用安全约束函数让仓库机器人避让人类，保障协作安全。
Safe Human Robot Navigation in Warehouse Scenario
- 结合学习型安全约束函数与机器人框架，实现动态避障。
- 多机器人多场景测试中成功避开静/动态障碍物。
- 适合工业仓库中人机协同的智能导航系统研发者。
自主移动机器人（AMRs）在工业环境，尤其是仓库中的应用已显著提升物流效率。然而，在动态共享空间中保障人类工作者的安全仍是关键挑战。本文提出一种新方法，利用控制屏障函数（CBFs）增强仓库导航安全性。通过将基于学习的CBFs与Open Robotics Middleware Framework（OpenRMF）集成，系统在多机器人、多智能体场景中实现自适应且安全的控制。实验使用多种机器人平台，在不同数量机器人、平台类型、速度及障碍物数量的场景下验证了该方法的有效性，均表现出良好性能，能有效规避静态和动态障碍物，包括行人。
原文摘要 · Abstract (English)
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.
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