Multi-Objective Distributed Task Allocation for UAV Swarms with Limited Interactions
TL;DR
This paper analyzes the factors affecting communication interactions between UAVs and proposes a bidding-based grouping method to eliminate ineffective communication interactions, and introduces a network simplification algorithm based on reducing the number of triangular network topologies to optimize the communication network structure.
Abstract
Unmanned Aerial Vehicles (UAVs) have experienced rapid development due to their advantages of low cost, high efficiency, flexibility, reliability, and strong environmental adaptability. To fully leverage the potential of UAV swarms in multi-task scenarios, optimizing task allocation has become a crucial direction to enhance the efficiency of UAV swarms. While existing task allocation methods have achieved promising results, frequent inter-UAV information exchange can impose substantial communication overhead in distributed UAV swarms, particularly in resource-constrained applications such as emergency rescue and mountainous operations. In this context, to reduce inter-UAV interactions while maintaining the solution quality of task allocation, this paper first analyzes the factors affecting communication interactions between UAVs. Based on this analysis, we utilize historical bidding information to infer other UAVs’ positions and employ estimation strategies to resolve task conflicts, thereby reducing communication iterations. Furthermore, we propose a bidding-based grouping method to eliminate ineffective communication interactions. Finally, we introduce a network simplification algorithm based on reducing the number of triangular network topologies to optimize the communication network structure. Simulation results demonstrate that the proposed algorithm significantly reduces inter-UAV interactions while preserving the number of allocated tasks, with a maximum observed increase of only approximately 8% in task waiting time across the evaluated simulation settings.