Feature Extraction for Terrain Classification with Crawling Robots

In this paper, we address the problem of terrain classification using a technically blind hexapod walking robot. The proposed approach is built on top of the existing method based on analysis of the feedback from the robot’s actuators and the desired trajectory. The formed method uses features for the Support Vector Machine classification method that assumes a regular time-invariant gait to control the robot. However, such a gait does not allow the robot to traverse rough terrains, and therefore, it is necessary to consider adaptive motion gait to deal with small obstacles, which is, unfortunately, not a regular gait with some fixed predefined period. Therefore, we propose to alter the features extraction process to utilize the terrain classification method also for an adaptive motion gait, which enables the robot to traverse rough terrains. The proposed method has been experimentally verified on several terrains that are not traversable by a default regular gait. The achieved results not only confirmed the high accuracy of the terrain classification as the existing approach, but also expanded the area of operation of a hexapod walking robot into more challenging terrains.