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Kalman Filter based position estimation using #x0022;optical mouse movement sensor#x0022; and differential drive robot model

机译:使用“光学鼠标移动传感器”和差动驱动机器人模型的基于卡尔曼滤波器的位置估计

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摘要

This article describes the creation of a mathematical model for the kinematics, dynamics and electronics of a two-wheel-steered robot. As a result, it is possible to use a previously created, potential field-based and fuzzy navigation-based robot control system ([1], [2], [3], [4]) with two-wheel-driven robots as well. Using the results presented in this article the current location and the driven path can be estimated more accurately. This can be achieved by using the Kalman Filter with the wheel encoder data and using Optical Flow-based movement measurement devices that are similar to the ones known from the optical mouse peripherals. The established equations define the bases for controlling and navigating robots in indoor environments (flat surface, no sliding). According to these, they reflect the kinematics of the ground unit, the mathematical model of the electronic motor, and also the models of the sensors installed on the robot (odometer and optical mouse movement sensor).
机译:本文介绍了如何为两轮转向机器人的运动学,动力学和电子学建立数学模型。结果,可以将先前创建的基于势场和基于模糊导航的机器人控制系统([1],[2],[3],[4])与两轮驱动机器人一起使用。出色地。使用本文中介绍的结果,可以更准确地估计当前位置和驱动路径。这可以通过将Kalman滤波器与滚轮编码器数据一起使用,以及使用基于光学流的运动测量设备来实现,该设备类似于从光学鼠标外围设备中已知的设备。建立的方程式定义了在室内环境(平坦表面,无滑动)中控制和导航机器人的基础。据此,它们反映了地面单元的运动学,电子马达的数学模型以及安装在机器人上的传感器(里程表和光学鼠标移动传感器)的模型。

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