To enable safe and effective human-robot collaboration (HRC) in smart manufacturing, seamless integration of sensing, cognition, and prediction into the robot controller is critical for real-time awareness, response, and communication inside a heterogeneous environment (robots, humans, and equipment). The proposed approach takes advantage of the prediction capabilities of nonlinear model predictive control (NMPC) to execute a safe path planning based on feedback from a vision system. In order to satisfy the requirement of real-time path planning, an embedded solver based on a penalty method is applied. However, due to tight sampling times NMPC solutions are approximate, and hence the safety of the system cannot be guaranteed. To address this we formulate a novel safety-critical paradigm with an exponential control barrier function (ECBF) used as a safety filter. We also design a simple human-robot collaboration scenario using V-REP to evaluate the performance of the proposed controller and investigate whether integrating human pose prediction can help with safe and efficient collaboration. The robot uses OptiTrack cameras for perception and dynamically generates collision-free trajectories to the predicted target interactive position. Results for a number of different configurations confirm the efficiency of the proposed motion planning and execution framework. It yields a 19.8% reduction in execution time for the HRC task considered.

翻译：为实现智能制造中安全高效的人机协作，需将感知、认知与预测无缝集成至机器人控制器中，以在异构环境（机器人、人类与设备）中实现实时感知、响应与通信。本方法利用非线性模型预测控制（NMPC）的预测能力，基于视觉系统反馈执行安全路径规划。为满足实时路径规划需求，采用基于惩罚法的嵌入式求解器。然而，由于采样时间限制，NMPC解为近似解，系统安全性无法保障。为此，我们提出一种新型安全关键范式，使用指数控制障碍函数（ECBF）作为安全滤波器。同时，设计基于V-REP的简单人机协作场景评估所提控制器性能，并研究人体姿态预测对安全高效协作的促进作用。机器人通过OptiTrack相机进行感知，动态生成无碰撞轨迹至预测的目标交互位置。不同配置下的实验结果证实了所提运动规划与执行框架的有效性，在人机协作任务中执行时间缩短19.8%。