Performance comparison of probabilistic methods based correction algorithms for localization of autonomous guided vehicle

Abstract

This paper presents performance comparison of probabilistic methods based correction algorithms for localization of AGV (Autonomous Guided Vehicle). Wireless guidance systems among the various guidance systems guides the AGV using position information from localization sensors. Laser navigation is mostly used to the AGV of a wireless type, however the performance of the laser navigation is influenced by a slow response time, big error of rotation driving and a disturbance with light and reflection. Therefore, the localization error of the laser navigation by the above-mentioned weakness has a great effect on the performance of the AGV. There are many different methods to correct the localization error, such as a method using a fuzzy inference system, a method with probabilistic method and so on. Bayes filter based estimation algorithms (Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter and Particle Filter) are mostly used to correct the localization error of the AGV. This paper analyses performance of estimation algorithms with probabilistic method at localization of the AGV. Algorithms for comparison are Extended Kalman Filter, Unscented Kalman Filter and Particle Filter. Kalman Filter is excluded to the comparison, because Kalman Filter is applied only to a linear system. For the performance comparison, a fork-type AGV is used to the experiments. Variables of algorithms is set experiments based heuristic values, and then variables of same functions on algorithms is set same values.