Error modeling and parameter identification of a 5-DOF hybrid robot considering angular transmission error

IF 4.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems Pub Date : 2025-06-09 DOI:10.1016/j.robot.2025.105080

Jinyin Zhou , Bin Zhu , Jun Wu , Yanling Tian

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引用次数: 0

Abstract

The forms of angular transmission error are complex and diverse, and it is difficult to derive the error model theoretically. Therefore, the geometric error and angular transmission error are generally studied respectively by neglecting the interference of these errors. In this paper, a novel mixed error model(MEM) combining the fitting model and the theoretical model is derived, which is formed by adding the mixed angular transmission error model to the geometric error model in the form of joint extension positioning error. The mixed angular transmission error model is derived based on the fitting angular transmission error and geometric error interference analysis in uniaxial experiments. In the identification process, the angular transmission error under uniaxial experimental measurement is first measured and fitted, and then the geometric error and angular transmission error are identified simultaneously based on the MEM. A 5-DOF hybrid robot is used as an example to verify the error modeling method and parameter identification process. Based on the error modeling method and parameter identification scheme, the robot’s motion error is dropped by more than 50% compared with the geometric error identification scheme.

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考虑角传动误差的五自由度混合动力机器人误差建模与参数辨识

角传输误差的形式复杂多样，难以从理论上推导出误差模型。因此，一般分别对几何误差和角透射误差进行研究，忽略这两种误差的干扰。本文通过将混合角传递误差模型以关节延伸定位误差的形式加入到几何误差模型中，推导出一种将拟合模型与理论模型相结合的新型混合误差模型（MEM）。在拟合角传输误差和单轴实验中几何误差干涉分析的基础上，推导了混合角传输误差模型。在识别过程中，首先测量并拟合单轴实验测量下的角传输误差，然后基于MEM同时识别几何误差和角传输误差。以五自由度混合动力机器人为例，验证了误差建模方法和参数辨识过程。基于误差建模方法和参数辨识方案，与几何误差辨识方案相比，机器人的运动误差降低了50%以上。

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

Robotics and Autonomous Systems 工程技术-机器人学

CiteScore

9.00

自引率

7.00%

发文量

164

审稿时长

4.5 months

期刊介绍： Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems. Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.