GitHub topics: motion-planning-algorithms
zhm-real/MotionPlanning
Motion planning algorithms commonly used on autonomous vehicles. (path planning + path tracking)
Language: Python - Size: 22.8 MB - Last synced at: about 1 month ago - Pushed at: over 1 year ago - Stars: 2,423 - Forks: 601
Abdulrasheed1729/undergrad_proj
A repository for my undergraduate final year project
Language: TeX - Size: 1.59 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 1 - Forks: 0
Tom-Forsyth/LCPMotionPlanner
A task space motion planner with global and local obstacle avoidance for robotic manipulators.
Language: C++ - Size: 12.9 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 15 - Forks: 0
arp95/turtlebot_astar
A-star Algorithm on ROS Turtlebot-2 (Part of ENPM661 - Planning for Autonomous Robots)
Language: Python - Size: 2.66 MB - Last synced at: almost 2 years ago - Pushed at: about 5 years ago - Stars: 1 - Forks: 0
xwnb/motion_planning
motion planning algorithms of robotics
Language: Python - Size: 790 KB - Last synced at: about 2 years ago - Pushed at: over 5 years ago - Stars: 4 - Forks: 2
SHITIANYU-hue/Efficient-motion-planning
To guarantee safe and efficient driving for automated vehicles in complicated traffic conditions, the motion planning module of automated vehicles are expected to generate collision-free driving policies as soon as possible in varying traffic environment. However, there always exist a tradeoff between efficiency and accuracy for the motion planning algorithms. Besides, most motion planning methods cannot find the desired trajectory under extreme scenarios (e.g., lane change in crowded traffic scenarios). This study proposed an efficient motion planning strategy for automated lane change based on Mixed-Integer Quadratic Optimization (MIQP) and Neural Networks. We modeled the lane change task as a mixed-integer quadratic optimization problem with logical constraints, which allows the planning module to generate feasible, safe and comfortable driving actions for lane changing process. Then, a hierarchical machine learning structure that consists of SVM-based classification layer and NN-based action learning layer is established to generate desired driving policies that can make online, fast and generalized motion planning. Our model is validated in crowded lane change scenarios through numerical simulations and results indicate that our model can provide optimal and efficient motion planning for automated vehicles
Language: MATLAB - Size: 27.6 MB - Last synced at: over 2 years ago - Pushed at: over 3 years ago - Stars: 34 - Forks: 11
arp95/path_planning_dijkstra
Using Dijkstra Path Planning Algorithm (Part of ENPM661 - Planning for Autonomous Robots)
Language: Python - Size: 149 KB - Last synced at: over 2 years ago - Pushed at: about 5 years ago - Stars: 1 - Forks: 1
trajkd/Highway-Driving
Highway Driving (project 7 of 9 from Udacity Self-Driving Car Engineer Nanodegree)
Language: C++ - Size: 2.87 MB - Last synced at: over 2 years ago - Pushed at: about 5 years ago - Stars: 2 - Forks: 1
arp95/path_planning_astar
Path Planning using A-Star algorithm
Language: Python - Size: 167 KB - Last synced at: over 2 years ago - Pushed at: about 5 years ago - Stars: 1 - Forks: 0
nicholasRenninger/bugAlgorithms_and_kinematics
Implementation of Bug Algorithms and Basic Forwards / Inverse Kinematics Equations.
Language: Python - Size: 936 KB - Last synced at: over 2 years ago - Pushed at: over 5 years ago - Stars: 2 - Forks: 1
antarikshnarain/Fetch-Robotics-simulation
Repository for Fetch Robotics simulation and POCs
Language: Python - Size: 28.3 KB - Last synced at: over 2 years ago - Pushed at: over 5 years ago - Stars: 0 - Forks: 0
ShivamChourey/Motion_Planning_Algorithms
Matlab Implementation of algorithms : 1. Dynamic programming 2. Dijksrta 3. A* 4. Weighted A*
Language: Matlab - Size: 2.93 KB - Last synced at: over 2 years ago - Pushed at: almost 7 years ago - Stars: 0 - Forks: 0
