ORCID
- Chunxu Li: 0000-0001-7851-0260
Abstract
Aiming at the problem that the progressively optimized Rapidly-exploring Random Trees Star (RRT*) algorithm generates a large number of redundant nodes, which causes slow convergence and low search efficiency in high-dimensional and complex environments. In this paper we present Target-biased Informed Trees (TBIT*), an improved RRT* path planning algorithm based on target-biased sampling strategy and heuristic optimization strategy. The algorithm adopts a combined target bias strategy in the search phase of finding the initial path to guide the random tree to grow rapidly toward the target direction, thereby reducing the generation of redundant nodes and improving the search efficiency of the algorithm; after the initial path is searched, heuristic sampling is used to optimize the initial path instead of optimizing the random tree, which can benefit from reducing useless calculations, and improve the convergence capability of the algorithm. The experimental results show that the algorithm proposed in this article changes the randomness of the algorithm to a certain extent, and the search efficiency and convergence capability in complex environments have been significantly improved, indicating that the improved algorithm is feasible and efficient.
DOI Link
Publication Date
2022-04-14
Publication Title
Journal of Computational Design and Engineering
Volume
9
Issue
2
ISSN
2288-4300
Acceptance Date
2022-02-22
Deposit Date
2026-06-10
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
First Page
755
Last Page
771
Recommended Citation
Wang, X., Ma, X., Li, X., Ma, X., & Li, C. (2022) 'Target-biased informed trees: sampling-based method for optimal motion planning in complex environments', Journal of Computational Design and Engineering, 9(2), pp. 755-771. Available at: 10.1093/jcde/qwac025
