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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Vydané v:Expert systems with applications Ročník 144; s. 113124
Hlavní autori: Xu, Jing, Song, Kechen, Dong, Hongwen, Yan, Yunhui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Elsevier Ltd 15.04.2020
Elsevier BV
Predmet:
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
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Shrnutí:•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. Practical applications favor anytime asymptotically-optimal algorithms that find and improve an initial solution toward the optimal solution as quickly as possible due to the algorithms may be terminated at any time. We present Batch-to-batch Informed Fast Marching Tree (BBI-FMT*), an anytime asymptotically-optimal sampling-based algorithm that is designed for solving complex motion planning problems. The proposed algorithm has the ability to fast find an initial low-cost solution by the batch sampling-based incremental search and the “lazy” optimal search, then it employs the batch informed sampling-based incremental search and the anytime optimal search to quickly improve the tree and achieve the optimal solution. The proposed anytime optimal search strategy integrates the “lazy” and “non-lazy” optimal search to efficiently improve the tree to the minimum-cost spanning tree in cluttered environments. This paper theoretically analyzes the proposed algorithm in depth and evaluates it by numerical experiments under a few challenging scenarios. The experimental results show that BBI-FMT* outperforms the state-of-the-art algorithms in the self-adaptability, robustness, convergence rate, and success rate of the planning. The proposed algorithm can be widely applied to intelligent robots with expert systems to improve the efficiency and stability of the motion planning and navigation modules which are the core modules in the expert systems.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2019.113124