A Robotic Grinding Motion Planning Methodology for a Novel Automatic Seam Bead Grinding Robot Manipulator

Guo, Wanjin; Zhu, Yaguang; He, Xu

doi:10.1109/access.2020.2987807

Cited by 23 publications

(5 citation statements)

References 43 publications

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“…Ge et al used robots and line laser sensors to realize the welding seam of the steel pipe workpiece via online measurement and removal [14]. Guo et al used robots and line laser sensors to measure and remove spiral welds on steel pipes [15]. Among them, it can be found that due to its limited accuracy, 3D cameras are often used in applications where precision requirements such as deburring and polishing are not high.…”

Section: Related Workmentioning

confidence: 99%

A Robotic Milling System Based on 3D Point Cloud

Gao

Bai

et al. 2021

Machines

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Industrial robots have advantages in the processing of large-scale components in the aerospace industry. Compared to CNC machine tools, robot arms are cheaper and easier to deploy. However, due to the poor consistency of incoming materials, large-scale and lightweight components make it difficult to automate robotic machining. In addition, the stiffness of the tandem structure is quite low. Therefore, the stability of the milling process is always a concern. In this paper, the robotic milling research is carried out for the welding pre-processing technology of large-scale components. In order to realize the automatic production of low-conformity parts, the on-site measurement–planning–processing method is adopted with the laser profiler. On the one hand, the laser profiler hand–eye calibration method is optimized to improve the measurement accuracy. On the other hand, the stiffness of the robot’s processing posture is optimized, combined with the angle of the fixture turntable. Finally, the experiment shows the feasibility of the on-site measurement–planning–processing method and verifies the correctness of the stiffness model.

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Section: Related Workmentioning

confidence: 99%

A Robotic Milling System Based on 3D Point Cloud

Gao

Bai

et al. 2021

Machines

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“…Traditional manual grinding not only has uneven grinding quality and low efficiency but also generates dust during the grinding process, which is hazardous to workers' physical and mental health. In contrast, robotic grinding can replace manual grinding and achieve automation [1]. Maintaining a constant grinding force during robotic grinding not only ensures the accuracy and consistency of the surface quality of the workpiece but also reduces the wear and tear on the robot and end-effector [3].…”

Section: Introductionmentioning

confidence: 99%

Constant Force Grinding Controller for Robots Based on Soft-Actor-Critic Optimal Parameter Finding Algorithm

Rei,

Wang,

Chen

et al. 2024

Preprint

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Since conventional PID (Proportional-Integral-Derivative) controllers hardly control the robot to stabilize for constant force grinding under changing environmental conditions, it is necessary to add a compensation term to conventional PID controllers. An optimal parameter finding algorithm based on SAC(Soft-Actor-Critic) is proposed to solve the problem that the compensation term parameters are difficult to obtain, including training state action and normalization preprocessing, reward function design, and targeted deep neural network design. The algorithm is used to find the optimal controller compensation term parameters and applied to the PID controller to complete the compensation through the inverse kinematics of the robot to achieve constant force grinding control. To verify the algorithm's feasibility, a simulation model of a grinding robot with sensible force information is established, and the simulation results show that the controller trained with the algorithm can achieve constant force grinding of the robot. Finally, the robot constant force grinding experimental system platform is built for testing, which verifies the control effect of the optimal parameter finding algorithm on the robot constant force grinding and has specific environmental adaptability.

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“…In recent years, robot manipulators have been widely used in many fields, such as manufacturing [ 1 ], assembly [ 2 ], grinding [ 3 ], and drilling [ 4 ]. However, these robotic arms are almost always mounted on fixed bases, significantly limiting their working space.…”

Section: Introductionmentioning

confidence: 99%

Compliant Contact Force Control for Aerial Manipulator of Adaptive Neural Network-Based Robust Control

Fang,

Mao

2024

Sensors

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Aerial manipulators expand the application scenarios of manipulators into the air. To complete various operations, the contact force between the aerial manipulator and the target must be precisely controlled. In this study, we first established the mathematical models of the multirotor and the manipulator separately. Their mutual influence is regarded as each other’s disturbance, and the overall linkage mechanism is established through analysis. Then, a robust sliding mode control strategy is developed for accurate trajectory tracking. The controller is derived from Lyapunov theory, which can ensure the stability of the closed-loop system. To compensate for the effect of system uncertainty, an adaptive radial basis function neural network is devised to approximate the part of the controller containing the model information. In addition, an impedance controller is designed to convert force control into position control to make the manipulator contact with the target compliantly. Finally, the simulation and experimental results indicate that the proposed method can guarantee the accuracy of the contact force and has good robustness.

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A Robotic Grinding Motion Planning Methodology for a Novel Automatic Seam Bead Grinding Robot Manipulator

Cited by 23 publications

References 43 publications

A Robotic Milling System Based on 3D Point Cloud

A Robotic Milling System Based on 3D Point Cloud

Constant Force Grinding Controller for Robots Based on Soft-Actor-Critic Optimal Parameter Finding Algorithm

Compliant Contact Force Control for Aerial Manipulator of Adaptive Neural Network-Based Robust Control

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