Path Planning and Operation Efficiency Improvement of Automated Warehouse Robots Based on Reinforcement Learning

This paper addresses the critical challenge of path planning and operational efficiency optimization for automated warehouse robots through the application of reinforcement learning techniques. The study begins by outlining the development context of modern warehousing, emphasizing that traditional path planning strategies often struggle to meet the increasing demands for speed, accuracy, and adaptability in high-volume and dynamic storage environments. It then presents the core principles of reinforcement learning, providing a theoretical foundation that supports the design and implementation of adaptive robotic systems capable of learning optimal navigation strategies over time. The current state of path planning in automated warehouse robots is analyzed, highlighting key limitations such as static route generation, low responsiveness to environmental changes, and difficulties in handling congestion or unexpected obstacles. The paper further explores the application methods of reinforcement learning, demonstrating that appropriately designed learning models allow robots to continuously adjust their paths based on real-time environmental feedback, thereby reducing travel time, avoiding collisions, and improving overall system throughput. In addition to path optimization, the study examines strategies for enhancing operational efficiency at multiple levels, including task scheduling, load balancing, and energy consumption management, all of which contribute to a more coordinated and responsive warehouse operation. Experimental results validate that reinforcement learning-based path planning significantly improves both individual robot performance and overall warehouse efficiency, confirming the practical value of integrating intelligent learning algorithms into automated logistics systems. This research provides comprehensive insights and methodological guidance for the advancement of automated warehousing technology, offering a scalable framework for future studies and industrial applications in complex, dynamic storage environments.