A Machine Learning Approach for Design and Control of Automated Guided Vehicle System - A Critical Review

Abstract

This paper presents a critical review of the machine learning approach for the design and control of automated guided vehicle (AGV) systems. The paper discusses the current state of the art in terms of machine learning approaches for the design and control of AGV systems. It also provides a comparison between traditional control approaches and machine learning approaches for AGV system design and control. The paper further explores the potential of machine learning algorithms and their application in the design and control of AGV systems. The paper reviews the various machine learning algorithms such as artificial neural networks (ANNs), support vector machines (SVMs), deep learning, gaussian process regression (GPR), and reinforcement learning (RL) that are used for the design and control of AGV systems. It also discusses the advantages and disadvantages of using each of these algorithms for AGV system design and control. The paper further presents a case study of an AGV system that is designed and controlled using a machine learning approach. This case study provides a detailed analysis of the system architecture and the performance of the system. The results from the case study demonstrate the potential of using machine learning algorithms for the design and control of AGV systems. The paper concludes by providing an overview of the current state of the art in terms of machine learning approaches for AGV system design and control. The paper also provides future research directions and recommendations for the further improvement of the design and control of AGV systems using machine learning algorithms.