An attempt to collect a curated list of awesome learning resources, research papers, tools and other resources related to Motion Planning. This is inpired from several such lists in programming community.
If you want to contribute to this list (please do), send a pull request, open an issue When sending suggestions please add a short blurb/description about each book that you have personally read/benefited from. Feel free to debate quality, headings, etc. irrespective of any particular field or subject area.
All the resources need not be freely available for download.
- Introduction to A-star
- Toward More Realistic Pathfinding
- Overview of Motion Planning
- A* Path Finding for Beginners By Patrick Lester
- 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
- 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
- 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.
- Robot Motion Planning Lectures By Howie Choset.
- Planning and Decision Making in Robotics By Maxim Likhachev.
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