Safe Human Robot Navigation in Warehouse Scenario

Safe Human Robot Navigation in Warehouse Scenario Seth Farrell* 1, Chenghao Li* 1, Hongzhan Y u 1, Ryo Y oshimitsu 2, Sicun Gao 1 and Henrik I. Christensen 1 Abstract -- 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. I. INTRODUCTION In recent decades, the industrial sector, particularly warehouse operations, has experienced a substantial rise in robotic implementation, driven by technological advances, lower costs, and growing consumer demand. This rapid growth has compelled regulatory bodies, including the Occupational Safety and Health Administration (OSHA), to explore measures for securing safe robot operations as automation progresses [1]. A key safety challenge lies in enabling autonomous mobile robots (AMRs) to respond effectively to irregular situations, such as dropped packages or mechanical breakdowns due to prolonged use.