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Mathematical Problems in Engineering
Volume 2017, Article ID 3938502, 14 pages
Research Article

A New Terrain Classification Framework Using Proprioceptive Sensors for Mobile Robots

School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

Correspondence should be addressed to Kai Zhao; nc.ude.tib@iakoahz

Received 25 May 2017; Revised 19 July 2017; Accepted 26 July 2017; Published 6 September 2017

Academic Editor: Oscar Reinoso

Copyright © 2017 Kai Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Mobile robots that operate in real-world environments interact with the surroundings to generate complex acoustics and vibration signals, which carry rich information about the terrain. This paper presents a new terrain classification framework that utilizes both acoustics and vibration signals resulting from the robot-terrain interaction. As an alternative to handcrafted domain-specific feature extraction, a two-stage feature selection method combining ReliefF and mRMR algorithms was developed to select optimal feature subsets that carry more discriminative information. As different data sources can provide complementary information, a multiclassifier combination method was proposed by considering a priori knowledge and fusing predictions from five data sources: one acoustic data source and four vibration data sources. In this study, four conceptually different classifiers were employed to perform the classification, each with a different number of optimal features. Signals were collected using a tracked robot moving at three different speeds on six different terrains. The new framework successfully improved classification performance of different classifiers using the newly developed optimal feature subsets. The greater improvement was observed for robot traversing at lower speeds.