Safe Human Robot Navigation in Warehouse Scenario
This addresses safety challenges for human workers in industrial logistics, but appears incremental as it builds on existing control barrier function methods.
The paper tackles the problem of ensuring human safety in warehouse environments with autonomous mobile robots by proposing a methodology using control barrier functions integrated with a robotics middleware, achieving promising performance in experiments with varied scenarios.
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.