A Novel Distributed Method For Time-Critical Task Allocation Problems In Multi-UAV System

This paper considers a time-critical task allocation problem in a distributed multi-UAV system. Existing distributed task allocation algorithms trend to increase communication overhead due to the resolution of numerous task-bundle conflicts between UAVs and easily trap into local optimum with greedy strategy. In this work, we propose a novel distributed task allocation method. First, tasks are divided into multiple clusters, and then UAVs build their task bundles from separate task clusters to avoid conflicts between them, thereby reducing communication overhead. Second, to increase the exploratory ability, an improved ant colony optimization algorithm is proposed to achieve task allocation of UAVs from their corresponding task clusters, instead of using greedy-based strategy. Moreover, an inter-cluster adjustment mechanism is proposed to solve unassigned tasks in task clusters to improve task assignment ratio with low communication overhead, which has been verified in our simulations. Extensive simulation results confirm that our method can achieve efficient task allocation solution with high task assignment ratio and low communication overhead when compared with the state-of-the-art algorithms.