SwarmVLM：用于动态仓储中异构机器人自主导航的VLM引导阻抗控制
SwarmVLM: VLM-Guided Impedance Control for Autonomous Navigation of Heterogeneous Robots in Dynamic Warehousing
AI总结：
SwarmVLM利用VLM和RAG调整阻抗控制参数，实现无人机领航与地面机器人跟随的异构语义协作，在动态仓储杂乱环境中实现了92%成功率的安全导航。
摘要：
随着对高效物流需求的不断增长，无人机（UAV）越来越多地与自动导引车（AGV）结合使用。虽然无人机具备在密集环境和不同高度间导航的能力，但受限于电池寿命、有效载荷容量和飞行时间，因此需要协调的地面支持。针对异构导航问题，SwarmVLM通过阻抗控制实现无人机与地面机器人之间的语义协作，从而解决了这些局限性。该系统利用视觉语言模型（VLM）和检索增强生成（RAG）来调整阻抗控制参数，以响应环境变化。在此框架中，无人机作为领航者，利用人工势场（APF）规划进行实时导航，而地面机器人则通过具有自适应拓扑的虚拟阻抗链路跟随，以避免与矮小障碍物发生碰撞。该系统在12次真实世界试验中取得了92%的成功率。在最佳光照条件下，VLM-RAG框架在物体检测和阻抗参数选择方面达到了8%的准确率。移动机器人优先考虑避开矮小障碍物，偶尔会导致偏离无人机路径多达50厘米的横向偏移，这展示了在杂乱环境中的安全导航。
英文摘要：
With the growing demand for efficient logistics, unmanned aerial vehicles (UAVs) are increasingly being paired with automated guided vehicles (AGVs). While UAVs offer the ability to navigate through dense environments and varying altitudes, they are limited by battery life, payload capacity, and flight duration, necessitating coordinated ground support. Focusing on heterogeneous navigation, SwarmVLM addresses these limitations by enabling semantic collaboration between UAVs and ground robots through impedance control. The system leverages the Vision Language Model (VLM) and the Retrieval-Augmented Generation (RAG) to adjust impedance control parameters in response to environmental changes. In this framework, the UAV acts as a leader using Artificial Potential Field (APF) planning for real-time navigation, while the ground robot follows via virtual impedance links with adaptive link topology to avoid collisions with short obstacles. The system demonstrated a 92% success rate across 12 real-world trials. Under optimal lighting conditions, the VLM-RAG framework achieved 8% accuracy in object detection and selection of impedance parameters. The mobile robot prioritized short obstacle avoidance, occasionally resulting in a lateral deviation of up to 50 cm from the UAV path, which showcases safe navigation in a cluttered setting.
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。
