Laplacian Support Vector Machine for Vibration-Based Robotic Terrain Classification
| Autor: | Ji Chang, Zerui Li, Xiaochuan Li, Yuping Wu, Wenjun Lv, Wenlei Shi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
semi-supervised learning
Computer Networks and Communications Computer science lcsh:TK7800-8360 02 engineering and technology Semi-supervised learning Machine learning computer.software_genre 01 natural sciences non-geometric hazards 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Electrical and Electronic Engineering ComputingMethodologies_COMPUTERGRAPHICS Sequence business.industry 020208 electrical & electronic engineering 010401 analytical chemistry Supervised learning lcsh:Electronics 0104 chemical sciences Support vector machine ComputingMethodologies_PATTERNRECOGNITION Hardware and Architecture Control and Systems Engineering terrain classification Signal Processing Robot Artificial intelligence vibration business Laplace operator computer |
| Zdroj: | Electronics, Vol 9, Iss 3, p 513 (2020) Electronics Volume 9 Issue 3 |
| ISSN: | 2079-9292 |
| Popis: | 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&rsquo 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. |
| Databáze: | OpenAIRE |
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