Enhancing Robot Calibration through Reliable High-Order Hermite Polynomials Model and SSA-BP Optimization

IF 2.6 3区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computing and Information Science in Engineering Pub Date : 2023-07-25 DOI:10.1115/1.4063035

Yujie Zhang, Qi Fang, Yu Xie, Weijie Zhang, Runxiang Yu

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引用次数: 0

Abstract

Various sources of error can lead to the position accuracy of the robot being orders of magnitude worse than its repeatability. For the accuracy of drilling in the aviation field, high-precision assembly, and other fields depend on the industrial robot's absolute positioning accuracy, it is essential to improve the accuracy of absolute positioning by calibration. In the present paper, an error model of the robot is established considering both constant and joint-dependent kinematic errors, and the robot model is modified by the Hermite polynomial. To identify joint-dependent kinematic errors, a robot calibration method based on back-propagation neural network(BP) optimized by Sparrow Search Algorithm (SSA-BP) is proposed, which optimize the uncertainty of weights and thresholds in the BP algorithm . To validate the efficiency of the proposed method, experiments on an EFORT ECR5 robot were implemented. The positioning error is reduced from 3.1704 mm to 0.2798 mm, and the positioning accuracy is improved by 91.27%. With the new calibration method using SSA-BP, robot positioning errors can be effectively compensated for and the robot positioning accuracy can be improved significantly.

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通过可靠的高阶埃尔米特多项式模型和SSA-BP优化增强机器人标定

各种误差来源可能导致机器人的位置精度比其可重复性差几个数量级。航空领域的钻孔、高精度装配等领域的精度依赖于工业机器人的绝对定位精度，因此通过标定提高绝对定位精度至关重要。本文建立了考虑常量运动误差和关节相关运动误差的误差模型，并用Hermite多项式对模型进行了修正。为了识别关节相关运动误差，提出了一种基于麻雀搜索算法(SSA-BP)优化的反向传播神经网络(BP)的机器人标定方法，该方法优化了BP算法中权值和阈值的不确定性。为了验证该方法的有效性，在EFORT ECR5机器人上进行了实验。定位误差由3.1704 mm减小到0.2798 mm，定位精度提高了91.27%。采用基于SSA-BP的标定方法，可以有效补偿机器人的定位误差，显著提高机器人的定位精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computing and Information Science in Engineering 工程技术-工程：制造

CiteScore

6.30

自引率

12.90%

发文量

100

审稿时长

6 months

期刊介绍： The ASME Journal of Computing and Information Science in Engineering (JCISE) publishes articles related to Algorithms, Computational Methods, Computing Infrastructure, Computer-Interpretable Representations, Human-Computer Interfaces, Information Science, and/or System Architectures that aim to improve some aspect of product and system lifecycle (e.g., design, manufacturing, operation, maintenance, disposal, recycling etc.). Applications considered in JCISE manuscripts should be relevant to the mechanical engineering discipline. Papers can be focused on fundamental research leading to new methods, or adaptation of existing methods for new applications. Scope: Advanced Computing Infrastructure; Artificial Intelligence; Big Data and Analytics; Collaborative Design; Computer Aided Design; Computer Aided Engineering; Computer Aided Manufacturing; Computational Foundations for Additive Manufacturing; Computational Foundations for Engineering Optimization; Computational Geometry; Computational Metrology; Computational Synthesis; Conceptual Design; Cybermanufacturing; Cyber Physical Security for Factories; Cyber Physical System Design and Operation; Data-Driven Engineering Applications; Engineering Informatics; Geometric Reasoning; GPU Computing for Design and Manufacturing; Human Computer Interfaces/Interactions; Industrial Internet of Things; Knowledge Engineering; Information Management; Inverse Methods for Engineering Applications; Machine Learning for Engineering Applications; Manufacturing Planning; Manufacturing Automation; Model-based Systems Engineering; Multiphysics Modeling and Simulation; Multiscale Modeling and Simulation; Multidisciplinary Optimization; Physics-Based Simulations; Process Modeling for Engineering Applications; Qualification, Verification and Validation of Computational Models; Symbolic Computing for Engineering Applications; Tolerance Modeling; Topology and Shape Optimization; Virtual and Augmented Reality Environments; Virtual Prototyping

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