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Sonar-based robot navigation using nonlinear-robust Kalman filter

机译:基于非线性鲁棒卡尔曼滤波器的基于声纳的机器人导航

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In this paper, we address the sonar-based navigation of mobile robots by using Kalman filtering. The extended Kalman filtering (EKF) technique is considered. For this problem, we present results on the robustness of the nonlinear observation scheme. The original feature is that the region-of-convergence question is posed in its complete nonlinear framework, that is, considering the dynamics not only of the estimation error ζ(t), but also of the covariance matrix P(t). In this way, and compared to previous results in the literature, the approach followed makes more rigorous the treatment and facilitates the convergence analysis. The proposed ideas were tested successfully on simulation experiments of a mobile platform.
机译:在本文中,我们通过使用卡尔曼滤波解决了基于声纳的移动机器人导航。考虑了扩展卡尔曼滤波(EKF)技术。对于这个问题,我们提出了非线性观测方案的鲁棒性的结果。最初的特征是将收敛区域问题置于其完整的非线性框架中,也就是说,不仅要考虑估计误差ζ(t)的动力学,而且还要考虑协方差矩阵P(t)的动力学。这样,与文献中的先前结果相比,所采用的方法使处理更加严格,并有助于收敛性分析。在移动平台的仿真实验中成功地测试了所提出的想法。

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