Vibration based terrain classification for mobile robot using machine learning methods

Nowadays, mobile robots require accurate and reliable control algorithms. The parameters of these algorithms are strongly influenced by the characteristics of the terrain over which the robot moves. The type of terrain can be identified using machine learning approaches. One promising approach is classification using measurements from inertial sensors. This method provides effective classification without the need for complex models. This paper deals with the analysis of different machine learning models using the vibration of inertial sensors. We experimented with various manual and automatic feature extraction techniques, each combined with different classification algorithms. At the conclusion of the evaluation phase, the most successful method from each feature extraction category will be selected based on performance metrics. These selected models were further validated on a custom dataset to assess their generalization capabilities in real-world conditions.