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PQ-RRT*: An improved path planning algorithm for mobile robots
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DOI：10.1016/j.eswa.2020.113425.png)
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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. (C) 2020 Elsevier Ltd. All rights reserved.
Keyword:
Path planning
Sampling-based algorithms
Rapidly-exploring random tree (RRT)
Optimal path planning
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7.5
论文数:
3.0W
被引数:
10.2W
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