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Open AccessArticle

Laplacian Support Vector Machine for Vibration-Based Robotic Terrain Classification

1
School of Economics and Management, Beijing University of Technology, Beijing 100124, China
2
Department of Automation, University of Science and Technology of China, Hefei 230027, China
3
School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
4
Faculty of Technology, De Montfort University, Leicester LE1 9BH, UK
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(3), 513; https://doi.org/10.3390/electronics9030513
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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