Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers

Abstract

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%.

Laplacian Support Vector Machine for Vibration-Based Robotic Terrain Classification

“…Dispensing with feature extraction, the recurrent neural network is able to operate the vibration data in time domain and competent for classification of 14 different terrain types [42]. Similar work based on ANN could be found in [43,44].…”

Well Logging Based Lithology Identification Model Establishment Under Data Drift: A Transfer Learning Method

“…Recent years have witnessed the development of a promising machine learning model; i.e., extreme learning machine (ELM). ELM is actually an artificial neural network with a single hidden layer, which was first proposed by Huang et al [27] and found its application in many domains, such as robotic perception, hyperspectral image classification, lithology identification, and human activity recognition [28][29][30][31][32]. Compared with support vector machine and other artificial neural networks, ELM has significant superiority in generalization performance and training time.…”

Low-Cost Road-Surface Classification System Based on Self-Organizing Maps

“…In [ 24 , 25 , 26 ], the most common machine-learning classifiers used in surface-classification tasks were analyzed. Moreover, works such as Park et al [ 27 ] or Mei et al [ 28 ] addressed the capabilities offered by deep learning in pattern analysis and feature extraction. The use of long short-term memory (LSTM) networks in combination with CNNs or sensor-fusion techniques reinforce the knowledge of the trained models.…”