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<p>To improve the absolute positioning accuracy of a six-degree-of-freedom (6-DOF) industrial robot, this study presents a method for predicting and compensating non-geometric errors using an optimally pruned extreme learning machine (OP-ELM), optimized by an enhanced backtracking search algorithm, alongside geometric parameter errors calibration. The proposed approach maps position errors of target points in Cartesian space to joint space and enhances the mutation strategy of the backtracking search algorithm to optimize the construction of an OP-ELM neural network model for joint space error prediction. This methodology enables the prediction and compensation of non-geometric errors for target point positions within the robot’s workspace. Experimental validation of the method was conducted using a 6-DOF industrial robot, demonstrating that the average absolute position error of the robot’s end-effector center point decreased from 1.229&#xa0;mm to 0.164&#xa0;mm, with the maximum absolute position error reduced from 1.938&#xa0;mm to 0.275&#xa0;mm. These results confirm the accuracy and efficacy of the proposed approach.</p>

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Improving the absolute positioning accuracy of industrial robots based on OP-ELM and an enhanced backtracking search algorithm

  • 期刊论文

  • 发表日期：

    2025年1月10日

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  • Haihong Pan,
  • Yukang Cai,
  • Bingqi Jia,
  • Lulu Li,
  • Lin Chen

摘要

To improve the absolute positioning accuracy of a six-degree-of-freedom (6-DOF) industrial robot, this study presents a method for predicting and compensating non-geometric errors using an optimally pruned extreme learning machine (OP-ELM), optimized by an enhanced backtracking search algorithm, alongside geometric parameter errors calibration. The proposed approach maps position errors of target points in Cartesian space to joint space and enhances the mutation strategy of the backtracking search algorithm to optimize the construction of an OP-ELM neural network model for joint space error prediction. This methodology enables the prediction and compensation of non-geometric errors for target point positions within the robot’s workspace. Experimental validation of the method was conducted using a 6-DOF industrial robot, demonstrating that the average absolute position error of the robot’s end-effector center point decreased from 1.229 mm to 0.164 mm, with the maximum absolute position error reduced from 1.938 mm to 0.275 mm. These results confirm the accuracy and efficacy of the proposed approach.