Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers
1
Department of Mechanical Engineering, Fujian Polytechnic of Information Technology, Fuzhou 350003, China
2
Department of Automation, University of Science and Technology of China, Hefei 230027, China
3
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China
4
Faculty of Technology, De Montfort University, Leicester LE1 9BH, UK
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sensors 2019, 19(5), 1137; https://doi.org/10.3390/s19051137
Received: 23 January 2019
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Revised: 28 February 2019
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Accepted: 1 March 2019
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Published: 6 March 2019
(This article belongs to the Section Physical Sensors)
Autonomous robots that operate in the field can enhance their security and efficiency by accurate terrain classification, which can be realized by means of robot-terrain interaction-generated vibration signals. In this paper, we explore the vibration-based terrain classification (VTC), in particular for a wheeled robot with shock absorbers. Because the vibration sensors are usually mounted on the main body of the robot, the vibration signals are dampened significantly, which results in the vibration signals collected on different terrains being more difficult to discriminate. Hence, the existing VTC methods applied to a robot with shock absorbers may degrade. The contributions are two-fold: (1) Several experiments are conducted to exhibit the performance of the existing feature-engineering and feature-learning classification methods; and (2) According to the long short-term memory (LSTM) network, we propose a one-dimensional convolutional LSTM (1DCL)-based VTC method to learn both spatial and temporal characteristics of the dampened vibration signals. The experiment results demonstrate that: (1) The feature-engineering methods, which are efficient in VTC of the robot without shock absorbers, are not so accurate in our project; meanwhile, the feature-learning methods are better choices; and (2) The 1DCL-based VTC method outperforms the conventional methods with an accuracy of 80.18%, which exceeds the second method (LSTM) by 8.23%.
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Keywords:
autonomous robots; terrain classification; vibration; LSTM
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MDPI and ACS Style
Mei, M.; Chang, J.; Li, Y.; Li, Z.; Li, X.; Lv, W. Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers. Sensors 2019, 19, 1137. https://doi.org/10.3390/s19051137
AMA Style
Mei M, Chang J, Li Y, Li Z, Li X, Lv W. Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers. Sensors. 2019; 19(5):1137. https://doi.org/10.3390/s19051137
Chicago/Turabian StyleMei, Mingliang, Ji Chang, Yuling Li, Zerui Li, Xiaochuan Li, and Wenjun Lv. 2019. "Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers" Sensors 19, no. 5: 1137. https://doi.org/10.3390/s19051137
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