. Observe the map that is beingupdated by the Hector SLAM node. Continue to drive the robot manually until the whole map isbuilt. Once the map is ready, save the map using the map_server node as follows:
    rosrun map_server map_saver -f ~/map
    
  • Part 2 : Automatic mapping using Hector SLAM - automatic control
    • In this part, I write a node called build_map_automatic that drivesthe robot in the environment to build its map. The node will subscribe to the following topics:
      • /scan : this topic contains the information from the LiDAR scanner
      • /slam_out_pose : this topic contains the “estimated” pose of the robot from thelocalization algorithm
      • /map : this topic contains the occupancy map estimated by the SLAM algorithm
    • This node should then publish to the topic:
      • /cmd_vel : this topic is used to control the linear and the angular velocities of the robot.
    • This node should perform the following steps:
      1. Read the current occupancy map. Determine if more areas of the map need to beexplored.
      2. Pick a point that is on the edge of what is currently explored. Move the robot to that point. Make sure the robot does not hit an obstacle while it moves to that point (you will need the LiDAR information to correct the course of the robot to avoid hitting the obstacles).
      3. Repeat steps 1 and 2 until the whole map is obtained. Save the map and printout thetime used to build the map.

About

University of California, Irvine

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1 watching

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, '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('^' + ".*" + '
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AutonomousSystem_Project2 @ University of California, Irvine

This project has two parts:

  • Part 1 : Mapping using Hector SLAM - manual control
    • In this problem, I'm going to use TurtleBot3 simulator to build a map for the environment. I manually move the robot until the robot build the map. First, install theHector SLAM package as follows:
    sudo apt-get install ros-melodic-hector-mapping
    
    • Now, in three different terminals, launch the following launch files:
    roslaunch turtlebot3_gazebo turtlebot3_stage_4.launch
    roslaunch turtlebot3_slam turtlebot3_slam.launchslam_methods:=hector
    roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch
    
    • Using the keyboard to move the robot around the environment. Observe the map that is beingupdated by the Hector SLAM node. Continue to drive the robot manually until the whole map isbuilt. Once the map is ready, save the map using the map_server node as follows:
    rosrun map_server map_saver -f ~/map
    
  • Part 2 : Automatic mapping using Hector SLAM - automatic control
    • In this part, I write a node called build_map_automatic that drivesthe robot in the environment to build its map. The node will subscribe to the following topics:
      • /scan : this topic contains the information from the LiDAR scanner
      • /slam_out_pose : this topic contains the “estimated” pose of the robot from thelocalization algorithm
      • /map : this topic contains the occupancy map estimated by the SLAM algorithm
    • This node should then publish to the topic:
      • /cmd_vel : this topic is used to control the linear and the angular velocities of the robot.
    • This node should perform the following steps:
      1. Read the current occupancy map. Determine if more areas of the map need to beexplored.
      2. Pick a point that is on the edge of what is currently explored. Move the robot to that point. Make sure the robot does not hit an obstacle while it moves to that point (you will need the LiDAR information to correct the course of the robot to avoid hitting the obstacles).
      3. Repeat steps 1 and 2 until the whole map is obtained. Save the map and printout thetime used to build the map.

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University of California, Irvine

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1 watching

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, '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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AutonomousSystem_Project2 @ University of California, Irvine

This project has two parts:

  • Part 1 : Mapping using Hector SLAM - manual control
    • In this problem, I'm going to use TurtleBot3 simulator to build a map for the environment. I manually move the robot until the robot build the map. First, install theHector SLAM package as follows:
    sudo apt-get install ros-melodic-hector-mapping
    
    • Now, in three different terminals, launch the following launch files:
    roslaunch turtlebot3_gazebo turtlebot3_stage_4.launch
    roslaunch turtlebot3_slam turtlebot3_slam.launchslam_methods:=hector
    roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch
    
    • Using the keyboard to move the robot around the environment. Observe the map that is beingupdated by the Hector SLAM node. Continue to drive the robot manually until the whole map isbuilt. Once the map is ready, save the map using the map_server node as follows:
    rosrun map_server map_saver -f ~/map
    
  • Part 2 : Automatic mapping using Hector SLAM - automatic control
    • In this part, I write a node called build_map_automatic that drivesthe robot in the environment to build its map. The node will subscribe to the following topics:
      • /scan : this topic contains the information from the LiDAR scanner
      • /slam_out_pose : this topic contains the “estimated” pose of the robot from thelocalization algorithm
      • /map : this topic contains the occupancy map estimated by the SLAM algorithm
    • This node should then publish to the topic:
      • /cmd_vel : this topic is used to control the linear and the angular velocities of the robot.
    • This node should perform the following steps:
      1. Read the current occupancy map. Determine if more areas of the map need to beexplored.
      2. Pick a point that is on the edge of what is currently explored. Move the robot to that point. Make sure the robot does not hit an obstacle while it moves to that point (you will need the LiDAR information to correct the course of the robot to avoid hitting the obstacles).
      3. Repeat steps 1 and 2 until the whole map is obtained. Save the map and printout thetime used to build the map.

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