倉庫環境における安全な人とロボットのナビゲーション
Safe Human Robot Navigation in Warehouse Scenario
学習ベースの制御バリア関数とOpenRMFを統合し、倉庫内で人を含む動的障害物を避けながら複数ロボットを安全にナビゲートする手法を提案した。
著者: Seth Farrell, Chenghao Li, Hongzhan Yu, Ryo Yoshimitsu, Sicun Gao, Henrik I. Christensen
分類: cs.RO, cs.LG, cs.MA
原文アブストラクト
The integration of autonomous mobile robots (AMRs) in industrial environments, particularly warehouses, has revolutionized logistics and operational efficiency. However, ensuring the safety of human workers in dynamic, shared spaces remains a critical challenge. This work proposes a novel methodology that leverages control barrier functions (CBFs) to enhance safety in warehouse navigation. By integrating learning-based CBFs with the Open Robotics Middleware Framework (OpenRMF), the system achieves adaptive and safety-enhanced controls in multi-robot, multi-agent scenarios. Experiments conducted using various robot platforms demonstrate the efficacy of the proposed approach in avoiding static and dynamic obstacles, including human pedestrians. Our experiments evaluate different scenarios in which the number of robots, robot platforms, speed, and number of obstacles are varied, from which we achieve promising performance.
