优化 UAV-UGV 协同作业：面向障碍环境中路径规划的混合聚类与多智能体强化学习方法
Optimizing UAV-UGV Coalition Operations: A Hybrid Clustering and Multi-Agent Reinforcement Learning Approach for Path Planning in Obstructed Environment
AI总结：
本文提出一种基于改进 mean-shift 聚类与 MADDPG、MAPPO 多智能体强化学习的 UAV-UGV 可变数量协同路径规划方法，通过多区域目标分配实现无碰撞导航，大幅提升了多目标交战的任务完成率与导航效率。
摘要：
无人飞行器（UAV）与无人地面车辆（UGV）协同执行的最关键应用之一，是通过遵循最省时的路线并避免碰撞来到达预定目标。然而，UAV 受限于电池续航能力，而 UGV 则因障碍物和地形高差变化面临可达性挑战。现有文献主要关注一对一协同，这限制了到达目标的效率。在本研究中，我们提出了一种针对可变数量车辆组成的 UAV-UGV 协同的新方法，采用改进的 mean-shift 聚类算法将目标分割为多个区域。每辆车利用 Multi-agent Deep Deterministic Policy Gradient (MADDPG) 和 Multi-agent Proximal Policy Optimization (MAPPO) 这两种先进的强化学习算法，构建有效的协同以在无碰撞的情况下导航通过障碍环境。这种基于密度和距离将目标分配到不同圆形区域的方法，显著减少了到达这些目标所需的时间。此外，在协同中引入 UAV 和 UGV 数量的可变性，通过实现同时的多目标交战提升了任务效率。我们的实验评估结果表明，所提出的方法大幅超越了当前最先进的技术，在目标导航时间和任务完成率方面将效率几乎提升了一倍。
英文摘要：
One of the most critical applications undertaken by coalitions of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) is reaching predefined targets by following the most time-efficient routes while avoiding collisions. Unfortunately, UAVs are hampered by limited battery life, and UGVs face challenges in reachability due to obstacles and elevation variations. Existing literature primarily focuses on one-to-one coalitions, which constrains the efficiency of reaching targets. In this work, we introduce a novel approach for a UAV-UGV coalition with a variable number of vehicles, employing a modified mean-shift clustering algorithm to segment targets into multiple zones. Each vehicle utilizes Multi-agent Deep Deterministic Policy Gradient (MADDPG) and Multi-agent Proximal Policy Optimization (MAPPO), two advanced reinforcement learning algorithms, to form an effective coalition for navigating obstructed environments without collisions. This approach of assigning targets to various circular zones, based on density and range, significantly reduces the time required to reach these targets. Moreover, introducing variability in the number of UAVs and UGVs in a coalition enhances task efficiency by enabling simultaneous multi-target engagement. The results of our experimental evaluation demonstrate that our proposed method substantially surpasses current state-of-the-art techniques, nearly doubling efficiency in terms of target navigation time and task completion rate.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
