Pose Selection Based on a Hybrid Observation Index for Robotic Accuracy Improvement

IF 2.1 3区工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Machines Pub Date : 2024-07-24 DOI:10.3390/machines12080501

Tiewu Xiang, Chunhui Gao, Baoan Du, Guifang Qiao, Hongfu Zuo

引用次数: 0

Abstract

The problem of the insufficient accuracy performance of industrial robots in high-precision manufacturing is addressed in this paper. Firstly, a kinematic error model based on an M-DH model was presented. Secondly, a hybrid observability index O6 was proposed to select the optimal poses for parameter identification. O6 is the combination of O1 and O3. The optimal poses were obtained by using the IOOPS algorithm. Thirdly, the fitness function for parameter identification was established, and the Levenberg–Marquardt (LM) algorithm was applied for the accurate identification of kinematic parameter errors. Finally, several experiments were conducted to evaluate the performance of the proposed hybrid observability index O6. The average position error and average attitude error of Staubli TX60 robot were reduced by 89% and 49%. The results show that the proposed hybrid observability index O6 has great stability and effectiveness for robot calibration.

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基于混合观测指数的姿势选择，提高机器人精度

本文探讨了工业机器人在高精度制造中精度性能不足的问题。首先，提出了基于 M-DH 模型的运动误差模型。其次，提出了混合可观测性指数 O6，用于选择参数识别的最佳姿势。O6 是 O1 和 O3 的组合。最佳姿势是通过 IOOPS 算法获得的。第三，建立了参数识别的拟合函数，并应用 Levenberg-Marquardt（LM）算法精确识别运动学参数误差。最后，进行了多项实验来评估所提出的混合可观测性指数 O6 的性能。Staubli TX60 机器人的平均位置误差和平均姿态误差分别减少了 89% 和 49%。结果表明，所提出的混合可观测性指数 O6 在机器人校准方面具有很高的稳定性和有效性。

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

Machines Multiple-

CiteScore

3.00

自引率

26.90%

发文量

1012

审稿时长

11 weeks

期刊介绍： Machines (ISSN 2075-1702) is an international, peer-reviewed journal on machinery and engineering. It publishes research articles, reviews, short communications and letters. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. Full experimental and/or methodical details must be provided. There are, in addition, unique features of this journal: *manuscripts regarding research proposals and research ideas will be particularly welcomed *electronic files or software regarding the full details of the calculation and experimental procedure - if unable to be published in a normal way - can be deposited as supplementary material Subject Areas: applications of automation, systems and control engineering, electronic engineering, mechanical engineering, computer engineering, mechatronics, robotics, industrial design, human-machine-interfaces, mechanical systems, machines and related components, machine vision, history of technology and industrial revolution, turbo machinery, machine diagnostics and prognostics (condition monitoring), machine design.