AI-Driven Optimization of AGV Movement in War...

AI-Driven Optimization of AGV Movement in Warehouse Management: A Reinforcement Learning Approach

Author Name : Yashovardhan Saraswat

ABSTRACT Every business faces a crisis caused by the unavailability of certain services and products. Today, the key to success in any business relies on the efficient and timely delivery of services and products. To achieve effective and real-time decision-making, an intelligent system is required. The management of the supply chain plays a crucial role in the growth of a business, and implementing an automated guided vehicle (AGV) within a warehouse can greatly enhance supply chain efficiency. This paper aims to develop an artificial intelligence (AI)-based system utilizing machine learning algorithms for an AGV. To automate the process, the warehouse with its available storage capacity is divided into a grid, with each grid cell assigned unique X and Y coordinates. The AGV is responsible for the movement of materials and products between storage and exit points, eliminating the need for human intervention. The concept involves designing an AGV equipped with reinforcement machine learning algorithms that can learn from its environment and navigate within the warehouse autonomously. The Python programming language is utilized to generate results for AGV movement in the form of code.