  The ability to perform locomotion in various terrains is critical for legged
robots. However, the robot has to have a better understanding of the surface it
is walking on to perform robust locomotion on different terrains. Animals and
humans are able to recognize the surface with the help of the tactile sensation
on their feet. Although, the foot tactile sensation for legged robots has not
been much explored. This paper presents research on a novel quadruped robot
DogTouch with tactile sensing feet (TSF). TSF allows the recognition of
different surface textures utilizing a tactile sensor and a convolutional
neural network (CNN). The experimental results show a sufficient validation
accuracy of 74.37\% for our trained CNN-based model, with the highest
recognition for line patterns of 90\%. In the future, we plan to improve the
prediction model by presenting surface samples with the various depths of
patterns and applying advanced Deep Learning and Shallow learning models for
surface recognition.
  Additionally, we propose a novel approach to navigation of quadruped and
legged robots. We can arrange the tactile paving textured surface (similar that
used for blind or visually impaired people). Thus, DogTouch will be capable of
locomotion in unknown environment by just recognizing the specific tactile
patterns which will indicate the straight path, left or right turn, pedestrian
crossing, road, and etc. That will allow robust navigation regardless of
lighting condition. Future quadruped robots equipped with visual and tactile
perception system will be able to safely and intelligently navigate and
interact in the unstructured indoor and outdoor environment.
