Title :
A stochastic approach of mobile robot navigation using customized RFID systems
Author :
Miah, M. Suruz ; Gueaieb, Wail
Author_Institution :
Sch. of Inf. Technol. & Eng., Univ. of Ottawa, Ottawa, ON, Canada
Abstract :
Operating a mobile robot using the signal strength of a Radio Frequency (RF) system and/or line-of-sight distances to other known points or RF stations is a challenging task. This problem has been traditionally solved by several approaches suggested in the literature. Among the most common shortcomings of those approaches are the use of excessive number of sensors or multiple reference RF stations for the robot to estimate its location in an indoor environment. The current manuscript outlines two different aspects of a mobile robot navigation problem in an indoor environment using Received Signal Strength (RSS) of a customized Radio Frequency IDentification (RFID) system. First, the robot´s current location is estimated by a trilateration method where the localization problem is solved through a geometric approach based on Cayley-Menger determinants. The robot position is then better estimated by the application of conventional stochastic filters such as the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Second, the problem is explored by a set of points on the ground defining a desired path along which a mobile robot is supposed to navigate. The proposed robot navigation system is validated through a number of computer simulation for testbeds of various complexities.
Keywords :
Kalman filters; mobile robots; radiofrequency identification; radionavigation; stochastic processes; Cayley-Menger determinant; conventional stochastic filter; customized RFID system; extended Kalman filter; geometric approach; line-of-sight distance; mobile robot navigation; radiofrequency identification; received signal strength; stochastic approach; trilateration method; unscented Kalman filter; Indoor environments; Mobile robots; Motion planning; RF signals; Radio frequency; Radio navigation; Radiofrequency identification; Robot sensing systems; Signal processing; Stochastic systems; Cayley-Menger determinant; Extended Kalman Filter; Mobile robot navigation; Unscented Kalman Filter; localization; received signal strength;
Conference_Titel :
Signals, Circuits and Systems (SCS), 2009 3rd International Conference on
Conference_Location :
Medenine
Print_ISBN :
978-1-4244-4397-0
Electronic_ISBN :
978-1-4244-4398-7
DOI :
10.1109/ICSCS.2009.5412288