An online terrain classification framework for legged robots based on acoustic signals

Terrain classification information is of great significance for legged robots to traverse various terrains. Therefore, this communication presents an online terrain classification framework for legged robots, utilizing the acoustic signals produced during locomotion. The Mel-Frequency Cepstral Coefficient (MFCC) feature vectors are extracted from the acoustic data recorded by an on-board microphone. Then the Gaussian mixture models (GMMs) are used to classify the MFCC features into different terrain type categories. The proposed framework was validated on a quadruped robot. Overall, our investigations achieved a classification time-resolution of 1 s when the robot trotted over three kinds of terrains, thus recording a comprehensive success rate of 92.7%.