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:为了在智能制造中实现安全有效的人机协作,将传感、认知和预测融入机器人控制器以实现实时意识、响应和通信是至关重要的,尤其是在异构环境中(包括机器人、人类和设备)。所提出的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的路径规划。为了满足实时路径规划的要求,应用基于罚函数的嵌入式求解器。然而,由于采样时间紧迫,NMPC解决方案是近似的,因此无法保证系统的安全性。为了解决这个问题,我们制定了一个新的安全关键范式,使用指数控制屏障函数(ECBF)作为安全过滤器。我们还设计了一个简单的V-REP人机协作场景,以评估所提出的控制器的性能,并研究将人类姿态预测集成到安全高效的协作中是否有帮助。机器人使用OptiTrack相机进行感知,并动态地生成无碰撞路径到预测的目标交互位置。不同配置的结果证实了所提出的运动规划和执行框架的效率。它给HRC任务带来19.8%的执行时间缩短。