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Efficiently obtaining the up-to-date information in the disaster-strickenarea is the key to successful disaster response. Unmanned aerial vehicles(UAVs), workers and cars can collaborate to accomplish sensing tasks, such asdata collection, in disaster-stricken areas. In this paper, we explicitlyaddress the route planning for a group of agents, including UAVs, workers, andcars, with the goal of maximizing the task completion rate. We proposeMANF-RL-RP, a heterogeneous multi-agent route planning algorithm thatincorporates several efficient designs, including global-local dual informationprocessing and a tailored model structure for heterogeneous multi-agentsystems. Global-local dual information processing encompasses the extractionand dissemination of spatial features from global information, as well as thepartitioning and filtering of local information from individual agents.Regarding the construction of the model structure for heterogeneousmulti-agent, we perform the following work. We design the same data structureto represent the states of different agents, prove the Markovian property ofthe decision-making process of agents to simplify the model structure, and alsodesign a reasonable reward function to train the model. Finally, we conducteddetailed experiments based on the rich simulation data. In comparison to thebaseline algorithms, namely Greedy-SC-RP and MANF-DNN-RP, MANF-RL-RP hasexhibited a significant improvement in terms of task completion rate.

Collaborative Route Planning of UAVs, Workers and Cars for Crowdsensing in Disaster Response