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.

翻译：在工业环境特别是仓库中，自主移动机器人（AMRs）的集成已彻底改变了物流和运营效率。然而，在动态共享空间中确保人类工作人员的安全仍然是一个关键挑战。本研究提出了一种新颖的方法，利用控制屏障函数（CBFs）来增强仓库导航的安全性。通过将基于学习的CBF与开放机器人中间件框架（OpenRMF）相结合，该系统在多机器人、多智能体场景中实现了自适应且安全性增强的控制。使用多种机器人平台进行的实验证明了所提方法在规避静态和动态障碍物（包括行人）方面的有效性。我们的实验评估了机器人数量、机器人平台、速度及障碍物数量各不相同的多种场景，并从中获得了令人满意的性能表现。