# Applying Reinforcement Learning for AMR's Docking and Obstacle Avoidance Behavior Control
## 1 Introduction  
As artificial intelligence technology rapidly advances, many service and industrial sectors are increasingly integrating AI systems, with the ROS (Robot Operating System) [1, 2] becoming a crucial platform for applying machine learning and AI. Through the ROS, technologies such as service robots, robotic arms, disinfection robots, and logistics robots can be effectively implemented. However, traditional mobile robot systems require pre-built maps for global and local path planning [2]. When using Move base, obstacles often cause collisions, limiting the system's flexibility. To overcome these limitations, this research has designed a self-learning mobile robot system that leverages machine learning, integrating DQN (Deep Q-Network), SLAM (Simultaneous Localization and Mapping), and AprilTag algorithms. This system allows robots to adapt to environments and navigate autonomously without the need to rebuild maps, improving mobility efficiency and accuracy while ensuring that robots can reliably reach their goals and autonomously dock for recharging [3].  
Furthermore, to address the shortcomings of traditional mobile robots in complex environments, this study focuses on enhancing the robot's autonomous decision-making capabilities through AI technology. Particularly, it enables the robot to adapt to changing environments without frequently rebuilding maps. The system integrates DQN deep reinforcement learning, allowing the robot to continuously interact with the environment for autonomous learning and route optimization, but also incorporates SLAM technology, enabling precise localization and map building in dynamic environments. The AprilTagalgorithm further enhances the accuracy and stability of marker recognition, ensuring the robot can navigate and dock for charging in complex environments. Additionally, by combining image recognition and radar-based obstacle avoidance training, the robot gains stronger environmental awareness and autonomous obstacle avoidance capabilities, significantly boosting the intelligence level of mobile robots and making them perform better in diverse application scenarios.  
Beyond these technological integrations, this research also focuses on developing a comprehensive hardware and software architecture to support these advanced machine learning and AI technologies. On the hardware side, high-performance processors and various sensors, including cameras, radars, and IMUs (Inertial Measurement Units), are employed to ensure the robot can accurately perceive its surroundings and perform complex computations. The data from these sensors are integrated into the ROS framework, enabling real-time map construction and localization through SLAM [3], and allowing the robot to navigate autonomously. The software architecture adopts a modular design, facilitating future expansions and upgrades. For example, the results of DQN algorithm training can be transmitted and applied through ROS, not only accelerating the deployment of machine learning models but also allowing the system to flexibly adjust when faced with different tasks [4].  
Additionally, to meet the demands of various application scenarios, this research explores multiple potential applications, such as in service robots, enabling them to autonomously navigate complex and dynamically changing indoor environments, or in industrial robots, to enhance the efficiency and accuracy of automated production lines. These applications demonstrate the system's broad applicability and potential, contributing to the advancement of robotics technology and providing technical support for the development of future smart factories and intelligent homes. Ultimately, these efforts aim to create a more thoughtful, efficient, and reliable robotic system capable of playing a crucial role in various complex real-world applications [5].