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

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.