Artificial Intelligence Development in Sensors and Computer Vision for Health Care and Automation Application

Reinforcement Learning in Robot Automation by Q-learning

Author(s): Minh Long Hoang *

Pp: 42-57 (16)

DOI: 10.2174/9789815313055124010005

* (Excluding Mailing and Handling)

Abstract

This chapter demonstrates the pivotal role of reinforcement learning (RL), specifically employing the Q-learning algorithm, in enhancing the capabilities of autonomous mobile robots (AMRs) for transportation tasks. The focus is on enabling the robot to learn and execute two critical tasks autonomously. The first task involves the robot learning the optimal path to transport an object from its current location to a specified destination. The second task requires the robot to adeptly avoid obstacles encountered along the way, ensuring safe and efficient navigation. The robot is equipped with advanced sensors, including light detection and ranging (Lidar) and inertial measurement unit (IMU) sensors, to accomplish these tasks. The Lidar sensor provides detailed scanning of the surrounding environment, allowing the robot to detect and map obstacles, while the IMU sensors aid in precise positioning and movement tracking. These sensory inputs are crucial for the robot to understand its environment and make informed decisions accurately. The chapter elucidates the working principles of the Q-learning algorithm, a model-free RL technique that enables the robot to learn optimal actions through trial-and-error interactions with its environment. The training process involves the robot being rewarded for successful task completion and penalized for undesirable actions, gradually refining its policy to maximize cumulative rewards. Through detailed explanations and practical demonstrations, this research showcases how Q-learning facilitates the robot's learning process, enabling it to master the tasks of path planning and obstacle avoidance. The insights gained from this study highlight the potential of RL in advancing the autonomy and efficiency of mobile robots in transportation and other applications, paving the way for further innovations in the field.


Keywords: Autonomous mobile robot, Q-learning, Reinforcement learning

Related Journals
Related Books
© 2025 Bentham Science Publishers | Privacy Policy