Kinematic and Joint Compliance Modeling Method to Improve Position Accuracy of a Robotic Vision System

Area of Science:

• Robotics
• Automation Engineering
• Computer Vision

Background:

• High position accuracy in robotic vision systems (RVSs) is crucial for industrial automation efficiency.
• Existing methods face challenges in complexity and error accumulation during calibration.

Purpose of the Study:

• To develop a comprehensive modeling approach for enhancing RVS position accuracy.
• To integrate kinematic and joint compliance factors for improved robotic system performance.

Main Methods:

• Developed a unified kinematic model to reduce calibration complexity and error.
• Formulated a joint compliance model considering joint connectors, external loads, and link self-weight.
• Utilized a novel 3D rotary laser sensor for precise error measurement and model calibration.

Main Results:

• Demonstrated significant improvements in the position accuracy of the robotic vision system.
• Validated the proposed models through experiments on a FANUC LR Mate 200iD robot.
• Showcased a streamlined and efficient solution for RVS integration.

Conclusions:

• The integrated kinematic and joint compliance models provide a robust framework for RVS calibration and error compensation.
• This approach offers potential for advancements in high-precision automated tasks.
• The study contributes to more effective and efficient industrial robotic automation.