awesome-motion-planning
Motion planning resources
A curated list of resources and tools for developing motion planning algorithms in robotics and autonomous systems.
A curated list of Resources for Motion Planning
207 stars
48 watching
54 forks
last commit: over 2 years agoLinked from 1 awesome list
Awesome Motion Planning / Blogs and Tutorials | |||
| Introduction to A-star | |||
| Toward More Realistic Pathfinding | |||
| Overview of Motion Planning | |||
| A* Path Finding for Beginners | By Patrick Lester | ||
| Hybrid A* Implementation | |||
| Dubins Path | |||
Awesome Motion Planning / Books | |||
| Planning Algorithms | By Steven M. LaValle | ||
| Robot Motion Planning | By Jean-Claude Latombe | ||
| Autonomus Robots: Modeling, Path Planning, and Control | by Farbod Fahimi | ||
| Principles of Robot Motion | By Howie Choset, Kevin M. Lynch, Seth Hutchinson, George A. Kantor, Wolfram Burgard, Lydia E. Kavraki and Sebastian Thrun | ||
Awesome Motion Planning / Papers | |||
| Randomized Kinodynamic Planning | by Steven M. LaValle and James J. Kuffner, | ||
| Limited-Damage A*: A path search algorithm that considers damage as a feasibility criterion | by Serhat Bayili, Faruk Polat | ||
| Real Time Continuous Curvature Path Planner for an Autonomous Vehicle in an Urban Environment | by David Knowles | ||
| An Evolutionary Artificial Potential Field Algorithm for Dynamic Path Planning of Mobile Robot | by Cao Qixin, Huang Yanwen, Zhou Jingliang | ||
| Planning continuous-curvature paths for car-like robots | by Scheuer A, Fraichard T | ||
| Optimal and Efficient Path Planning for Partially-Known Environments | by Anthony Stentz | ||
| An Introduction to the Conjugate Gradient Method Without the Agonizing Pain | by Jonathan Richard Shewchuk | ||
| Practical search techniques in path planning for autonomous driving | |||
| Junior The Stanford entry in the urban challenge | |||
Awesome Motion Planning / Papers / Heuristic Search | |||
| A Formal Basis for the Heuristic Determination of Minimum Cost Paths | The original A* paper. Introduces the ideas of consistency and admissibility. Also has proofs for the optimality of A* | ||
| On the complexity of Admissible Search Algorithms | A* has worst-case performance with an admisible by inconsistent heuristic. This algorithm deals with such heuristics and improves the worst-case performance | ||
| A Heuristic Search Algorithm with Modifiable Estimate | Most algorithms derived from A* consider the heuristic cost h(s) to be a constant. This is the first algorithm that treats the heuristic cost as a variable and improves it during search whenever possible. The paper also has an influential proof of a result that says that no overall optimal algorithm exits if the cost of an algorithm is measured by the total number of node expansions | ||
Awesome Motion Planning / Lecture Notes | |||
| Robot Motion Planning Lectures | By Howie Choset | ||
| Planning and Decision Making in Robotics | By Maxim Likhachev | ||
Awesome Motion Planning / Software Packages and Libraries | |||
| OMPL | : Sampling based planning | ||
| SBPL | 324 | over 5 years ago | : Heuristic search based planning |
| SMPL | 40 | over 3 years ago | : Heuristic search based planning for manipulators |
jnv/lists