无人机与地面机器人协作导航，应对复杂动态环境挑战。
HetSwarm: Cooperative Navigation of Heterogeneous Swarm in Dynamic and Dense Environments through Impedance-based Guidance
- 用势场法实时规划无人机路径，地面机按路径追踪并保持连接。
- 30次测试成功率达90%，近障碍物时平均偏差仅45厘米。
- 适合物流仓储中需灵活避障的多机器人协同场景。
随着对高效物流与仓库管理需求的增长，无人机（UAV）正作为自动导引车（AGV）的重要补充。无人机可通过密集环境和不同高度飞行提升效率，但其续航、电池寿命和载重有限，需依赖地面站支持。为此，我们提出HetSwarm——一种由无人机与移动地面机器人组成的异构多机器人系统，用于在杂乱且动态的环境中协同导航。该方法采用基于人工势场（APF）的路径规划器，使无人机可实时动态调整轨迹；地面机器人沿此路径行进，并通过阻抗链接维持连通性，确保稳定协调。此外，地面机器人还与低矮地面障碍物建立时间性阻抗链接，避免局部碰撞，而这些障碍物不影响无人机飞行。在多种环境下的实验验证显示，系统在30次测试中成功率高达90%。地面机器人在靠近障碍物时平均偏差为45厘米，证实了有效避障能力。大量基于Gym PyBullet环境的仿真进一步验证了系统的鲁棒性，表明其具备在真实复杂环境中实现实时任务执行的潜力。
原文摘要 · Abstract (English)
With the growing demand for efficient logistics and warehouse management, unmanned aerial vehicles (UAVs) are emerging as a valuable complement to automated guided vehicles (AGVs). UAVs enhance efficiency by navigating dense environments and operating at varying altitudes. However, their limited flight time, battery life, and payload capacity necessitate a supporting ground station. To address these challenges, we propose HetSwarm, a heterogeneous multi-robot system that combines a UAV and a mobile ground robot for collaborative navigation in cluttered and dynamic conditions. Our approach employs an artificial potential field (APF)-based path planner for the UAV, allowing it to dynamically adjust its trajectory in real time. The ground robot follows this path while maintaining connectivity through impedance links, ensuring stable coordination. Additionally, the ground robot establishes temporal impedance links with low-height ground obstacles to avoid local collisions, as these obstacles do not interfere with the UAV's flight. Experimental validation of HetSwarm in diverse environmental conditions demonstrated a 90% success rate across 30 test cases. The ground robot exhibited an average deviation of 45 cm near obstacles, confirming effective collision avoidance. Extensive simulations in the Gym PyBullet environment further validated the robustness of our system for real-world applications, demonstrating its potential for dynamic, real-time task execution in cluttered environments.
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