面向基于视觉的人机协作中动态运动规划与执行的NMPC-ECBF框架
An NMPC-ECBF Framework for Dynamic Motion Planning and Execution in vision-based Human-Robot Collaboration
AI总结：
针对智能制造中人机协作的安全实时运动规划需求，提出融合NMPC预测规划与ECBF安全滤波的框架，经V-REP场景验证可将任务执行时间降低19.8%。
摘要：
为在智能制造中实现安全高效的人机协作（HRC），将感知、认知与预测无缝集成到机器人控制器中，对于异构环境（机器人、人类与设备）内的实时感知、响应与通信至关重要。本文提出的方法利用非线性模型预测控制（NMPC，Nonlinear Model Predictive Control）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的要求，采用了基于惩罚法的嵌入式求解器。然而，由于采样时间极短，NMPC的解是近似的，因此系统的安全性无法得到保障。为解决这一问题，我们构建了一种全新的安全关键范式，采用指数控制屏障函数（ECBF，Exponential Control Barrier Function）作为安全滤波器。我们还利用V-REP搭建了一个简单的人机协作场景，以评估所提控制器的性能，并探究整合人体姿态预测是否有助于实现安全高效的协作。机器人使用OptiTrack相机进行感知，并动态生成通往预测的目标交互位置的无碰撞轨迹。多种不同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，针对所研究的人机协作任务，该框架将执行时间降低了19.8%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
