Xue Xianfa, Huang Haohui, Zuo Lei, Wang Ning
Key Laboratory of Autonomous Systems and Networked Control, School of Automation Science and Engineering, South China University of Technology, Guangzhou, China.
Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
Front Neurorobot. 2022 Mar 23;16:865187. doi: 10.3389/fnbot.2022.865187. eCollection 2022.
To meet the enormous demand for smart manufacturing, industrial robots are playing an increasingly important role. For industrial operations such as grinding 3C products, numerous demands are placed on the compliant interaction ability of industrial robots to interact in a compliant manner. In this article, an adaptive compliant control framework for robot interaction is proposed. The reference trajectory is obtained by single-point demonstration and DMP generalization. The adaptive feedforward and impedance force controller is derived in terms of position errors, and they are input into an admittance controller to obtain the updated amount of position deviation. The compliant interaction effect is achieved, which is shown that the grinding head fits on the curved surface of a computer mouse, and the interaction force is within a certain expected range in the grinding experiment based on the performance an Elite robot. A comparative experiment was conducted to demonstrate the effectiveness of the proposed framework in a more intuitive way.
为了满足智能制造的巨大需求,工业机器人正发挥着越来越重要的作用。对于诸如研磨3C产品之类的工业操作,对工业机器人以柔顺方式进行交互的柔顺交互能力提出了诸多要求。本文提出了一种用于机器人交互的自适应柔顺控制框架。参考轨迹通过单点演示和DMP泛化获得。根据位置误差推导出自适应前馈和阻抗力控制器,并将它们输入到导纳控制器中以获得位置偏差的更新量。实现了柔顺交互效果,基于一台Elite机器人的性能,在研磨实验中表明磨头贴合在电脑鼠标的曲面上,并且交互力在一定的预期范围内。进行了对比实验以更直观地证明所提出框架的有效性。