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

2025-11-29

AI Summary

Addressing the challenge of jointly optimizing efficiency (total cost) and fairness (load balancing) in multi-UAV cooperative path planning, this paper proposes an iterative exchange optimization framework that alternates between task reallocation and path optimization under a composite objective integrating total travel distance and makespan. We innovatively design a local task-exchange mechanism subject to safety constraints and generate collision-free trajectories using A* in a terrain-aware configuration space, followed by path refinement to enhance solution quality. Experimental results on multiple terrain datasets demonstrate that our approach significantly reduces total flight distance and minimizes makespan, achieving superior trade-offs between efficiency and fairness compared to existing baseline methods.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Balancing efficiency and fairness in multi-UAV path planning
• Minimizing total mission cost while distributing workload evenly
• Generating collision-free trajectories for distributed tasks via iterative exchanges

Innovation

• Iterative task exchanges and path refinements
• Composite objective combining distance and makespan
• A* search in terrain-aware configuration space