Tracked robots have high traversability over rough terrain. However, even for such robots, it is still challenging to traverse terrain with non-fixed obstacles which may move when the robots go over them. Therefore, we propose a reinforcement learning-based method to generate the motion of the tracked robot to go over the obstacle. We set a task where the robot attempts to go over a sphere-shaped non-fixed obstacle and reach the goal. To succeed in the task, we designed a reward function so that the robot can reach the goal as straight as possible. As a training algorithm, Deep Q-Network was used and the robot was trained in a dynamics simulator. It was confirmed that the robot succeeded in the task using the trained network, which generated motion for going over a sphere-shaped non-fixed obstacle.
Published in: 2023 IEEE/SICE International Symposium on System Integration (SII)
Date of Conference: 17-20 January 2023
Date Added to IEEE Xplore: 15 February 2023
INSPEC Accession Number: 22626365
DOI: 10.1109/SII55687.2023.10039098
Publisher: IEEE
Conference Location: Atlanta, GA, USA