A batch informed sampling-based algorithm for fast anytime asymptotically-optimal motion planning in cluttered environments

•Present an anytime asymptotically-optimal motion planning algorithm.•A strategy is proposed that balances the “lazy” and “non-lazy” optimal search.•Analyze the swift convergence and computational complexity for the algorithm.•The proposed algorithm is comprehensively evaluated by rigorous experimen...

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Veröffentlicht in:	Expert systems with applications Jg. 144; S. 113124
Hauptverfasser:	Xu, Jing, Song, Kechen, Dong, Hongwen, Yan, Yunhui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Ltd 15.04.2020 Elsevier BV
Schlagworte:	Algorithms Anytime algorithm Asymptotic optimality Asymptotic properties Cost analysis Expert systems Graph theory Modules Motion planning Motion stability Optimal path planning Robustness (mathematics) Sampling Searching Motion planning Asymptotic optimality Optimal path planning Anytime algorithm
ISSN:	0957-4174, 1873-6793
Online-Zugang:	Volltext
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