Asymptotically Optimal Sampling-based Planners
This is an incremental review article that synthesizes existing knowledge on asymptotically optimal planners for robotics researchers.
The paper tackles the problem of robot motion planning by analyzing sampling-based planners that guarantee convergence to optimal path cost as sample count increases, providing a comprehensive review of their theoretical properties, performance, and applications.
An asymptotically optimal sampling-based planner employs sampling to solve robot motion planning problems and returns paths with a cost that converges to the optimal solution cost, as the number of samples approaches infinity. This comprehensive article covers the theoretical characteristics of asymptotic optimality of motion planning algorithms, and traces its origins, analysis models, practical performance, extensions, and applications.