PQ-RRT: An improved path planning algorithm for mobile robots

•Propose a sampling-based asymptotically optimal path planning algorithm.•The proposed algorithm guarantees a fast convergence rate.•Theoretical proof of asymptotic optimality and fast convergence rate is given. During the last decade, sampling-based algorithms for path planning have gained consider...

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Veröffentlicht in:Expert systems with applications Jg. 152; S. 113425
Hauptverfasser: Li, Yanjie, Wei, Wu, Gao, Yong, Wang, Dongliang, Fan, Zhun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 15.08.2020
Elsevier BV
Schlagworte:
Algorithms
Asymptotic properties
Convergence
Optimal path planning
Optimization
Path planning
Rapidly-exploring random tree (RRT)
Sampling-based algorithms
Path planning
Rapidly-exploring random tree (RRT)
Optimal path planning
Sampling-based algorithms
ISSN:0957-4174, 1873-6793
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Zusammenfassung:•Propose a sampling-based asymptotically optimal path planning algorithm.•The proposed algorithm guarantees a fast convergence rate.•Theoretical proof of asymptotic optimality and fast convergence rate is given. During the last decade, sampling-based algorithms for path planning have gained considerable attention. The RRT*, a variant of RRT (rapidly-exploring random trees), is of particular concern to researchers due to its asymptotic optimality. However, the limits of the slow convergence rate of RRT* makes it inefficient for applications. For the purposes of overcoming these limitations, this paper proposes a novel algorithm, PQ-RRT*, which combines the strengths of P-RRT* (potential functions based RRT*) and Quick-RRT*. PQ-RRT* guarantees a fast convergence to an optimal solution and generates a better initial solution. The asymptotic optimality and fast convergence of the proposed algorithm are proved in this paper. Comparisons of PQ-RRT* with P-RRT* and Quick-RRT* in four benchmarks verify the effectiveness of the proposed algorithm.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2020.113425