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NPField: Neural Potential Field for Obstacle-Aware Local Motion Planning

The preprint of the algorithm is available at https://arxiv.org/abs/2310.16362#

Abstarct

For the problem of local path planning with Model Predictive Control (MPC) algorithm, a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, global path, and robot footprint is suggested. The proposed approach provides path for different robots footprints, without needing detection obstacles stage, and with safe distance from obstacles.

Prerequisites

  • Python3.9 or above, Pytorch, cuda and all other libraries needed for Acados and L4CasADi
  • Install Acados and make sure that it works by testing examples in exampls/acados_python
  • Install L4CasADi

Steps of running the algorithm:

The algorithm is written for two resolutions of maps (2cm , 10cm). For anyone of those maps, the general steps of using this method are:

  • Training the neural model written in file model_nn.py
  • Runing the file create_solver.py will create slover for MPC local planning problem for a differential-drive mobile robot
  • Use test_solver.py for testing the results of algorithm

Demonstration video:

The code is tested on Ubuntu 20.04. Video for testing the proposed algorithm on Unmanned Ground Vehicle Husky with the created solver and ROS is presented here.

About

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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NPField: Neural Potential Field for Obstacle-Aware Local Motion Planning
The preprint of the algorithm is available at
https://arxiv.org/abs/2310.16362#
Abstarct
For the problem of local path planning with Model Predictive Control (MPC) algorithm, a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, global path, and robot footprint is suggested. The proposed approach provides path for different robots footprints, without needing detection obstacles stage, and with safe distance from obstacles.
Prerequisites
  • Python3.9 or above, Pytorch, cuda and all other libraries needed for Acados and L4CasADi
  • Install Acados and make sure that it works by testing examples in exampls/acados_python
  • Install L4CasADi
Steps of running the algorithm:
The algorithm is written for two resolutions of maps (2cm , 10cm). For anyone of those maps, the general steps of using this method are:
  • Training the neural model written in file model_nn.py
  • Runing the file create_solver.py will create slover for MPC local planning problem for a differential-drive mobile robot
  • Use test_solver.py for testing the results of algorithm
Demonstration video:
The code is tested on Ubuntu 20.04. Video for testing the proposed algorithm on Unmanned Ground Vehicle Husky with the created solver and ROS is presented here.
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, 'i');
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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");
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Repository files navigation
NPField: Neural Potential Field for Obstacle-Aware Local Motion Planning
The preprint of the algorithm is available at
https://arxiv.org/abs/2310.16362#
Abstarct
For the problem of local path planning with Model Predictive Control (MPC) algorithm, a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, global path, and robot footprint is suggested. The proposed approach provides path for different robots footprints, without needing detection obstacles stage, and with safe distance from obstacles.
Prerequisites
  • Python3.9 or above, Pytorch, cuda and all other libraries needed for Acados and L4CasADi
  • Install Acados and make sure that it works by testing examples in exampls/acados_python
  • Install L4CasADi
Steps of running the algorithm:
The algorithm is written for two resolutions of maps (2cm , 10cm). For anyone of those maps, the general steps of using this method are:
  • Training the neural model written in file model_nn.py
  • Runing the file create_solver.py will create slover for MPC local planning problem for a differential-drive mobile robot
  • Use test_solver.py for testing the results of algorithm
Demonstration video:
The code is tested on Ubuntu 20.04. Video for testing the proposed algorithm on Unmanned Ground Vehicle Husky with the created solver and ROS is presented here.
About
Neural Potential Field for Obstacle-Aware Local Motion Planning
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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); }
})();
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
NPField: Neural Potential Field for Obstacle-Aware Local Motion Planning
The preprint of the algorithm is available at
https://arxiv.org/abs/2310.16362#
Abstarct
For the problem of local path planning with Model Predictive Control (MPC) algorithm, a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, global path, and robot footprint is suggested. The proposed approach provides path for different robots footprints, without needing detection obstacles stage, and with safe distance from obstacles.
Prerequisites
  • Python3.9 or above, Pytorch, cuda and all other libraries needed for Acados and L4CasADi
  • Install Acados and make sure that it works by testing examples in exampls/acados_python
  • Install L4CasADi
Steps of running the algorithm:
The algorithm is written for two resolutions of maps (2cm , 10cm). For anyone of those maps, the general steps of using this method are:
  • Training the neural model written in file model_nn.py
  • Runing the file create_solver.py will create slover for MPC local planning problem for a differential-drive mobile robot
  • Use test_solver.py for testing the results of algorithm
Demonstration video:
The code is tested on Ubuntu 20.04. Video for testing the proposed algorithm on Unmanned Ground Vehicle Husky with the created solver and ROS is presented here.
About
Neural Potential Field for Obstacle-Aware Local Motion Planning
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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); }
})();
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var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Repository files navigation
NPField: Neural Potential Field for Obstacle-Aware Local Motion Planning
The preprint of the algorithm is available at
https://arxiv.org/abs/2310.16362#
Abstarct
For the problem of local path planning with Model Predictive Control (MPC) algorithm, a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, global path, and robot footprint is suggested. The proposed approach provides path for different robots footprints, without needing detection obstacles stage, and with safe distance from obstacles.
Prerequisites
  • Python3.9 or above, Pytorch, cuda and all other libraries needed for Acados and L4CasADi
  • Install Acados and make sure that it works by testing examples in exampls/acados_python
  • Install L4CasADi
Steps of running the algorithm:
The algorithm is written for two resolutions of maps (2cm , 10cm). For anyone of those maps, the general steps of using this method are:
  • Training the neural model written in file model_nn.py
  • Runing the file create_solver.py will create slover for MPC local planning problem for a differential-drive mobile robot
  • Use test_solver.py for testing the results of algorithm
Demonstration video:
The code is tested on Ubuntu 20.04. Video for testing the proposed algorithm on Unmanned Ground Vehicle Husky with the created solver and ROS is presented here.
About
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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");
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Repository files navigation
NPField: Neural Potential Field for Obstacle-Aware Local Motion Planning
The preprint of the algorithm is available at
https://arxiv.org/abs/2310.16362#
Abstarct
For the problem of local path planning with Model Predictive Control (MPC) algorithm, a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, global path, and robot footprint is suggested. The proposed approach provides path for different robots footprints, without needing detection obstacles stage, and with safe distance from obstacles.
Prerequisites
  • Python3.9 or above, Pytorch, cuda and all other libraries needed for Acados and L4CasADi
  • Install Acados and make sure that it works by testing examples in exampls/acados_python
  • Install L4CasADi
Steps of running the algorithm:
The algorithm is written for two resolutions of maps (2cm , 10cm). For anyone of those maps, the general steps of using this method are:
  • Training the neural model written in file model_nn.py
  • Runing the file create_solver.py will create slover for MPC local planning problem for a differential-drive mobile robot
  • Use test_solver.py for testing the results of algorithm
Demonstration video:
The code is tested on Ubuntu 20.04. Video for testing the proposed algorithm on Unmanned Ground Vehicle Husky with the created solver and ROS is presented here.
About
Neural Potential Field for Obstacle-Aware Local Motion Planning
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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");
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Repository files navigation
NPField: Neural Potential Field for Obstacle-Aware Local Motion Planning
The preprint of the algorithm is available at
https://arxiv.org/abs/2310.16362#
Abstarct
For the problem of local path planning with Model Predictive Control (MPC) algorithm, a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, global path, and robot footprint is suggested. The proposed approach provides path for different robots footprints, without needing detection obstacles stage, and with safe distance from obstacles.
Prerequisites
  • Python3.9 or above, Pytorch, cuda and all other libraries needed for Acados and L4CasADi
  • Install Acados and make sure that it works by testing examples in exampls/acados_python
  • Install L4CasADi
Steps of running the algorithm:
The algorithm is written for two resolutions of maps (2cm , 10cm). For anyone of those maps, the general steps of using this method are:
  • Training the neural model written in file model_nn.py
  • Runing the file create_solver.py will create slover for MPC local planning problem for a differential-drive mobile robot
  • Use test_solver.py for testing the results of algorithm
Demonstration video:
The code is tested on Ubuntu 20.04. Video for testing the proposed algorithm on Unmanned Ground Vehicle Husky with the created solver and ROS is presented here.
About
Neural Potential Field for Obstacle-Aware Local Motion Planning
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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");
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NPField: Neural Potential Field for Obstacle-Aware Local Motion Planning
The preprint of the algorithm is available at
https://arxiv.org/abs/2310.16362#
Abstarct
For the problem of local path planning with Model Predictive Control (MPC) algorithm, a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, global path, and robot footprint is suggested. The proposed approach provides path for different robots footprints, without needing detection obstacles stage, and with safe distance from obstacles.
Prerequisites
  • Python3.9 or above, Pytorch, cuda and all other libraries needed for Acados and L4CasADi
  • Install Acados and make sure that it works by testing examples in exampls/acados_python
  • Install L4CasADi
Steps of running the algorithm:
The algorithm is written for two resolutions of maps (2cm , 10cm). For anyone of those maps, the general steps of using this method are:
  • Training the neural model written in file model_nn.py
  • Runing the file create_solver.py will create slover for MPC local planning problem for a differential-drive mobile robot
  • Use test_solver.py for testing the results of algorithm
Demonstration video:
The code is tested on Ubuntu 20.04. Video for testing the proposed algorithm on Unmanned Ground Vehicle Husky with the created solver and ROS is presented here.
About
Neural Potential Field for Obstacle-Aware Local Motion Planning
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