Positioning Method of Four-Wheel-Steering Mobile Robots Based on Improved UMBmark of Michigan Benchmark Algorithm

IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Advanced Computational Intelligence and Intelligent Informatics Pub Date : 2023-03-20 DOI:10.20965/jaciii.2023.p0135

Dianjun Wang, Meng Xu, Ya Chen, Haoxiang Zhong, Y. Zhu, Zilong Wang, Linlin Gao

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引用次数: 0

Abstract

To reduce the error of the odometer positioning system and improve the positioning accuracy of four-wheel-steering mobile robots, three types of coupling errors are considered, based on the University of Michigan Benchmark (UMBmark) method: unequal track width, unequal wheel diameter, and speed difference of ipsilateral wheels. A “dual direction square path experiment” is designed to decouple the error, a new system error model is defined, and an improved UMBmark method for a four-wheel mobile robot is proposed. In the mobile robot positioning system, a laser tracker is used to measure the absolute positions of the starting and ending points of the robot. The positioning test results of the robot using the improved UMBmark method show that the odometer system error is 69.103 mm, which is 2.6 times less than that in the traditional UMBmark method. Hence, the improved UMBmark can better compensate for the system error of four-wheel-steering mobile robots.

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基于改进的密歇根基准算法的四轮转向移动机器人定位方法

为了减小里程表定位系统的误差，提高四轮转向移动机器人的定位精度，基于密歇根大学基准(ummark)方法，考虑了三种耦合误差:履带宽度不等、车轮直径不等和同侧车轮速度差。设计了“双向方形路径实验”来解耦误差，定义了新的系统误差模型，提出了一种改进的四轮移动机器人UMBmark方法。在移动机器人定位系统中，激光跟踪仪用于测量机器人起点和终点的绝对位置。采用改进的UMBmark方法对机器人进行了定位测试，结果表明，里程表系统误差为69.103 mm，比传统的UMBmark方法减小了2.6倍。因此，改进的UMBmark能够更好地补偿四轮转向移动机器人的系统误差。

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 Advanced Computational Intelligence and Intelligent Informatics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

1.50

自引率

14.30%

发文量

期刊介绍： JACIII focuses on advanced computational intelligence and intelligent informatics. The topics include, but are not limited to; Fuzzy logic, Fuzzy control, Neural Networks, GA and Evolutionary Computation, Hybrid Systems, Adaptation and Learning Systems, Distributed Intelligent Systems, Network systems, Multi-media, Human interface, Biologically inspired evolutionary systems, Artificial life, Chaos, Complex systems, Fractals, Robotics, Medical applications, Pattern recognition, Virtual reality, Wavelet analysis, Scientific applications, Industrial applications, and Artistic applications.