Collaborative Route Planning of UAVs, Workers, and Cars for Crowdsensing in Disaster Response
Lei Han, Chunyu Tu, Zhiwen Yu, Zhiyong Yu, Weihua Shan, Liang Wang, Bin Guo
Abstract
Efficiently obtaining the up-to-date information in the disaster-stricken area is the key to successful disaster response. Unmanned aerial vehicles (UAVs), workers and cars can collaborate to accomplish sensing tasks, such as life detection task in disaster-stricken areas. In this paper, we explicitly address the route planning for a group of agents, including UAVs, workers, and cars, with the goal of maximizing the sensing task completion rate. we propose a MARL-based heterogeneous multi-agent route planning algorithm called MANF-RL-RP. The algorithm has made targeted designs in terms of global-local dual information processing and model structure for heterogeneous multi-agent, making it effectively considers the collaboration among heterogeneous agents and the long-term impact of current decisions. Finally, we conducted detailed experiments based on the rich simulation data. In comparison to the baseline algorithms, namely Greedy-SC-RP and MANF-DNN-RP, MANF-RL-RP has exhibited a significant performance improvement. Compared to MANF-DNN-RP and Greedy-SC-RP, the task completion rate based on MANF-RL-RP increased by an average of 8.82% and 56.8%, respectively.