CCPF-RRT: An improved path planning algorithm with consideration of congestion

Path planning is essential for robots to efficiently execute jobs in challenging settings. In this paper, we propose a new algorithm, called potential function-based sampling heuristic optimal path planning considering congestion (CCPF-RRT*), extending the standard Rapidly-exploring Random Tree star...

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Vydáno v:Expert systems with applications Ročník 228; s. 120403
Hlavní autoři: Liang, Yan-ming, Zhao, Hai-yang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.10.2023
Témata:
Considering congestion
Path planning
Rapidly-exploring Random Tree star (RRT)
Sampling-based algorithms
Considering congestion
Path planning
Rapidly-exploring Random Tree star (RRT)
Sampling-based algorithms
ISSN:0957-4174, 1873-6793
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Shrnutí:Path planning is essential for robots to efficiently execute jobs in challenging settings. In this paper, we propose a new algorithm, called potential function-based sampling heuristic optimal path planning considering congestion (CCPF-RRT*), extending the standard Rapidly-exploring Random Tree star (RRT*) to address the issues of slow convergence, high path cost, and how to plan suitable paths in the presence of congested regions in the environment. First, the CCPF-RRT* algorithm adds the artificial potential field to RRT*, which can drastically cut down on the number of iterations and speed up convergence. Second, a movement cost function that takes congestion intensity and path length into account when evaluating a connection between two path nodes is created. This function is then used to direct the growth of new nodes and the updating of parent nodes so that the algorithm takes congestion intensity and path length into account when generating paths. Third, in order to reduce the path cost, the algorithm establishes parent nodes for random nodes by combining the benefits of the F-RRT* algorithm. The movement cost function is added to the process of establishing parent nodes to optimize the path cost in order to prevent the creation of parent nodes that force the path to traverse through crowded areas. In experimental simulation results, the algorithm outperforms RRT*, Q-RRT*, PQ-RRT*, and F-RRT* not only in terms of initial solution and quick convergence speed, but is also able to design an ideal path with the lowest movement cost in a crowded environment using the movement cost function.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.120403