SwarmVLM：動的倉庫環境における異種ロボット群の自律ナビゲーションのためのVLM誘導インピーダンス制御
SwarmVLM: VLM-Guided Impedance Control for Autonomous Navigation of Heterogeneous Robots in Dynamic Warehousing
UAVとAGVの異種ロボット群が、VLMとRAGを用いてインピーダンス制御パラメータを適応させ、動的倉庫内を協調自律ナビゲーションするシステムを提案。実環境12試行で92%の成功率を達成。
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著者: Malaika Zafar, Roohan Ahmed Khan, Faryal Batool, Yasheerah Yaqoot, Ziang Guo, Mikhail Litvinov, Aleksey Fedoseev, Dzmitry Tsetserukou
分類: cs.RO
原文アブストラクト
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.
