BABIT: a bidirectional advanced BIT for fast path planning with implicit random geometric graph

Yihang Xiang; Jiajun Ouyang; Zhen Huang; Hui Yu; Junyu Dong; Shu Zhang

doi:10.1117/12.3016851

2 May 2024 BABIT: a bidirectional advanced BIT for fast path planning with implicit random geometric graph

Yihang Xiang, Jiajun Ouyang, Zhen Huang, Hui Yu, Junyu Dong, Shu Zhang

Proceedings Volume 13164, International Workshop on Advanced Imaging Technology (IWAIT) 2024; 1316402 (2024) https://doi.org/10.1117/12.3016851
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2024, 2024, Langkawi, Malaysia

Abstract

Path planning is an active and essential research field for many applications with autonomous mobile robotics. However, popular technologies have limitations in situations where robots require onboard computing to work independently. To this end, this paper proposes a bidirectional advanced batch information tree (BABIT*), which is an asymptotically optimal algorithm path planner enhanced from Batch Informed Trees (BIT*). It uses an edge queue sorted by inflated potential path cost to guide the search of implicit random geometry graph (RGG) to generate explicit solutions while minimizing height calculation tasks such as collision checking. BABIT* promotes the exploration of the entire state space by adopting a more reasonable sampling strategy to achieve a more uniform and decentralized approximation of problems, and ensures a faster discovery of solutions by using symmetric bidirectional search for the state space from both directions. The experimental results show that BABIT* outperforms existing single-query, sampling based planners on the tested problems.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yihang Xiang, Jiajun Ouyang, Zhen Huang, Hui Yu, Junyu Dong, and Shu Zhang "BABIT: a bidirectional advanced BIT for fast path planning with implicit random geometric graph", Proc. SPIE 13164, International Workshop on Advanced Imaging Technology (IWAIT) 2024, 1316402 (2 May 2024); https://doi.org/10.1117/12.3016851

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