Abnormal movement state detection and identification for mobile robots based on neural networks

Abstract

Movement state estimation plays an important role in navigating and movement controlling for wheeled mobile robots (WMRs), especially those in unknown environments such as planetary exploration. When exploring in unknown environments, mobile robot suffers from many kinds of abnormal movement state, such as baffled by an obstacle, slipping, among others. This paper employs neural network method to detect abnormal movement states. Specifically, it exploits the kinematics of the normal and abnormal movement states of the monitored robot. Several residuals are exploited and four probabilistic neural networks are used to classify the residuals. Simulation experiments show that the methods can detect and identify most abnormal movement states. © Springer-Verlag Berlin Heidelberg 2005.