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有效的人机协作（HRC），将感知、认知和预测无缝集成至机器人控制器，对于在异构环境（机器人、人类及设备）中实现实时感知、响应与通信至关重要。本文所提方法利用非线性模型预测控制（NMPC）的预测能力，基于视觉系统的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，由于采样时间紧凑，NMPC解具有近似性，因此系统安全性无法得到保证。为解决该问题，本文提出一种新型安全关键范式——采用指数控制障碍函数（ECBF）作为安全滤波器。我们还在V-REP中设计了一个简单的人机协作场景，用于评估所提控制器的性能，并探究结合人体姿态预测是否有助于实现安全高效协作。机器人利用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。多种配置下的实验结果验证了所提运动规划与执行框架的有效性。针对所考虑的HRC任务，执行时间缩短了19.8%。