发表机构
Shanghai Jiao Tong University（上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种集成性能约束与优化机制的在线动态力引导方法，通过可变导纳控制和虚拟力引导提升协作机器人示教过程中的操作性能，实验验证其可提高工作效率。
AI 中文摘要
协作机器人越来越多地部署在频繁更换产品的工业场景中。作为一种直观的编程方法，示教操作促进了机器人的快速部署。然而，用户在示教操作过程中可能忽视机器人的构型，导致操作性能下降。操作性能是指机器人产生运动的能力，可通过雅可比矩阵的最小奇异值来量化。为解决这一问题，本文提出了一种集成了性能约束与优化机制的在线动态力引导方法。具体而言，可变导纳控制将机器人的操作性能维持在预设阈值之上，同时虚拟力主动引导用户将机器人拖向具有更优性能的构型。实验在六自由度协作机器人上进行，比较了任务空间中的三条典型路径。为评估示教轨迹的质量，进行了轨迹回放实验，以分析机器人操作性能与工作效率之间的关系。结果表明，所提方法有效提升了机器人的操作性能，进而提高了工作效率，对于减少工业部署中的生产节拍时间具有重要价值。
英文摘要
Collaborative robots are increasingly deployed in industrial scenarios characterized by frequent product changeovers. As an intuitive programming method, kinesthetic teaching facilitates rapid robot deployment. However, users may overlook the configuration of the robot during kinesthetic teaching, leading to degradation in operational performance. Operational performance refers to the capability of the robot to generate motion and can be quantified by the Minimum Singular Value of the Jacobian matrix. To address this issue, this paper proposes an online dynamic force guidance method that integrates performance constraint and optimization mechanisms. Specifically, variable admittance control maintains the operational performance of the robot above a predefined threshold, while a virtual force actively guides the user to drag the robot towards configurations with improved performance. Experiments are conducted on a 6-DOF collaborative robot, comparing three typical paths in the task space. To evaluate the quality of the taught trajectories, trajectory playback experiments are conducted to analyze the relationship between the operational performance of the robot and the work efficiency. The results demonstrate that the proposed method effectively enhances the operational performance of the robot and consequently improves the work efficiency, holding significant value for reducing production takt time in industrial deployment.
