FRedBots: Fundamental Reinforcement Learning Enabled Bots

Autonomous systems have undergone a revolutionary transformation with the advent of artificial intelligence and machine learning, namely reinforcement learning (RL). This research investigates the use of Q-learning in warehouse automation and autonomous mobile robot (AMR) navigation. It uses the ROS (Robot Operating System) to tackle the problems of job scheduling and deadlock avoidance, demonstrating the revolutionary power of RL in dynamic situations. We demonstrate effective job coordination and obstacle avoidance by integrating Q-learning with ROS, highlighting the potential for AI-driven solutions to improve operational efficiency in warehouses which includes optimized path planning strategies.