On the influence of body velocity in foothold adaptation for dynamic legged locomotion via CNNs

Abstract

This paper analyzes the importance of considering the body velocity for foothold adaptation during legged locomotion. We show how this velocity affects the decisions made by a foothold adaption strategy, and the number of feasible footholds computed by the approach. We extend our previous work by considering the body velocity in this foothold adaptation method and augmenting a convolutional neural network (CNN) classifier to account for the current velocity of the robot. Our results suggest that the foothold evaluation strategy has a better performance with this new CNN than with architectures that assume a constant velocity and that only consider heightmaps as input for the foothold evaluation.