awesome-motion-planning
by AGV-IIT-KGP
A curated list of Resources for Motion Planning
AI summary
Motion planning resources
A curated list of resources and tools for developing motion planning algorithms in robotics and autonomous systems.
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What's in the list
27 links in 6 sections, with live GitHub stats.activeno commit in 2y
Blogs and Tutorials
- A* Path Finding for Beginners
By Patrick Lester
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
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
- 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
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
Lecture Notes
- Robot Motion Planning Lectures
By Howie Choset
- Planning and Decision Making in Robotics
By Maxim Likhachev
Software Packages and Libraries
SBPL
SMPLNothing in this list matches your filter.
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