Motion generation for a tracked robot going over an unfixed obstacle on a slope using reinforcement learning

Tracked robots are useful in disaster recovery missions owing to their high traversability. However, even tracked robots find traversing terrains with obstacles such as rocks difficult. Particularly, unfixed obstacles pose problems. Therefore, this study aims to generate motion for tracked robots to go over unfixed obstacles on slopes by utilizing a reinforcement learning (RL) approach. In RL, rewards are important to make robots learn optimal policies for various tasks. Hence, a new definition of going over an unfixed obstacle is proposed. Based on the proposed definition, a sparse reward is designed to motivate the robot to go over the obstacle while avoiding the large sliding-down phenomenon responsible for the failure of going over and the large directional deviation of the robot. In our experiment, a robot is trained in a dynamic simulator and it attempts to go over a spherical unfixed obstacle under various environmental conditions: radius of the obstacle, slope degree, and initial position of the obstacle. We verify that the success rate converges to a high value and the robot successfully generates motions to go over the spherical unfixed obstacle.