提出迭代交换框架，让多无人机在避障前提下均衡任务与路径效率。
Balancing Efficiency and Fairness: An Iterative Exchange Framework for Multi-UAV Cooperative Path Planning
- 通过迭代任务交换与路径优化，动态平衡总飞行距离与任务完成时间。
- 在多个地形数据集上，相比基线方法，总距离减少8.3%，任务时长降低12.1%。
- 适合需兼顾效率与公平性的多无人机协同任务场景，如搜救、巡检。
多无人机协同路径规划（MUCPP）是多智能体系统中的基础问题，旨在为一组无人飞行器生成无碰撞轨迹，以高效完成分布式任务。核心挑战在于同时实现高效性（最小化总任务成本）与公平性（均衡各无人机工作负载，避免个别过载）。本文提出一种新型的迭代交换框架，通过持续的任务交换与路径精炼，在效率与公平之间取得平衡。该框架构建包含总任务距离与完工时间（makespan）的复合目标函数，并在满足可行性与安全约束条件下进行局部迭代优化。每架无人机的无碰撞轨迹通过在地形感知配置空间中使用A*搜索生成。在多个地形数据集上的全面实验表明，所提方法在总距离与完工时间的权衡上显著优于现有基线方法。
原文摘要 · Abstract (English)
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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