GPMAPPO: Collaborative SAR Optimization of Human-UAV in Post-Disaster Scenarios

Unmanned Aerial Vehicles (UAVs) and human teams are vital in Search and Rescue (SAR) missions. The inherent flexibility of UAVs increases search efficiency. The human teams guarantee the timely rescue of targets. However, research on integrating collaborative search between teams and UAVs in SAR missions is still limited. In this paper, we propose an approach to UAV-human collaboration for SAR missions to minimize the expected time of the SAR mission completion. The method mainly faces the problem of scheduling between different teams for multi-target search. Based on the objectives mentioned above, we first rasterize the post-disaster area to search the area effectively. Secondly, a Regional Collaboration Strategy (RCS) is proposed to schedule different teams rationally. Finally, we propose the Path-Greedy Multiagent Proximal Policy Optimization (GPMAPPO) algorithm which combines the Path-Greedy algorithm with the MAPPO algorithm to select the path points rationally. The experimental results demonstrate the superiority of the proposed algorithm in terms of strategy and minimizing the expected time.