A hybrid tactile sensor-based obstacle overcoming method for hexapod walking robots

Abstract Walking robots are considered as a promising solution for locomotion across irregular or rough terrain. While wheeled or tracked robots require flat surface like roads or driveways, walking robots can adapt to almost any terrain type. However, overcoming diverse terrain obstacles still remains a challenging task even for multi-legged robots with a high number of degrees of freedom. Here, we present a novel method for obstacle overcoming for walking robots based on the use of tactile sensors and generative recurrent neural network for positional error prediction. By using tactile sensors positioned on the front side of the legs, we demonstrate that a robot is able to successfully overcome obstacles close to robots height in the terrains of different complexity. The proposed method can be used by any type of a legged machine and can be considered as a step toward more advanced walking robot locomotion in unstructured terrain and uncertain environment.