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

Authors: Rakesh Kumar Sidharthan; Ramkumar Kannan; Seshadhri Srinivasan

Addresses: Electric Vehicle Engineering and Robotics (EVER) Lab, School of Electrical and Electronics Engineering, SASTRA Deemed to be University, Thanjavur-613402, Tamil Nadu, India ' Electric Vehicle Engineering and Robotics (EVER) Lab, School of Electrical and Electronics Engineering, SASTRA Deemed to be University, Thanjavur-613402, Tamil Nadu, India ' Department of Engineering, University of Sannio, Benevento, 82100, Italy

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.

Keywords: autonomous robots; adaptive neuro-fuzzy inference system; ANFIS; terrain classification; wheel-terrain interactions; user interface.

DOI: 10.1504/IJCVR.2020.110641

International Journal of Computational Vision and Robotics, 2020 Vol.10 No.6, pp.545 - 560

Accepted: 06 Jul 2019
Published online: 27 Oct 2020 *

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