Extraterrestrial celestial patrol missions have introduced very strict requirements for the performance of rovers, due to their high cost. Vision-based or Lidar-based environment sensing technology has matured. However, due to its perceptual characteristics, it is impossible to predict the traversability of the terrain completely, and it lacks the judgment of the physical properties of the terrain, such as the degree of hardness and softness. Due to the spectrum of risks that the rover is facing, a wide range of detection processes is required. This research paper proposes a terrain classification approach based on 3-D vibrations induced in the rover structure by the wheel-terrain interaction. Initially, the acceleration information of the three directions is obtained by using the Inertial measurement unit of the rover. Then, the characteristics of the vibrations of the known terrain are learned. The Fast Fourier Transformation (FFT) is used to transform the labeled three-axis vibration vectors into a frequency domain. Then the training feature vectors are obtained through normalization. Taking into account the characteristics of the environment, an improved back propagation (BP) neural network is used to get the mapping relationships between the vibrations and the terrain types. Finally, classification testing has been conducted on five kinds of environments, including concrete, grassland, sand, gravel, and mixed. After 20 times random testing experiments, the classification accuracy has proven to be in the range 88.99%-100%, which verified the validity and the robustness of the algorithm and laid a foundation for the subsequent identification of terrain characteristic.
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