Balancing Efficiency and Fairness: An Iterative Exchange Framework for Multi-UAV Cooperative Path Planning
Hongzong Li, Luwei Liao, Xiangguang Dai, Yuming Feng, Rong Feng, Shiqin Tang
TL;DR
This work tackles multi-UAV cooperative path planning with a dual focus on efficiency and fairness. It introduces an Iterative-Exchange Framework that jointly optimizes task allocation and path refinement via neighborhood exchanges guided by a composite objective that combines total distance and makespan, using an A*-based distance oracle to ensure feasibility. The approach is validated on ten MUCPP datasets, where it consistently achieves superior trade-offs between total distance and makespan compared to strong baselines. The results suggest significant practical gains for coordinated UAV missions in terrain-rich environments, with potential extensions to dynamic obstacles, energy limits, and communication constraints for large fleets.
Abstract
Multi-UAV cooperative path planning (MUCPP) is a fundamental problem in multi-agent systems, aiming to generate collision-free trajectories for a team of unmanned aerial vehicles (UAVs) to complete distributed tasks efficiently. A key challenge lies in achieving both efficiency, by minimizing total mission cost, and fairness, by balancing the workload among UAVs to avoid overburdening individual agents. This paper presents a novel Iterative Exchange Framework for MUCPP, balancing efficiency and fairness through iterative task exchanges and path refinements. The proposed framework formulates a composite objective that combines the total mission distance and the makespan, and iteratively improves the solution via local exchanges under feasibility and safety constraints. For each UAV, collision-free trajectories are generated using A* search over a terrain-aware configuration space. Comprehensive experiments on multiple terrain datasets demonstrate that the proposed method consistently achieves superior trade-offs between total distance and makespan compared to existing baselines.