Ground substrate classification for adaptive quadruped locomotion

Abstract

In order to realize adaptive quadruped locomotion on terrains with different properties (such as surface friction or elasticity modulus), we plan to collect the foot-ground contact force and gyroscope information during locomotion on different ground substrates, then classify the ground substrates with the feature vector extracted from the collected data using Support Vector Machine (SVM) algorithm. However, the quadruped walk gait generated by Central Pattern Generators (CPGs) does not perform well on certain ground substrates, e.g., robots may be stuck in the soft ground substrates with small elasticity modulus. Therefore, for one thing, we present a Center of Gravity (COG) adjustment method to eliminate the offset between the control signal generated by CPGs and the actual phase of the quadruped limb, so the limb in theoretical swing phase is able to lift off the ground. For another, we combine CPGs with a foot path planning method to make the lift height controllable. Using these methods, the quadruped robot Biodog realizes the sensor data collection on five different ground substrates. Then we train and classify the sensor data with the SVM. About 99.33% of the five ground substrates can be classified correctly.