Investigation on intermittent observation in mobile robot localization with fuzzy logic technique

2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) Pub Date : 2016-10-01 DOI:10.1109/I2CACIS.2016.7885282

H. Ahmad, N. A. Othman

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引用次数: 1

Abstract

This paper deals with an analysis of intermittent observations for mobile robot localization with Fuzzy Logic approach. Mobile robot can easily lost its sight during environment observations due to several factors such as sensor faulty, and dynamic conditions. This can lead to erroneous estimation and the mobile robot become uncertain about its position. As a solution to this issue, this paper proposed a study on Fuzzy Logic technique to overcome such problem considering the Extended Kalman Filter(EKF) measurement innovation characteristic. The rules and fuzzy sets are designed such that it preserved good estimation whenever the relative angle and its relative distance measurements suddenly becomes larger than the previous measurements. The simulation results discusses two different cases observing the performance of the proposed technique. The results show that EKF with Fuzzy Logic technique is able to deal with intermittent observations if the design takes proper analysis and consideration on the measurement innovations.

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模糊逻辑技术在移动机器人定位中的间歇观测研究

本文用模糊逻辑方法对移动机器人定位中的间歇观测进行了分析。由于传感器故障、动态环境等因素，移动机器人在环境观测过程中容易失明。这可能导致错误的估计和移动机器人变得不确定自己的位置。针对这一问题，本文结合扩展卡尔曼滤波(EKF)测量创新特性，提出了模糊逻辑技术的研究，以克服这一问题。设计了规则和模糊集，使其在相对角度和相对距离的测量值突然大于之前的测量值时保持良好的估计。仿真结果讨论了两种不同的情况，观察了所提技术的性能。结果表明，如果在设计中对测量创新进行了适当的分析和考虑，模糊逻辑EKF技术能够处理间歇观测。

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

2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

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