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
• 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.

Related papers

• Towards Reliable Aerial Ground Vehicle Collaboration: An Integrated Planning and Autonomy Framework for Field Deployment [0.0]
  We present an integrated planning and autonomy framework for reliable field deployment.<n>We formulate the problem as an energy constrained cooperative routing task and solve it using a Deep Reinforcement Learning (DRL) based planner.<n>To bridge the gap between planning and execution, we introduce a standardized two layer YAML based mission API.<n>This framework is supported by a complete autonomy stack using PX4/MAVSDK for UAV control and ROS 2/Nav2 for UGV navigation.
  arXiv  Detail & Related papers  (2026-07-07T15:59:49Z)
• Optimal Safety-Aware Scheduling for Multi-Agent Aerial 3D Printing with Utility Maximization under Dependency Constraints [16.61271813421072]
  An optimization problem takes a construction mission divided into sub-tasks and a team of autonomous UAVs, along with limited volume and battery.<n>It generates an optimal mission plan comprising task assignments and scheduling while accounting for task dependencies.<n>An importance prioritization is proposed to accelerate the computation by guiding the solution toward more important tasks.
  arXiv  Detail & Related papers  (2025-12-05T15:34:43Z)
• Dependency-Aware Task Offloading in Multi-UAV Assisted Collaborative Mobile Edge Computing [53.88774113545582]
  This paper presents a novel multi-unmanned aerial vehicle (UAV) assisted collaborative mobile edge computing (MEC) framework.<n>It aims to minimize the system cost, and thus realize an improved trade-off between task consumption and energy consumption.<n>We show that the proposed scheme can significantly reduce the system cost, and thus realize an improved trade-off between task consumption and energy consumption.
  arXiv  Detail & Related papers  (2025-10-23T02:55:40Z)
• Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning [52.64813150003228]
  The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring.<n>In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas.<n>The task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV.
  arXiv  Detail & Related papers  (2025-01-11T02:32:42Z)
• Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks [60.085771314013044]
  Low-altitude economy holds significant potential for development in areas such as communication and sensing.<n>We propose a Clustering-based Multi-agent Deep Deterministic Policy Gradient (CMADDPG) algorithm to address the multi-UAV cooperative task scheduling challenges in SAGIN.
  arXiv  Detail & Related papers  (2024-12-14T06:17:33Z)
• UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning [79.16150966434299]
  We formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs. We use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB.
  arXiv  Detail & Related papers  (2024-04-11T03:19:22Z)
• Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning [12.97430155510359]
  This work proposes a weighted random generator for the creation and mutation of new individuals. The main objective of this work is to reduce the convergence rate of the MOEA solver for multi-UAV mission planning.
  arXiv  Detail & Related papers  (2024-02-28T23:05:27Z)
• Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System [25.32139119893323]
  Unmanned aerial vehicles (UAVs) can be applied in many Internet of Things (IoT) systems. The UAV-IoT wireless channels may be occasionally blocked by trees or high-rise buildings. This article aims to minimize the energy consumption of the system by jointly optimizing the deployment and trajectory of the UAV.
  arXiv  Detail & Related papers  (2022-10-27T06:27:40Z)
• Efficient UAV Trajectory-Planning using Economic Reinforcement Learning [65.91405908268662]
  We introduce REPlanner, a novel reinforcement learning algorithm inspired by economic transactions to distribute tasks between UAVs. We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources. As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size.
  arXiv  Detail & Related papers  (2021-03-03T20:54:19Z)
• Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing [99.27205900403578]
  An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed. We aim to jointly optimize the geographical fairness among all the user equipments (UEs) and the fairness of each UAV's UE-load. We show that our proposed solution has considerable performance over other traditional algorithms.
  arXiv  Detail & Related papers  (2020-09-23T17:44:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.