A Survey of Robotic Polishing

Li, Jian; Zhang, Tao; Liu, Xineng; Guan, Yisheng; Wang, Danwei

doi:10.1109/robio.2018.8664890

Cited by 33 publications

(13 citation statements)

References 66 publications

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“…They are expensive and relatively difficult to implement. Besides, a specific device generates less stiffness and raise weight in the end effector [28].…”

Section: Introductionmentioning

confidence: 99%

Force Control Improvement in Collaborative Robots through Theory Analysis and Experimental Endorsement

2020

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Due to the elasticity of their joints, collaborative robots are seldom used in applications with force control. Besides, the industrial robot controllers are closed and do not allow the user to access the motor torques and other parameters, hindering the possibility of carrying out a customized control. A good alternative to achieve a custom force control is sending the output of the force regulator to the robot controller through motion commands (inner/outer loop control). There are different types of motion commands (e.g., position or velocity). They may be implemented in different ways (Jacobian inverse vs. Jacobian transpose), but this information is usually not available for the user. This article is dedicated to the analysis of the effect of different inner loops and their combination with several external controllers. Two of the most determinant factors found are the type of the inner loop and the stiffness matrix. The theoretical deductions have been experimentally verified on a collaborative robot UR3, allowing us to choose the best behaviour in a polishing operation according to pre-established criteria.

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“…They are expensive and relatively difficult to implement. Besides, a specific device generates less stiffness and raise weight in the end effector [28].…”

Section: Introductionmentioning

confidence: 99%

Force Control Improvement in Collaborative Robots through Theory Analysis and Experimental Endorsement

2020

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“…Thereby, Moore-Penrose pseudo-inverse is used in (2). b is the zero-space vector of J; I is the identity matrix.…”

Section: Robot Macro Motion Controlmentioning

confidence: 99%

“…In order to solve the shortcomings of manual polishing and the advantages of applying industrial robots, the development of grinding and polishing robot systems and other types of automatic polishing equipment has received extensive attention. 2 Grinding and polishing are typical contact processing, which is different from non-contact operations such as spraying. The contact between the end effector and the surface of the workpiece will generate force, which affects the contact stress between the polishing tool and the workpiece.…”

Section: Introductionmentioning

confidence: 99%

A hybrid control strategy for grinding and polishing robot based on adaptive impedance control

Zhou

Wang

et al. 2021

Advances in Mechanical Engineering

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In order to realize the active and compliant motion of the robot, it is necessary to eliminate the impact caused by processing contact. A hybrid control strategy for grinding and polishing robot is proposed based on adaptive impedance control. Firstly, an electrically driven linear end effector is designed for the robot system. The macro and micro motions control model of the robot is established, by using impedance control method, which based on the contact model of the robot system and the environment. Secondly, the active compliance method is adopted to establish adaptive force control and position tracking control strategies under impact conditions. Finally, the algorithm is verified by Simulink simulation and experiment. The simulation results are as follows: The position tracking error does not exceed 0.009 m, and the steady-state error of the force is less than 1 N. The experimental results show that the motion curve coincides with the surface morphology of the workpiece, and the contact force is stable at 10 ± 3 N. The algorithm can realize more accurate position tracking and force tracking, and provide a reference for the grinding and polishing robot to realize surface processing.

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“…In [15], an active end effector-based force control system is devised for robotic deburring. Although the active compliance control that uses an actuator is very good resulting in high accuracy, there are difficulties in designing these special compliant polishing tool [16]. Further, excessive compliance of the tool will decrease the stiffness during the polishing and makes the profile tracking less accurate.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the robot control, manual programming of the robot is highly cumbersome and needs highly skilled operators [41]. Since the polishing posture and the toolpath have an enormous effect on the efficiency of the overall polishing operation as well on the surface integrity of the workpiece [16], SMEs often have to rely on either inefficient manual programming or expensive vision sensors [42]. To address this challenge, we propose a novel mathematical model to generate a toolpath in real time for filleting process based on Lissajous curves that do not require any external sensors.…”

Section: Introductionmentioning

confidence: 99%

An adaptive framework for robotic polishing based on impedance control

Lakshminarayanan

Kana

Mohan

et al. 2020

Int J Adv Manuf Technol

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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.

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A Survey of Robotic Polishing

Cited by 33 publications

References 66 publications

Force Control Improvement in Collaborative Robots through Theory Analysis and Experimental Endorsement

Force Control Improvement in Collaborative Robots through Theory Analysis and Experimental Endorsement

A hybrid control strategy for grinding and polishing robot based on adaptive impedance control

An adaptive framework for robotic polishing based on impedance control

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