Cluster-Based Hungarian Approach to Task Allocation for Unmanned Aerial Vehicles

Abstract

In the context of autonomy for Unmanned Aerial Vehicles (UAVs), task allocation plays a significant role for collaborative UAVs to make decisions in dynamic environments. This paper presents a novel Hungarian-based approach to challenging multi-task allocation (MTA) problems, where the number of UAVs is smaller than the number of tasks. We developed the Cluster-Based Hungarian Algorithm (CBHA), in which (1) tasks are grouped such that the number of UAVs is the same as the number of task groups, (2) the original Hungarian algorithm is applied, and (3) an algorithm for travel-salesman-problem (TSP) is applied to perform path planning for individual UAVs. The performance of the proposed CBHA was compared with the Consensus-Based Bundle Algorithm (CBBA) in Monti Carlo simulations, where different numbers of UAVs and tasks were adopted in the scenario of a team of unmanned aerial vehicles traveling through a number of targeting locations. The simulation result shows that the CBHA outperforms CBBA in all cases.