Adaptive Sliding Mode Control for Robotic Surface Treatment Using Force Feedback

Abstract

This work presents a hybrid position-force control of robots in order to apply surface treatments such as polishing, grinding, finishing, deburring, etc. The robot force control is designed using sliding mode concepts to benefit from robustness. In particular, the sliding mode force task is defined using equality constraints to attain the desired tool pressure on the surface, as well as to keep the tool orientation perpendicular to the surface. In order to deal with sudden changes in material stiffness, which are ultimately transferred to the polishing tool and can produce instability and compromise polishing performance, several adaptive switching gain laws are considered and compared. Moreover, a lower priority tracking controller is defined to follow the desired reference trajectory on the surface being polished. Hence, deviations from the reference trajectory are allowed if such deviations are required to satisfy the constraints mentioned above. Finally, a third-level task is also considered for the case of redundant robots in order to use the remaining degrees of freedom to keep the manipulator close to the home configuration with safety in mind. The main advantages of the method are increased robustness and low computational cost. The applicability and effectiveness of the proposed approach is substantiated by experimental results using a redundant 7R manipulator: the Rethink Robotics Sawyer collaborative robot.

Position/Force Control of Manipulator in Contact with Flexible Environment

“…The number of industrial applications, in which robots come in contact with the environment and where it has a significant influence on the robotised processes realisation quality, is constantly increasing ( Mendes and Neto, 2015). The aforementioned processes include, among others, robotised mechanical processing such as grinding (Zhu et al, 2015), polishing (Gracia et al, 2018;Tian et al, 2016), or edge deburring (Burghardt et al, 2017b). Therefore, modelling and control of robots in interaction with the environment becomes crucial, particularly if the actual features of the environment are taken into account, such as flexibility or damping.…”

Disturbance Observer‐Based Robot End Constant Contact Force‐Tracking Control

“…For example, in the case of too large external disturbance, the convergence speed is slow, especially for the constant force control in the applications such as grinding processing, which may lead to large uctuations of the contact force. In recent years, scholars have proposed many force control methods that integrate both the classic methods and the intelligent algorithms, such as fuzzy control and neural network control [3][4][5][6][7][8][9]. Panwar and Sukavanam et al [4] used a feedforward neural network to compensate the uncertainty of the robot model and presented an optimized force/position hybrid control method.…”

“…Nagata et al [6] used a neural network to nely adjust the ideal damping, which helped to obtain nonlinear e ective sti ness and improve the surface treatment performance of a metal mold. In References [7] and [8], a method combining the sliding mode control with the force/position hybrid control was proposed. e sliding mode control guaranteed trajectory tracking robustness while allowing the robot to slide over unknown obstacles.…”

A Robotic Deburring Methodology for Tool Path Planning and Process Parameter Control of a Five-Degree-of-Freedom Robot Manipulator

“…An application of a human mimicking control strategy that mimics the human behavior during the manual deburring on the deburring of hard material items using an industrial robot was introduced [30]. By satisfying a set of constraints to properly perform the desired surface contact conditioning, a hybrid position/force control approach using task priority and sliding mode control was proposed for contact-driven robotic surface treatments such as deburring [31,32]. A set of optimal process parameter combination for robotic machining and the effect of process parameters such as spindle speed, feed rate and tool path strategies on the performance characteristics were investigated using the Taguchi-Grey relational approach and analysis of variance [33].…”

“…The tool path adaptation for robotic deburring implemented usually needs to supplement extra equipment or processes, e.g., using a vision system described in [27,36] or direct teaching [29]. Other studies on the process parameter control for robotic deburring have certain constraints, e.g., the deburring tool is an abrasive diamond disc in the control strategy for the process parameter control, and other deburring tools are not considered [30]; the designed control action and implementation are more intricate, such as [25,31,32,34,36]; the procedure of the proposed approach is more complicated, such as [33]; or detailed deburring process parameters such as robotic feed and spindle speed for the deburring industrial robot are not considered [35].…”