Balancing Efficiency and Fairness: An Iterative Exchange Framework for Multi-UAV Cooperative Path Planning

• URL: http://arxiv.org/abs/2512.00410v1
• Date: Sat, 29 Nov 2025 09:41:22 GMT
• Title: Balancing Efficiency and Fairness: An Iterative Exchange Framework for Multi-UAV Cooperative Path Planning
• Authors: Hongzong Li, Luwei Liao, Xiangguang Dai, Yuming Feng, Rong Feng, Shiqin Tang,
• Abstract summary: Multi-UAV cooperative path planning (MU CPP) is a fundamental problem in multi-agent systems.<n>This paper presents a novel Iterative Exchange Framework for MU CPP, balancing efficiency and fairness through iterative task exchanges and path refinements.
• Score: 6.14294229178003
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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