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
data collection, 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 task completion rate. We propose
MANF-RL-RP, a heterogeneous multi-agent route planning algorithm that
incorporates several efficient designs, including global-local dual information
processing and a tailored model structure for heterogeneous multi-agent
systems. Global-local dual information processing encompasses the extraction
and dissemination of spatial features from global information, as well as the
partitioning and filtering of local information from individual agents.
Regarding the construction of the model structure for heterogeneous
multi-agent, we perform the following work. We design the same data structure
to represent the states of different agents, prove the Markovian property of
the decision-making process of agents to simplify the model structure, and also
design a reasonable reward function to train the model. 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 improvement in terms of task completion rate