Adaptive neuro-fuzzy inference system based on-the-move terrain classification for autonomous wheeled mobile robots

Abstract

Building intelligence in autonomous robots to classify heterogeneous terrains on-the-move is a challenging task, but a pivotal feature required for accomplishing safety critical missions. This paper proposes an adaptive neuro-fuzzy inference system for online terrain classification in the wheeled mobile robot using the steady-state behaviour of robot wheel on the terrain. The key idea is to model the wheel-terrain interactions as a parametric varying system, whose steady-state behaviours are characterised by the terrain type. The proposed method uses the steady state gains and the corresponding input command to robot wheel for identifying the terrain type. Our results show that the proposed approach has a classification accuracy of 95.2% for the trained terrains, whereas 94.2% and 93.8% are observed in robust and adaptive testing, respectively. Additionally, a customised graphical user interface is developed to provide easy access to the researchers for terrain identification.