The Position Estimation of Mobile Robot Under Dynamic Environment

Abstract

Localization is most important and necessary technology for mobile robot to work well. The robots need to recognize their position and pose in known environment as well as unknown environment. In the future, the robots will be humanfriendly robots that are able to coexist with humans in dynamic space. The localization includes several restrictions which arise from dynamic obstacles-people, moving chair, and so on. It is desirable for a mobile robot to estimate his position using dynamic obstacles. In this paper, we propose the method for the localization of the mobile robot using a moving object. Throughout the computer simulation experiments, its performance is verified.

Data fusion fault tolerant strategy for a quadrotor UAV under sensors and software faults

“…In [10] a belief function theory data fusion technique is used and adapted to the robot localization problem using ultrasound measurements. As navigation is fundamental for mobile robots, Kalman filters have been used in system localization for a long time [11,12]. As the original Kalman filter can only be applied to linear systems, the extended Kalman filter (EKF) has been proposed for non linear systems.…”

A Fault Tolerant Architecture for Data Fusion Targeting Hardware and Software Faults

“…This is a typical but serious fault in such sensors. We inject the fault at time t i = 59.7 seconds, while the vehicle is still driving in a straight line.Figures 13,14,15,and 16 show that the fault is correctly detected, diagnosed and recovered from at t = 67.3…”

“…In [13] a belief function theory data fusion technique is used and adapted to the robot localization problem using ultrasound images. As navigation is fundamental for mobile robots, Kalman filters have been used also for a long time [14][15] [16]. As the original Kalman filter can only be applied to linear systems, an extended Kalman filter (EKF) has been proposed for non linear systems.…”

Architecture de filtre de Kalman tolérante aux fautes pour la localisation des robots mobiles

“…Dans sa forme originale, le filtre de Kalman utilise un modèle du système pour la prédiction, et un ensemble de données de capteurs pour la correction [13]. Etant donné que la navigation est essentielle pour les robots mobiles, les filtres de Kalman ont pendant longtemps été utilisés dans ce type d'application (Choi et al, 2007) (Freeston et al, 2002), et (Congwei Hu et al, 2003). Comme le filtre de Kalman d'origine ne peut être appliqué qu'à des systèmes linéaires, le filtre de Kalman étendu (EKF) a été proposé pour les systèmes non linéaires.…”