An improved measurement variable estimation model for positioning mobile robot

IF 0.9 4区心理学 Q3 COMMUNICATION

Interaction Studies Pub Date : 2019-07-15 DOI:10.1075/IS.18014.QU

Junsuo Qu, L. Hou, Ruijun Zhang, Zhiwei Zhang, Qipeng Zhang, Kaiming Ting

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引用次数: 2

Abstract

The localization and navigation technology are the key factors in the research of mobile robots. With the demand of smart manufacturing industry and the development of robotics technology, the importance of mobile robot has become increasingly prominent. Mobile robot positioning research is mostly based on odometry, however, it has cumulative errors that would affect the accuracy of positioning results. This paper describes an improved measurement model that suitable from 0° to 180° and used this model in the Extended Kalman Filter (EKF) and Unscented Kalman Filter(UKF) time update step respectively, the method can address the interference of kinematics model predicted position and heading angle, both of them are easily disturbed by noises and other factors. Designing a tracked mobile robot as experimental platform to collect the raw data, conducting experimental research including the performance of hardware platform and autonomous obstacle avoidance, the real-time and stability of remote data interaction, and the accuracy of optimal pose estimation. The simulation results have been verified the accuracy of the improved measurement model applied to UKF.

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一种改进的移动机器人定位测量变量估计模型

定位和导航技术是移动机器人研究的关键因素。随着智能制造业的需求和机器人技术的发展，移动机器人的重要性日益凸显。移动机器人定位研究大多基于里程计，但它存在累积误差，会影响定位结果的准确性。本文介绍了一种适用于0°至180°的改进测量模型，并将该模型分别用于扩展卡尔曼滤波器（EKF）和无迹卡尔曼滤波器（UKF）的时间更新步骤，该方法可以解决运动学模型预测位置和航向角的干扰，这两种干扰都容易受到噪声等因素的干扰。设计了一个跟踪移动机器人作为实验平台来收集原始数据，进行了实验研究，包括硬件平台和自主避障的性能、远程数据交互的实时性和稳定性以及最优姿态估计的准确性。仿真结果验证了改进后的UKF测量模型的准确性。

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

Interaction Studies Multiple-

CiteScore

3.30

自引率

6.70%

发文量

期刊介绍： This international peer-reviewed journal aims to advance knowledge in the growing and strongly interdisciplinary area of Interaction Studies in biological and artificial systems. Understanding social behaviour and communication in biological and artificial systems requires knowledge of evolutionary, developmental and neurobiological aspects of social behaviour and communication; the embodied nature of interactions; origins and characteristics of social and narrative intelligence; perception, action and communication in the context of dynamic and social environments; social learning.