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

作者：

Highlights：

• 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 experiments.

摘要

•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 experiments.

论文关键词：Motion planning,Anytime algorithm,Asymptotic optimality,Optimal path planning

论文评审过程：Received 10 May 2019, Revised 14 October 2019, Accepted 7 December 2019, Available online 9 December 2019, Version of Record 20 December 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.113124