In advanced manufacturing, strict safety guarantees are required to allow humans and robots to work together in a shared workspace. One of the challenges in this application field is the variety and unpredictability of human behavior, leading to potential dangers for human coworkers. This paper presents a novel control framework by adopting safety-critical control and uncertainty estimation for human-robot collaboration. Additionally, to select the shortest path during collaboration, a novel quadratic penalty method is presented. The innovation of the proposed approach is that the proposed controller will prevent the robot from violating any safety constraints even in cases where humans move accidentally in a collaboration task. This is implemented by the combination of a time-varying integral barrier Lyapunov function (TVIBLF) and an adaptive exponential control barrier function (AECBF) to achieve a flexible mode switch between path tracking and collision avoidance with guaranteed closed-loop system stability. The performance of our approach is demonstrated in simulation studies on a 7-DOF robot manipulator. Additionally, a comparison between the tasks involving static and dynamic targets is provided.

翻译：在先进制造业中，为确保人与机器人在共享工作空间内协同作业，需提供严格的安全保障。该应用领域的挑战之一在于人类行为的多样性与不可预测性，这可能对人类同事造成潜在危险。本文提出了一种融合安全关键控制与不确定性估计的新型控制框架，用于人机协作。此外，为在协作过程中选择最短路径，还提出了一种新颖的二次罚函数方法。该方法的核心创新在于：即使人类在协作任务中发生意外移动，所提出的控制器也能防止机器人违反任何安全约束。这通过结合时变积分障碍李雅普诺夫函数（TVIBLF）与自适应指数控制障碍函数（AECBF）实现，从而在路径跟踪与避障之间实现灵活的模式切换，并保证闭环系统稳定性。在七自由度机器人机械臂上的仿真研究验证了该方法的性能，同时对比了涉及静态与动态目标的任务场景。