Highly Accurate Robot Calibration Using Adaptive and Momental Bound with Decoupled Weight Decay

TL;DR

This paper introduces a novel robot calibration algorithm combining adaptive moment estimation with decoupled weight decay, significantly improving accuracy and reliability in industrial robot positioning.

Contribution

The paper proposes AdaModW, a new algorithm that integrates adaptive and momental bounds with decoupled weight decay for enhanced robot calibration.

Findings

Outperforms existing algorithms in calibration accuracy

Demonstrates high convergence rate and stability

Improves generalization in robot calibration tasks

Abstract

Within the context of intelligent manufacturing, industrial robots have a pivotal function. Nonetheless, extended operational periods cause a decline in their absolute positioning accuracy, preventing them from meeting high precision. To address this issue, this paper presents a novel robot algorithm that combines an adaptive and momental bound algorithm with decoupled weight decay (AdaModW), which has three-fold ideas: a) adopting an adaptive moment estimation (Adam) algorithm to achieve a high convergence rate, b) introducing a hyperparameter into the Adam algorithm to define the length of memory, effectively addressing the issue of the abnormal learning rate, and c) interpolating a weight decay coefficient to improve its generalization. Numerous experiments on an HRS-JR680 industrial robot show that the presented algorithm significantly outperforms state-of-the-art algorithms in robot calibration performance. Thus, in light of its reliability, this algorithm provides an efficient way to address robot calibration concerns.