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Ultrasonic Localization and Pose Tracking of an Autonomous Mobile Robot via Fuzzy Adaptive Extended Information Filtering

机译:基于模糊自适应扩展信息滤波的自主移动机器人超声定位与姿态跟踪

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摘要

This paper presents methodologies and technologies for ultrasonic localization and pose tracking of an autonomous mobile robot (AMR) by using a fuzzy adaptive extended information filtering (FAEIF) scheme. A novel ultrasonic localization system, which consists of two ultrasonic transmitters and three receivers, is proposed to estimate both the static and the dynamic position and orientation of the AMR. FAEIF is presented to improve estimation accuracy and robustness for the proposed localization system, while the system lacks sufficient information of complete models or the process and measurement noise varies with time. Static pose estimation that utilizes the averaging approach is also investigated. Six time-of-flight ultrasonic measurements, together with the vehicle''s dead-reckoned localization information, are merged to update the vehicle''s pose by utilizing the FAEIF sensor fusion algorithm. The proposed algorithms were implemented using an industrial personal computer with a computation speed of 800 MHz and standard C++ programming techniques. The system prototype, together with computer simulations and experimental results, was used to confirm that the system not only provides precise estimation of both the static and dynamic pose of the AMR but also provides a simple economical structure for navigational use and installation/calibration.
机译:本文介绍了使用模糊自适应扩展信息过滤(FAEIF)方案对自主移动机器人(AMR)进行超声定位和姿态跟踪的方法和技术。提出了一种新颖的超声定位系统,该系统由两个超声发射器和三个接收器组成,用于估计AMR的静态和动态位置及方向。提出了FAEIF来提高所提出的定位系统的估计准确性和鲁棒性,而该系统缺乏完整模型的足够信息,或者过程和测量噪声会随时间变化。还研究了利用平均方法的静态姿态估计。通过使用FAEIF传感器融合算法,六个飞行时间的超声测量结果以及车辆的死机定位信息被合并以更新车辆的姿态。所提出的算法是使用具有800 MHz计算速度的工业个人计算机和标准C ++编程技术来实现的。该系统原型连同计算机仿真和实验结果被用于确认该系统不仅可以提供AMR静态和动态姿态的精确估计,而且还可以为导航使用和安装/校准提供简单的经济结构。

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