Fuzzy Adaptive Particle Filter for Localization of a Mobile Robot

Localization is one of the important topics in robotics and it is essential to execute a mission. Most problems in the class of localization are due to uncertainties in the modeling and sensors. Therefore, various filters are developed to estimate the states in noisy information. Recently, particle filter is issued widely because it can be applied to a nonlinear model and a non-Gaussian noise. In this paper a fuzzy adaptive particle filter is proposed, whose basic idea is to generate samples at the high-likelihood using a fuzzy logic approach. The method brings out the improvement of an accuracy of estimation. In addition, this paper presents the localization method for a mobile robot with ultrasonic beacon systems. For comparison purposes, we test a conventional particle filter method and our proposed method. Experimental results show that the proposed method has better localization performance.

• Type: Conference
• Year: 2007
• Where: KES
• Authors: Young-Joong Kim, Chan-Hee Won, Jung-Min Pak, Myo-Taeg Lim
• Keywords: Fuzzy Adaptive Particle, KES 2007, Particle filter, Particle filter Method