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

TL;DR

This paper introduces an iterative exchange framework for multi-UAV path planning that balances efficiency and fairness, improving mission performance through local task exchanges and path refinements.

Contribution

It proposes a novel iterative framework that optimizes both total mission cost and workload fairness in multi-UAV path planning, a significant advancement over existing methods.

Findings

Achieves better trade-offs between total distance and makespan.

Demonstrates superior performance on multiple terrain datasets.

Ensures collision-free trajectories with terrain-aware A* search.

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.