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Open AccessArticle

Laplacian Support Vector Machine for Vibration-Based Robotic Terrain Classification

School of Economics and Management, Beijing University of Technology, Beijing 100124, China
Department of Automation, University of Science and Technology of China, Hefei 230027, China
School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
Faculty of Technology, De Montfort University, Leicester LE1 9BH, UK
Author to whom correspondence should be addressed.
Electronics 2020, 9(3), 513;
Received: 19 February 2020 / Revised: 14 March 2020 / Accepted: 17 March 2020 / Published: 20 March 2020
(This article belongs to the Special Issue Robots in Assisted Living)
The achievement of robot autonomy has environmental perception as a prerequisite. The hazards rendered from uneven, soft and slippery terrains, which are generally named non-geometric hazards, are another potential threat reducing the traversing efficient, and therefore receiving more and more attention from the robotics community. In the paper, the vibration-based terrain classification (VTC) is investigated by taking a very practical issue, i.e., lack of labels, into consideration. According to the intrinsic temporal correlation existing in the sampled terrain sequence, a modified Laplacian SVM is proposed to utilise the unlabelled data to improve the classification performance. To the best of our knowledge, this is the first paper studying semi-supervised learning problem in robotic terrain classification. The experiment demonstrates that: (1) supervised learning (SVM) achieves a relatively low classification accuracy if given insufficient labels; (2) feature-space homogeneity based semi-supervised learning (traditional Laplacian SVM) cannot improve supervised learning’s accuracy, and even makes it worse; (3) feature- and temporal-space based semi-supervised learning (modified Laplacian SVM), which is proposed in the paper, could increase the classification accuracy very significantly. View Full-Text
Keywords: non-geometric hazards; terrain classification; vibration; semi-supervised learning non-geometric hazards; terrain classification; vibration; semi-supervised learning
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Shi, W.; Li, Z.; Lv, W.; Wu, Y.; Chang, J.; Li, X. Laplacian Support Vector Machine for Vibration-Based Robotic Terrain Classification. Electronics 2020, 9, 513.

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