Ground unmanned vehicle cluster search method based on multi-agent reinforcement learning

Yinjun Cheng; Siwei Wei; Chunzhi Wang

doi:10.1117/12.3025879

6 May 2024 Ground unmanned vehicle cluster search method based on multi-agent reinforcement learning

Yinjun Cheng, Siwei Wei, Chunzhi Wang

Proceedings Volume 13161, Fourth International Conference on Telecommunications, Optics, and Computer Science (TOCS 2023); 131610M (2024) https://doi.org/10.1117/12.3025879
Event: Fourth International Conference on Telecommunications, Optics and Computer Science (TOCS 2023), 2023, Xi’an, China

Abstract

The achievements in modern unmanned systems technology are noteworthy, with Unmanned Ground Vehicles (UGVs) exemplifying exceptional payload capacity and endurance. The collaborative operation of UGV clusters has emerged as a pivotal direction for constructing future intelligent transportation systems and driving the development of smart cities. Existing algorithms predominantly focus on single-agent systems, leaving a research gap in the exploration of multi-agent systems. To address this void, this paper concentrates on resolving the UGV cluster search problem in closed road sections. The approach involves the utilization of multi-agent reinforcement learning algorithms to handle task scheduling and collision avoidance, enabling UGV clusters to complete extensive area search tasks with minimal data transmission efficiently. This methodology ensures the decentralized and efficient operation of UGV clusters, showcasing robust search capabilities in closed road sections. The practical applications of this approach underscore its substantial potential.

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

Citation Download Citation

Yinjun Cheng, Siwei Wei, and Chunzhi Wang "Ground unmanned vehicle cluster search method based on multi-agent reinforcement learning", Proc. SPIE 13161, Fourth International Conference on Telecommunications, Optics, and Computer Science (TOCS 2023), 131610M (6 May 2024); https://doi.org/10.1117/12.3025879

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