An Optimized Scheduling Scheme for UAV-USV Cooperative Search via Multi-Agent Reinforcement Learning Approach

The collaboration between unmanned aerial vehicles (UAV s) and unmanned surface vehicles (USV s) is critical in mar-itime search scenarios, in which UAV s can leverage high altitude and wide field of view providing real-time target information, while USV s have longer endurance capability of performing precise operations for surface targets. However, UAV s are limited by their battery capacity, which reduces their search duration and range. Furthermore, the traditional fixed charging stations necessitate UAV s to return for recharging, resulting in mission interruptions and decreased search efficiency. To address this problem, we propose an optimized scheduling scheme for UAV-USV cooperative search, in which USV s act as mobile charging stations to provide wireless charging services for UAV s to increase cooperative search mission duration with uninterrupted search execution. Firstly, the USV trajectory optimization problem under the target search constraint is formulated to minimize its energy consumption and maximize the UAV energy utilization. Then, we model the problem as a partially observable Markov decision process (POMDP) and design a scheduling algorithm leveraging the multi-agent deep deterministic policy gradient (MADDPG) method for long endurance UAV-USV collaborative search mission under energy constraints. Numerical simulations confirm the effectiveness of the proposed scheduling scheme.