Purpose

The purpose of this paper is to transfer the impedance regulation of manual belt grinding to robot belt grinding control.

Design/methodology/approach

This paper presents a novel methodology for transmitting human impedance regulation skills to robot control in robot belt grinding. First, according to the human grinding experimental data, the skilled worker’s arm impedance regulation is calculated. Next, the human skills are encapsulated as the statistical learning model where the kernel parameters are learned from the demonstration data by Gaussian process regression (GPR) algorithms. The desired profiles of robot are generated by the task planner based on the learned skill knowledge model. Lastly, the learned skill knowledge model is integrated with an adaptive hybrid position-force controller over the trajectory and force of end-effector in robot belt grinding task.

Findings

Manual grinding skills are represented and transferred to robot belt grinding for higher grinding quality of the workpiece.

Originality/value

The impedance of the manual grinding is estimated by k-means++ algorithm at different grinding phases. Manual grinding skills (e.g. trajectory, impedance regulation) are represented and modeled by GMM and GPR algorithms. The desired trajectory, force and impedance of robot are generated by the planner based on the learned skills knowledge model. An adaptive hybrid position-force controller is designed based on learned skill knowledge model. This paper proposes a torque-tracking controller to suppress the vibration in robot belt grinding process.

中文翻译：

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从人类演示中学习机器人砂带磨削的阻抗调节技巧

目的

本文的目的是将手动砂带磨削的阻抗调节转化为机器人砂带磨削控制。

设计/方法/方法

本文提出了一种将人体阻抗调节技能传递到机器人砂带磨削中的机器人控制的新方法。首先，根据人体磨削实验数据，计算熟练工人的手臂阻抗调节。接下来，将人类技能封装为统计学习模型，其中通过高斯过程回归 (GPR) 算法从演示数据中学习内核参数。任务规划器根据学习到的技能知识模型生成所需的机器人配置文件。最后，将学习到的技能知识模型与自适应混合位置力控制器集成到机器人砂带磨削任务中末端执行器的轨迹和力上。

发现

手工磨削技能得到体现，并转移到机器人砂带磨削中，以提高工件的磨削质量。

原创性/价值

在不同的研磨阶段，通过k-means++算法估计手动研磨的阻抗。手动磨削技能（例如轨迹、阻抗调节）由 GMM 和 GPR 算法表示和建模。机器人所需的轨迹、力和阻抗由规划器根据学习到的技能知识模型生成。基于习得技能知识模型设计了一种自适应混合位置力控制器。本文提出了一种扭矩跟踪控制器来抑制机器人砂带磨削过程中的振动。