Guided Vehicle System Research Articles

Research on workstation-to-zone assignment for tandem automated guided vehicle system with multiple AGVs in overload zone of transport

Research on workstation-to-zone assignment for tandem automated guided vehicle system with multiple AGVs in overload zone of transport

Analysis of Factory Automated Guided Vehicles Systems by using Multi-agent System and Contract Net Protocol

Analysis of Factory Automated Guided Vehicles Systems by using Multi-agent System and Contract Net Protocol

A review of recent advances, techniques, and control algorithms for automated guided vehicle systems

A review of recent advances, techniques, and control algorithms for automated guided vehicle systems

Failure Prediction of Automated Guided Vehicle Systems in Production Environments through Artificial Intelligence

Modern industrial systems demand intricate connectivity and automation, especially in the realm of shop floor processes and intralogistics. Automated Guided Vehicle (AGV) systems are characterized by their potential for seamlessly networking value creation areas. However, failures and disruptions in AGV systems and adjacent facilities can lead to production halts, adversely affecting delivery reliability and quality. A substantial portion of the downtime stems from manual troubleshooting, underscoring the pivotal importance of the response time from maintenance staff. This paper introduces an approach employing a neural network with long short-term memory for forecasting and predictive maintenance to enhance AGV system reliability and availability in production environments. By analysing historical data, identifying patterns, and predicting potential failures or maintenance needs in AGV components and neighbouring facilities, the proposed AI-based forecasting ensures timely preventive measures. A case study shows the effectiveness of this approach in significantly improving AGV system performance, minimizing disruptions, and enhancing operational availability. This research contributes to smart manufacturing by providing a practical solution for optimizing availability of the concerned AGV system through advanced AI-based forecasting strategies.

Digital Twin and Simulation Analyses for Process Optimization of an Automated Guided Vehicle System

Development in industrial companies aims to digitize processes, ensure traceability and advance automation. Systems with automated guided vehicles (AGVs) can help meet these objectives. A core element of these systems is the higher-level system controller. When an AGV system is operated, some dynamic aspects appear. It is then to identify, that the previous specified rules for scheduling and routing of vehicles are not always proper and practical, which may result in waiting time of AGVs. That means the static performance requirements and the specifications based on them often lead to suboptimal results. A methodical problem-solving approach is to develop a digital twin as a reflection of reality and to carry out strategy analyses using simulation. For this purpose, a digital twin of the concerned AGV system with five vehicles and its technological environment is created using software of plant simulation. Various simulation scenarios are developed to simulate material flows by using different routing and scheduling rules and strategies. Drawing from the insights gained through the digital twin and simulation analyses, this study identifies novel scheduling and routing rules for the AGV system. These rules improve the overall efficiency and effectiveness of the system operations by easing traffic congestion, reducing transit time, and minimizing production downtime.

Hand Gesture Controlled Smart Car Using Image Recognition

A hand gesture-controlled smart automobile is the newest initiative in the field of human-computer interaction, and it represents the evolution toward more natural and in- tuitive user interfaces. This paper describes how OpenCV and Google’s MediaPipe are intricately coordinated to produce a control strategy that is both agile and responsive. The deployed technology translates dynamic vehicle motion commands from complex human hand gestures using advanced image recognition algorithms. This is the pinnacle of interactive technology meeting real-world locomotion needs: state-of-the-art computer vision and machine learning combined. The suggested system not only demonstrates the viability of non-contact user input for complex tasks like driving, but it also sets the way for future research in the field of autonomous vehicle guidance and control systems. This study highlights how gesture-based interfaces have the power to completely change the way people interact with cars, paving the way for more flexible and human-centered navigation systems.

A scheduling optimization method for multiple automated guided vehicle systems

When constructing a multiple automated guided vehicle (multi-AGV) system, it is essential to calculate the potential values of the system’s feature parameters. This article presents two time-cost calculators and an improved simulation platform for scientifically estimating the cost of constructing a multi-AGV system. By inputting the parameters of the number of automated guided vehicles (AGVs), the velocity of the AGV, the number of tasks, scheduling interval and path length, the designed calculator can output the system’s time expense when all tasks are completed. The designed simulation platform can display a specific multi-AGV system’s time cost and how the entire progress works in a visual interface. An advanced scheduling method based on handover mode and A* algorithm is used to solve task scheduling problems in a multi-AGV system. The overall performance of the newly integrated scheduling system is compared with other scheduling systems to validate its superiority and shortcomings in different corresponding work scenarios. The final results present a robust solution to improve a multi-AGV system’s effectiveness.

Automated Guided Vehicle Systems Way to I4.0

Automated Guided Vehicle Systems Way to I4.0

An Approach for Designing Automated Guided Vehicle Systems

An Approach for Designing Automated Guided Vehicle Systems

Automated Guided Vehicle Systems Designing & Development

Automated Guided Vehicle Systems Designing & Development

AGV robot for laser-SLAM based method testing in automated container terminal

PurposeMagnet spot is the primary method to develop the automated guided vehicle (AGV) guidance system for many automated container terminals (ACT). Aiming to improve the high flexibility of AGV operation in ACT, this paper aims to address the problem of technical stability leading to ACT production paralysis and propose a mini-terminal AGV robot for testing laser simultaneous location and mapping (SLAM)-based methods in ACT operation scenarios.Design/methodology/approachThis study developed a physical simulation robot for terminal AGV operations, providing a platform to test technical solutions for applying laser navigation-related technologies in ACTs. Then, the terminal-AGV navigation system framework is designed to apply the laser-SLAM-based method in the physical simulation robot. Finally, the experiment is conducted in the terminal operation scenario to verify the feasibility of the proposed framework for lased-SLAM-based method testing and analyze the performance of the different mini-terminal AGV robots.FindingsA series of experiments are conducted to analyze the performance of the proposed mini-terminal AGV robot for laser-SLAM-based method testing. The experimental results show the validity and effectiveness of the AGV robot and AGV navigation system framework with better local map matching, loopback and absolute positional error.Originality/valueThe proposed mini-terminal AGV robot and AGV navigation system framework can provide a platform for innovative laser-SLAM-based method testing in ACTs applications. Therefore, this study can effectively meet the high requirements of ACT for maturity and stability of the laser navigation technical.

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

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.

A review of the automated guided vehicle systems: dispatching systems and navigation concept

Problem. As one of the three key elements of flexible manufacturing systems (FMS), automated guided vehicles (AGVs) play a significant role in material handling systems. They've been around for more than a decade, primarily in Europe. AGVs were formerly utilized mostly in production lines, but due to recent industrial development, they now play a vital role in a variety of different applications and domains, including warehouses, port facilities, transportation systems, and even the medical sector. Goal. The purpose of the work is to analyze two methods of AGV control and the paths that the AGV should follow during transport operations, taking into account safety and limitations for heavy loads that need to be transported in difficult conditions. Methodology. We attempted to provide a summary of the two existing AGV control methods, centralized and decentralized control offered by major manufacturers in this study. Almost all function in a centralized manner, with a single central controller controlling the whole fleet of AGVs. The author sees a tendency toward decentralized systems in which AGVs make individual decisions in favor of transportation flexibility, robustness, and adaptability. A brief explanation of well-known and mature navigation technologies used by AGVs in the industry is also provided, along with both physical and virtual paths to be followed by the AGV during transportation operations, since these technologies get to be a key issue given the safety constraints, particularly for heavy loads to be transported in challenging environments. Results. This paper covers one of the key tasks that must be completed in order to control an AGV system for a central and decentral architecture, for which we have already given a broad overview, as well as the differences between the architectures and their benefits and drawbacks. Originality. The author sees that the merging of various techniques we have will lead to more and new challenges as well as innovations, however, that practical application of distributed control still requires further research and studies and has a way to go in terms of taking other sections such as safety and sustainability into account. Practical value. The primary benefits and drawbacks of various technologies are discussed, as well as how we might improve the efficiency of some of them.

Synchronized Tracking Control of Dynamic System of Unmanned Rear-Wheel Vehicles Based on Dynamic Analysis

From the classic automatic guided vehicle system, the system of the unmanned rear-wheel drive vehicle (URWDV) based on a dynamic analysis is studied. In the URWDV system, the relationship among the position information, velocity, and the heading angular velocity of the unmanned vehicle is established in the plane coordinate system and the coordinate system centered vehicle itself. The velocity and heading angular velocity values are obtained through a dynamic analysis and are used as control parameters. The synchronized tracking control of the unmanned vehicle is realized by the control scheme of the velocity and the heading angular velocity. Finally, the simulation examples show the effectiveness of the tracking control.

Multi-agent based scheduling method for tandem automated guided vehicle systems

We consider the scheduling method that allows to minimize the traveling time of a AGV (and thus energy consumption) at each of zones, while to minimize the waiting time of the automated guided vehicles (AGVs) and/or containers for pickup and delivery (PD) at the transfer points (TPs) installed between adjacent zones in tandem systems. Scheduling for these tandem AGV systems is often not possible from a single point by a single intelligent agent. Intead, scheduling has to be performed using multiple intelligent agents. We consider the cooperative multi-agent scheduling (CMAS) scheme in which each agent employs a multi-offspring genetic algorithm. Coordination between the agents is used to improve their schedulings. This coordination can be in the form of serial scheme. We propose a novel CMAS method based on Lagrange theory and compare this to a non-cooperative scheduling approach between agents. The proposed method is verified at AGV system for a book printing process in Pyongyang Textbook Print Shop as the real-world experiment and it is shown that the CMAS method for tandem AGV system is effective.

Demonstration And Analysis of The Guiding Control System for Aerospace Crafts

The development of space vehicle guidance and control systems gradually becomes a focus in recent research as a key to space vehicles’ information processing and control in the space environment. In general, among the popular methods in G&C systems, artificial intelligence-based algorithms contain a wide prospect. This study offers descriptions of the current methods in AI-based algorithms, proposed challenges involved in this field, and described the recent achievements in solving these issues. To be specific, the basic descriptions of the controlling systems as well as the various state-of-art machine learning approaches that are suitable for the G&C system are demonstrated and analyzed including ANN, DNN, RL, SVM, DRL, etc. According to the analysis, the challenges as well as the drawbacks of the advanced scenarios are estimated as well as propose the future prospects to improve the performance of landing. Overall, these results shed light on guiding further exploration of aerospace vehicles.

Research on parking guidance system based on computer vision

The rapid pace of urbanization has made the urban parking problem more and more important, and various regions have introduced corresponding policies and measures to solve the parking problem, such as building large parking plants, introducing intelligent systems, etc. However, the number of parking spaces in large parking plants is large, the spatial distribution is complex, and it is difficult to find parking spaces. It is very important to improve parking efficiency through intelligent parking guidance and management. The parking space recognition system based on image recognition technology of computer vision can obtain real-time parking space utilization. The vehicle guidance system based on the generalized shortest path can guide incoming vehicles, reduce the time for vehicles to wait and find parking spaces. As the core module of the parking intelligent management system, the intelligent parking space recognition system and vehicle guidance system can significantly improve the parking efficiency.

Implementation and evaluation of an assistance software to support decision-making, design and simulation setup for automated guided vehicle systems

Autonomous guided vehicle systems (AGVS) offer huge potential for optimizing transportation tasks in manufacturing and warehouse logistics. Especially for small and medium enterprises, this potential is left untapped, as the selection of a proper solution requires substantial expertise. Support from external consultancies is often not requested. To address this issue, concepts for expert systems and multi-criteria decision algorithms have been discussed in academia for years but have not found any practical application yet. Therefore, in this paper we present an easy-to-use, holistic planning tool implemented with a user-centered development process, followed by the evaluation of the prototype. The results show its feasibility to realize a comprehensive planning process in such way, that users with little or no specific prior knowledge can perform initial steps for planning AGVS on their own. The user tests indicate good usability and acceptance for the prototype and show improvement potential for further development iterations.

Usage of Automatic Guided Vehicle Systems and Multi-agent Technology in Higher Education

Today, smart manufacturing is differentiated from many other initiatives by its emphasis on human ingenuity. Human capabilities must be enhanced by intelligently designing a customized solution for a specific domain. For example, Industry 4.0 is based on collaborative robots that digitize and simplify manufacturing processes. In fact, Automatic Guided Vehicles (AGVs) are widely used in intelligent industries due to their productivity, flexibility, and versatility. They are widely considered as one of the most important tools for flexible logistics in workshops. They can move materials and products without a predefined route. Many commercially available AGVs provide a self-guided navigation system to find their way to target workstations. However, many developers and producers of industrial robots face several challenges in designing AGV systems, such as the difficulty of defining a decentralized system decision as well as the discontinuity and complexity of the design process. One of the relevant research areas related to our AGV solution is the establishment of the human-machine industrial relationship and the creation of safe operation side by side.

Safe Maneuvering Near Offshore Installations: A New Algorithmic Tool

Maneuvers of human-operated and autonomous marine vessels in the safety zone of drilling rigs, wind farms and other installations present a risk of collision. This article proposes an algorithmic toolkit that ensures maneuver safety, taking into account the restrictions imposed by ship dynamics. The algorithms can be used for anomaly detection, decision making by a human operator or an unmanned vehicle guidance system. We also consider a response to failures in the vessel’s control systems and emergency escape maneuvers. Data used by the algorithms come from the vessel’s dynamic positioning control system and positional survey charts of the marine installations.