Applying Reinforcement Learning for AMR’s Docking and Obstacle Avoidance Behavior Control

Abstract

In recent years, advancements in artificial intelligence (AI) have become an essential study for machine learning. The use of AI with the Robot Operating System (ROS) enables mobile robots to learn and move autonomously. Mobile robots can now be widely used in industrial and service sectors. Generally, robots have been operated on fixed paths requiring set points to function. This study utilizes Deep Q-Network (DQN) incorporating filtering to train and reward AprilTag images, paths, and obstacle avoidance. Training is conducted in a Gazebo simulation environment, and the collected data is verified on physical mobile robots. The DQN network excels in computing complex functions; AprilTag provides X, Y, Z, Pitch, Yaw, and Roll data. By employing DQN methods, recognition and path accuracy are simultaneously enhanced. The constructed DQN network can endow mobile robots with autonomous learning capabilities.

Keywords

• DQN (Deep Q-Network)
• AprilTag
• machine learning
• docking