Deep Learning Vision System for Quadruped Robot Gait Pattern Regulation

Robots with bio-inspired locomotion systems, such as quadruped robots, have recently attracted significant scientific interest, especially those designed to tackle missions in unstructured terrains, such as search-and-rescue robotics. On the other hand, artificial intelligence systems have allowed for the improvement and adaptation of the locomotion capabilities of these robots based on specific terrains, imitating the natural behavior of quadruped animals. The main contribution of this work is a method to adjust adaptive gait patterns to overcome unstructured terrains using the ARTU-R (A1 Rescue Task UPM Robot) quadruped robot based on a central pattern generator (CPG), and the automatic identification of terrain and characterization of its obstacles (number, size, position and superability analysis) through convolutional neural networks for pattern regulation. To develop this method, a study of dog gait patterns was carried out, with validation and adjustment through simulation on the robot model in ROS-Gazebo and subsequent transfer to the real robot. Outdoor tests were carried out to evaluate and validate the efficiency of the proposed method in terms of its percentage of success in overcoming stretches of unstructured terrains, as well as the kinematic and dynamic variables of the robot. The main results show that the proposed method has an efficiency of over 93% for terrain characterization (identification of terrain, segmentation and obstacle characterization) and over 91% success in overcoming unstructured terrains. This work was also compared against main developments in state-of-the-art and benchmark models.