Control Method Based on Deep Reinforcement Learning for Robotic Follower with Monocular Vision

Abstract

Abstract Robotic follower is receiving attention widely in recent years. Aiming at the problems of low sample collection efficiency, high training cost and difficult design of reward function in the real world, we propose a control method based on deep reinforcement learning. Different depth layers are adopted to attain the end-to-end control of the robotic follower through pre-trained. Then, we design a reward function mechanism to judge whether the robot follower follow falsely. Then the appropriate pre-trained network is transferred to reinforcement learning, and a deep reinforcement learning system for monocular vision robot following tasks is established. According to the experimental results, the proposed deep reinforcement learning method can efficiently collect a large number of data sets, shorten the training period and reduce the number of times that the robot follower loses its target.