ImpedanceGPT：VLM驱动的微型无人机群阻抗控制，用于动态环境下的智能导航
ImpedanceGPT: VLM-driven Impedance Control of Swarm of Mini-drones for Intelligent Navigation in Dynamic Environment
AI总结：
针对含动态活体与无生命障碍物环境下的无人机群导航难题，提出融合VLM和RAG的ImpedanceGPT系统，可动态调整阻抗控制参数，经实验验证在不同环境下均能实现安全高效的群导航与协作。
摘要：
群体机器人技术在实现动态、不可预测环境中的自主作业方面发挥着关键作用。然而，在同时存在动态活体障碍物（例如人类）和动态无生命障碍物（例如非生命物体）的环境中，确保安全高效的导航仍是一项重大挑战。本文提出了ImpedanceGPT，这是一种将视觉语言模型（Vision-Language Model, VLM）与检索增强生成（retrieval-augmented generation, RAG）相结合的新型系统，可实现复杂环境下微型无人机群自适应导航的实时推理。\n ImpedanceGPT的核心创新在于VLM与RAG的融合，这为无人机提供了更强的周围环境语义理解能力。该系统能够根据障碍物类型和环境条件动态调整阻抗控制参数。我们的方法不仅确保了安全精准的导航，还提升了无人机群内的协作效率。\n 实验评估验证了该系统的有效性。VLM-RAG框架在最优光照条件下的障碍物检测与检索准确率达到80%。在静态环境中，无人机以1.4 m/s的速度绕过动态无生命障碍物，但在靠近人类时会减速至0.7 m/s并增大间距。在动态环境中，无人机靠近硬质障碍物时速度调整为1.0 m/s，而在靠近移动的人类时速度降至0.6 m/s并增大偏转量，以安全规避。
英文摘要：
Swarm robotics plays a crucial role in enabling autonomous operations in dynamic and unpredictable environments. However, a major challenge remains ensuring safe and efficient navigation in environments filled with both dynamic alive (e.g., humans) and dynamic inanimate (e.g., non-living objects) obstacles. In this paper, we propose ImpedanceGPT, a novel system that combines a Vision-Language Model (VLM) with retrieval-augmented generation (RAG) to enable real-time reasoning for adaptive navigation of mini-drone swarms in complex environments. The key innovation of ImpedanceGPT lies in the integration of VLM and RAG, which provides the drones with enhanced semantic understanding of their surroundings. This enables the system to dynamically adjust impedance control parameters in response to obstacle types and environmental conditions. Our approach not only ensures safe and precise navigation but also improves coordination between drones in the swarm. Experimental evaluations demonstrate the effectiveness of the system. The VLM-RAG framework achieved an obstacle detection and retrieval accuracy of 80 % under optimal lighting. In static environments, drones navigated dynamic inanimate obstacles at 1.4 m/s but slowed to 0.7 m/s with increased separation around humans. In dynamic environments, speed adjusted to 1.0 m/s near hard obstacles, while reducing to 0.6 m/s with higher deflection to safely avoid moving humans.
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。
