Three-stage Path Planning for Warehouse Robots Based on Q-learning Algorithm

With the rapid growth of e-commerce, warehouse automation has become an important area to improve efficiency and reduce operational costs. Warehouse robots have been widely used by online merchants to automate the delivery process of warehouse logistics. However, traditional research lacks clear guidance on the optimal setting of parameters for Q-learning in a warehouse environment. And it does not consider path planning for multi-stage operations. This research examines the application of the Q-learning algorithm for three-stage path planning in robotic systems inside a warehouse setting. The research optimized the convergence rate of Q-learning in a warehouse by comparing the efficiency and stability of the Q-learning algorithm under different parameters. This research provides a three-stage warehouse transportation path planning strategy. This strategy is accomplished through a three-stage “inbound-shipping-return” path planning. It enables the robot to navigate efficiently through the warehouse layout, reducing transportation time and avoiding obstacles. This research demonstrates the effectiveness of Q-learning in warehouse path planning and shows that warehouse robots have great potential to save labor costs and improve the efficiency of warehouse transportation tasks. The Q-learning-based optimization method provides a solution for the automation of warehouse transportation tasks.