An adaptive framework for robotic polishing based on impedance control
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ORIGINAL ARTICLE
An adaptive framework for robotic polishing based on impedance control Srinivasan Lakshminarayanan 1 & Sreekanth Kana 1 & Dhanya Menoth Mohan 1 & Omey Mohan Manyar 1,2 & David Then 2 & Domenico Campolo 1 Received: 10 September 2020 / Accepted: 16 October 2020 # Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Precise finishing operations such as chamfering and filleting are characterized by relatively low contact forces and low material removal. For such processes, conventional automation approaches like pre-programmed position or force control without adaptations are not suitable to obtain fine surface finishing with high profile accuracy. As a result, polishing tasks are still mainly carried out manually by skilled operators. In this paper, we propose an adaptive framework capable of polishing a wide range of materials including hard metals like titanium using a collaborative robot. We propose an iterative learning controller based on impedance control that adapts both position and forces simultaneously in each iteration to regulate the polishing process. The proposed controller can track the desired profile without any a priori knowledge of the forces required to polish different materials. In addition, we introduce a novel mathematical model to generate the complex filleting toolpath based on Lissajous curves. Trials are carried out in finishing tasks such as chamfering and filleting using a collaborative industrial robot to validate the novel framework. Surface roughness and profile measurements show that our adaptive controller can obtain fine polishing output in various materials such as titanium, aluminum, and wood. Keywords Collaborative robots . Impedance control . Toolpath generation . Robotic polishing . Filleting . Chamfering
1 Introduction Robotic research has witnessed remarkable advances in industrial applications such as spray painting, palletizing, and welding. Such tasks are performed using either simple position-based or force-based control strategies as the interaction between the end effector and the environment is negligible. Despite the numerous advancements in robotics, the tasks that involve physical interactions with the environment in which humans excel are inherently challenging for the robots to carry out autonomously. The subtle positional and force adaptations exhibited by the human operators to compensate for instabilities cannot be captured by pre-programmed Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s00170-02006270-1. * Domenico Campolo [email protected] 1
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
2
Rolls-Royce Plc, Singapore, Singapore
position or force control strategies alone [1, 2]. As a consequence, polishing tasks that take up to 50% of total manufacturing time in the industry [3] still predominantly rely on skilled operators. Notwithstanding the growth, robot finishing constitutes less than 1%
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