An intelligent calibrator integrating advanced social memory optimization algorithm and improved radial basis function neural network.

Authors

Peiyang Wei, Zhibin Li, Linlin Chen, Hongping Shu, Xun Deng, Tinghui Chen, JianHong Gan, Guodong Li, Shuai Li

Published in

Neural networks : the official journal of the International Neural Network Society. Volume 205. Issue Pt A. Pages 109425. Jul 23, 2026. Epub Jul 23, 2026.

Abstract

Industrial robots are a key component of intelligent manufacturing because they improve productivity, precision, and operational reliability. However, long-term operation inevitably introduces wear and other error sources that reduce absolute positioning accuracy and limit precision tasks. To address this issue, this paper develops a two-stage calibrator that combines the advanced social memory optimization algorithm with a neural network optimized by a gradient-based particle swarm optimization scheme, denoted ASMO-GPSONN. In the proposed framework, ASMO identifies robot kinematic errors through memory-guided global exploration, whereas GPSONN compensates the remaining nonlinear residual errors through gradient-corrected swarm refinement. Experiments on two robot calibration datasets, including a real ABB IRB1100 robot, show that the proposed method achieves the best overall calibration accuracy among the compared algorithms. On the held-out test sets, ASMO-GPSONN attains RMSE values of 0.43 mm on D1 and 0.47 mm on D2, demonstrating its practical effectiveness for robot calibration.

PMID:
42526152
Bibliographic data and abstract were imported from PubMed on 30 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.