Deep Multi-Layer Perception Based Terrain Classification for Planetary Exploration Rovers

• Main Authors: Chengchao Bai, Jifeng Guo, Linli Guo, Junlin Song
• Format: Article
• Language: English
• Published: MDPI AG 2019-07-01
• Series: Sensors
• Subjects: planetary rover; terrain classification; vibration; multi-layer perception; deep neural network; field test
• Online Access: https://www.mdpi.com/1424-8220/19/14/3102

Accurate classification and identification of the detected terrain is the basis for the long-distance patrol mission of the planetary rover. But terrain measurement based on vision and radar is subject to conditions such as light changes and dust storms. In this paper, under the premise of not increasing the sensor load of the existing rover, a terrain classification and recognition method based on vibration is proposed. Firstly, the time-frequency domain transformation of vibration information is realized by fast Fourier transform (FFT), and the characteristic representation of vibration information is given. Secondly, a deep neural network based on multi-layer perception is designed to realize classification of different terrains. Finally, combined with the Jackal unmanned vehicle platform, the XQ unmanned vehicle platform, and the vibration sensor, the terrain classification comparison test based on five different terrains was completed. The results show that the proposed algorithm has higher classification accuracy, and different platforms and running speeds have certain influence on the terrain classification at the same time, which provides support for subsequent practical applications.