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Target-biased informed trees: sampling-based method for optimal motion planning in complex environments
Journal of Computational Design and Engineering, Volume: 9, Issue: 2, Pages: 755 - 771
Swansea University Author:
Chunxu Li
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DOI (Published version): 10.1093/jcde/qwac025
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 Inform...
| Published in: | Journal of Computational Design and Engineering |
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| ISSN: | 2288-5048 |
| Published: |
Oxford University Press (OUP)
2022
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa65998 |
| 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. |
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| Keywords: |
path planning, rapidly exploring random trees, improved RRT*, target bias, heuristic |
| College: |
Faculty of Science and Engineering |
| Issue: |
2 |
| Start Page: |
755 |
| End Page: |
771 |