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Updated: Jan 17, 2026

Operation of the Collaborative Composite Manufacturing CCM System
Published on: October 1, 2019
Research on robot positioning error compensation algorithm based on the Dog Leg and PSONN algorithm
1School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei, China.
This study introduces an industrial robot error compensation algorithm, combining kinematic calibration and advanced prediction methods. The novel approach significantly enhances robot positioning accuracy and reduces localization errors.
Area of Science:
- Robotics
- Control Systems
- Machine Learning
Background:
- Industrial robots exhibit lower absolute positioning accuracy compared to repetitive tasks.
- Existing algorithms often suffer from low accuracy or local optima issues.
Purpose of the Study:
- To propose an advanced error compensation algorithm for industrial robots.
- To improve the absolute positioning accuracy of industrial robots.
Main Methods:
- Kinematic parameter calibration using an enhanced Dog Leg algorithm.
- Odd point error prediction with Particle Swarm Optimization Neural Network (PSONN).
- Positioning error calculation via Spatial Grid Multipoint Interpolation (SGMI).
Main Results:
- Kinematic calibration reduced positioning error from 3.158 mm to 0.406 mm.
- SGMI compensation further decreased positioning error to 0.0685 mm.
- The algorithm effectively calibrated kinematic parameters and reduced uncertainty.
Conclusions:
- The proposed algorithm integrates traditional interpretability with neural network non-linearity.
- It successfully addresses limitations of traditional methods and neural networks.
- The SGMI algorithm significantly enhances industrial robot positioning accuracy.
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