HB-RRT:A path planning algorithm for mobile robots using Halton sequence-based rapidly-exploring random tree

Path planning remains crucial for efficient robot operation. A Halton Biased Rapidly-exploring Random Tree (HB-RRT) path planning algorithm is introduced in this study. The Halton sequence, known for its uniform distribution and low discrepancy, is employed for sampling. Issues arising from the pseu...

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Vydáno v:Engineering applications of artificial intelligence Ročník 133; s. 108362
Hlavní autoři: Zhong, Huageng, Cong, Ming, Wang, Minghao, Du, Yu, Liu, Dong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.07.2024
Témata:
Halton sequence
Mobile robotics
Optimal path planning
Path planning
Rapidly-exploring random tree (RRT)
Sampling-based algorithms
Path planning
Rapidly-exploring random tree (RRT)
Halton sequence
Optimal path planning
Mobile robotics
Sampling-based algorithms
ISSN:0952-1976, 1873-6769
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Shrnutí:Path planning remains crucial for efficient robot operation. A Halton Biased Rapidly-exploring Random Tree (HB-RRT) path planning algorithm is introduced in this study. The Halton sequence, known for its uniform distribution and low discrepancy, is employed for sampling. Issues arising from the pseudo-random sequence in the standard RRT algorithm, leading to uneven distribution of sampling points, are addressed. A mouse-inspired goal-oriented strategy and a candidate sampling pool strategy are incorporated to enhance the sampling point quality, thereby addressing the challenge of insufficient memory during node expansion. Path optimization is further achieved through a multi-level planning approach, which aims to minimize redundancy. A subsequent smoothing of the path is conducted using a cubic B-spline method. Comparisons with the RRT, Bionic Target Bias-RRT, and Informed-RRT* algorithms, through both numerical simulations and real-world testing, confirm the superiority of the HB-RRT algorithm in terms of planning time, path length, and overall path quality.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2024.108362