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

• Published in: Expert systems with applications Vol. 152; p. 113425
• Main Authors: Li, Yanjie, Wei, Wu, Gao, Yong, Wang, Dongliang, Fan, Zhun
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.08.2020; Elsevier BV
• Subjects: 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
• DOI: 10.1016/j.eswa.2020.113425

•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.