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