A Kinematic Calibration Method Based on Residual Network Combining Joint Angles and Robot Pose
2023 5th International Conference on Robotics and Computer Vision (ICRCV)(2023)
Abstract
Robot performance metrics like their absolute positioning accuracy have a significant impact on their industrial applications. This research introduces a kinematic calibration approach for industrial robots based on the residual network that combines joint angles and robot pose. To compensate for the geometric error, a geometric parameter identification algorithm founded on the MDH model and error model is suggested. The residual network uses joint angles and robot pose as network inputs to compensate for non-geometric error together with the results of geometric parameter identification. Experimental validation is conducted on the Rokae XB7S robot, with a dataset constructed from joint angle changes and robot pose variations. The experimental results show that the robot’s position error decreases from $0.6549\mathrm{~mm}$ to $0.1011\mathrm{~mm}$ after compensation, verifying the suggested approach’s efficiency.
MoreTranslated text
Key words
Data-driven,Error modeling,Geometric parameters,Industrial robot,Kinematic calibration,Residual network
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined