Title :
Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic
Author :
Gammell, Jonathan D. ; Srinivasa, Siddhartha S. ; Barfoot, Timothy D.
Author_Institution :
Autonomous Space Robot. Lab., Univ. of Toronto, Toronto, ON, Canada
Abstract :
Rapidly-exploring random trees (RRTs) are popular in motion planning because they find solutions efficiently to single-query problems. Optimal RRTs (RRT*s) extend RRTs to the problem of finding the optimal solution, but in doing so asymptotically find the optimal path from the initial state to every state in the planning domain. This behaviour is not only inefficient but also inconsistent with their single-query nature.
Keywords :
optimal control; path planning; admissible ellipsoidal heuristic; direct sampling; informed RRT; motion planning; optimal path; optimal sampling; path planning; rapidly exploring random trees; single query nature; single query problems; Convergence; Heuristic algorithms; Matrix decomposition; Planning; Probabilistic logic; Search problems; Smoothing methods;
Conference_Titel :
Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on
Conference_Location :
Chicago, IL
DOI :
10.1109/IROS.2014.6942976
