Bi-AM-RRT: A Fast and Efficient Sampling-Based Motion Planning Algorithm in Dynamic Environments

The efficiency of sampling-based motion planning brings wide application in autonomous mobile robots. The conventional rapidly exploring random tree (RRT) algorithm and its variants have gained significant successes, but there are still challenges for the optimal motion planning of mobile robots in...

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Veröffentlicht in:IEEE transactions on intelligent vehicles Jg. 9; H. 1; S. 1282 - 1293
Hauptverfasser: Zhang, Ying, Wang, Heyong, Yin, Maoliang, Wang, Jiankun, Hua, Changchun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Bidirectional search
Intelligent vehicles
mobile robot
Mobile robots
Motion planning
Optimization
rewiring
Robot dynamics
Robot motion
Sampling
Sampling methods
Search problems
Searching
Vehicle dynamics
ISSN:2379-8858, 2379-8904
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Zusammenfassung:The efficiency of sampling-based motion planning brings wide application in autonomous mobile robots. The conventional rapidly exploring random tree (RRT) algorithm and its variants have gained significant successes, but there are still challenges for the optimal motion planning of mobile robots in dynamic environments. In this paper, based on Bidirectional RRT and the use of an assisting metric (AM), we propose a novel motion planning algorithm, namely Bi-AM-RRT*. Different from the existing RRT-based methods, the AM with a larger connection distance is introduced in this paper to optimize the performance of robot motion planning in dynamic environments with obstacles. On this basis, the bidirectional search sampling strategy is employed to reduce the search time. Further, we present a new rewiring method to shorten path lengths. The effectiveness and efficiency of the proposed Bi-AM-RRT* are proved through comparative experiments in different environments. Experimental results show that the Bi-AM-RRT* algorithm can achieve better performance in terms of path length and search time, and always finds near-optimal paths with the shortest search time when the diffusion metric is used as the AM.
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ISSN:2379-8858
2379-8904
DOI:10.1109/TIV.2023.3307283