Towards implementation and autonomous navigation of an intelligent automated guided vehicle in material handling systems

Abstract

Automated Guided Vehicles (AGV) have been a conventional solution and choice made by many manufacturing enterprises as means for Flexible Material Handling Systems (FMHS). In recent years, a considerable number of these vehicles have been installed on shop floors worldwide, effectively proving the usefulness of material handling systems. However, the increasing complexity of demand as well as a need for "make to order" rather than "make to stock" policy implies usage of more intelligent material handling solutions. This paper discusses the usage of an intelligent AGV as means for FMHS which should have Intelligent Material Handling System (IMHS) as the final outcome. This paper presents the experimental results of hybrid robotic control architecture. To evaluate the performance of the architecture, a mobile robot built on LEGO (R) Mindstorms NXT technology was used. Some of the architectural modules are based on the implementation of Artificial Neural Networks in order to achieve the needed robustness in exploitation. Then, through simulation using AnyLogic (R) software, the performance in terms of manufacturing system effectiveness and workstation utilization is analyzed. Based on the experimental results, the proposed IMHS can have significant advantages over conventional Material handling systems.