Demand-Driven Hierarchical Task Allocation in UAV Swarms: A Distributed ADMM Optimization Approach
Bin Luo, Bo Jiang, Hong Xu · 2025
This study sets out to address the issue of demand-driven task allocation in unmanned aerial vehicle (UAV) swarms. To this end, a novel hierarchical framework is proposed, integrating layered fuzzy possibilistic c-means (FPCM) clustering with a distributed alternating direction method of multipliers (ADMM) optimisation scheme. The proposed method ensures high scalability and minimal communication overhead, while delivering task allocation performance that approaches global optimality. Results demonstrate that the framework significantly outperforms existing benchmarks, including the consensus-based bundle algorithm (CBBA), greedy heuristics, and randomized strategies, highlighting its superior efficiency, robustness, and applicability in resource-constrained environments.