基于动态模型的增强扩展卡尔曼滤波用于移动机器人的实时仿真

I. Hassanzadeh, M. Fallah
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引用次数: 3

摘要

针对移动机器人的实时仿真问题,提出了一种基于动态模型的增强扩展卡尔曼滤波器(AEKF)的新设计。动态模型由两个连续的部分组成;线性和角矩的运动学模型和方程。开发了一个Simulink®模型,用于实时移动机器人的同步定位和里程计校准。假设一个绝对测量可用,AEKF提供移动机器人的位置和方向的局部重建与在线里程计校准。此外,给出了机器人的线速度和角速度的估计,可用于控制策略。仿真结果验证了该方法的有效性,为移动机器人的实时控制提供了一种可行的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic model-based augmented extended Kalman filter for real time simulation of mobile robots
This paper presents a new design of augmented extended Kalman filter (AEKF) based on the dynamic model for real-time simulation of mobile robots. The dynamic model is composed of two consecutive parts; kinematic model and equations of linear and angular torques. A Simulink® model is developed for simultaneous localization and odometry calibration of mobile robots in real time manner. Assuming an absolute measurement available, the AEKF provides the local reconstruction of mobile robots position and orientation with an on-line odometry calibration. Furthermore, it gives estimations for the linear and angular velocities of the robot that can be used for control strategy. The simulation results verify the effectiveness of the proposed method and suggest it as a promising way for real time control of mobile robots.
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