DocumentCode :
250961
Title :
Asymptotically near-optimal RRT for fast, high-quality, motion planning
Author :
Salzman, Oren ; Halperin, Dan
Author_Institution :
Blavatnik Sch. of Comput. Sci., Tel-Aviv Univ., Tel-Aviv, Israel
fYear :
2014
fDate :
May 31 2014-June 7 2014
Firstpage :
4680
Lastpage :
4685
Abstract :
We present Lower Bound Tree-RRT (LBT-RRT), a single-query sampling-based algorithm that is asymptotically near-optimal. Namely, the solution extracted from LBT-RRT converges to a solution that is within an approximation factor of 1 + ε of the optimal solution. Our algorithm allows for a continuous interpolation between the fast RRT algorithm and the asymptotically optimal RRT* and RRG algorithms. When the approximation factor is 1 (i.e., no approximation is allowed), LBT-RRT behaves like the RRT* algorithm. When the approximation factor is unbounded, LBT-RRT behaves like the RRT algorithm. In between, LBT-RRT is shown to produce paths that have higher quality than RRT would produce and run faster than RRT* would run. This is done by maintaining a tree which is a sub-graph of the RRG roadmap and a second, auxiliary tree, which we call the lower-bound tree. The combination of the two trees, which is faster to maintain than the tree maintained by RRT*, efficiently guarantee asymptotic near-optimality. We suggest to use LBT-RRT for high-quality, anytime motion planning. We demonstrate the performance of the algorithm for scenarios ranging from 3 to 12 degrees of freedom and show that even for small approximation factors, the algorithm produces high-quality solutions (comparable to RRT*) with little runtime overhead when compared to RRT.
Keywords :
interpolation; mobile robots; path planning; trees (mathematics); LBT-RRT; approximation factor; asymptotically near-optimal RRT; interpolation; lower bound tree-RRT; motion planning; robotics; Algorithm design and analysis; Approximation algorithms; Approximation methods; Convergence; Motion-planning; Robots;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation (ICRA), 2014 IEEE International Conference on
Conference_Location :
Hong Kong
Type :
conf
DOI :
10.1109/ICRA.2014.6907543
Filename :
6907543
Link To Document :
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