Automated Guided Vehicle System Research Articles

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.

Efficient Predictive Regulation Algorithms for AGV System in Industrial Internet

<p>In recent years, the industrial Internet has developed rapidly. In order to improve the reliability, real-time, and economy, Automated Guided Vehicle (AGV) in intelligent manufacturing system becomes an indispensable technology. However, the current AGV system relies too much on the fixed network bandwidth environment in information transmission and management. When the traffic demand changes frequently, this form of network configuration lacks network resource management mechanism. Further, it leads to the problems of delay, waste of network flow, and inability to dynamically allocate network resources. So it is vital to improve the AGV system. Therefore, this paper proposes three predictive control algorithms and a Network Cable Scheduling algorithm to manage the network resources. They are Markov Chain Linear Programming Regulation (MCLPR) algorithm, Prophet Linear Programming Regulation (PLPR) algorithm, and Machine Learning Linear Programming Regulation (MLLPR) algorithm. The experimental results show that PLPR and MLLPR algorithm have high efficiency in the aspect of regulation. MLLPR algorithm has the lowest cost. MLLPR algorithm has the strongest leakage limitation ability, followed by PLPR algorithm. The balance regulation efficiency of MLLPR in none “4 + 1” mode is the highest in different network cable modes.</p> <p> </p>

Deadlock prevention and multi agent path finding algorithm considering physical constraint for a massive fleet AGV system

This study introduces deadlock avoidance algorithms for Automated Guided Vehicles (AGVs) within warehouse fulfillment systems. Numerous AGVs are tasked with transporting requests to deliver shelves to workers stationed at various locations. The pathfinding challenge within such systems is identified as the Multi-Agent Pickup and Delivery (problem) and has been extensively explored in computer science and AI. Nevertheless, most prior research was conducted in environments that are discretized and allow circular movements of AGVs, potentially leading to deadlocks in real-world applications. Our proposed algorithm leverages a dynamic path block method and path reservation. Specifically, it limits movement to certain edges when AGVs navigate along reserved paths, while the remaining AGVs plan their routes using unrestricted edges. We show that our algorithm not only effectively prevents deadlocks but also scales well in environments with a high number of AGVs.

Research on AGV scheduling and potential conflict resolution in port scenarios: based on improved genetic algorithm

In the research of Automated Guided Vehicle (AGV) scheduling, the most critical issues are the optimization of task allocation to AGVs and the handling of conflict scenarios. To address these challenges, we propose a scheme for AGV scheduling optimization and conflict resolution. To begin with, we introduce a novel improved genetic algorithm grounded on a combination strategy that re-encodes tasks into compound groupings, effectively simplifying large-scale integer programing problems into smaller, more manageable ones. Subsequently, the simplified problem is solved using an improved genetic algorithm. Test results validate that this method not only quickens the pace of finding solutions but also significantly improves the quality of those solutions. This is particularly evident when it comes to managing larger-scale optimization challenges. Furthermore, within AGV system conflict scenarios, this paper divides them into two primary categories: navigational conflicts and task quantity changes. For navigational conflicts, three resolution approaches are designed to address four different types of conflict situations: head-on, crossing, occupation, and chasing conflicts. Considering the fluctuations in task quantity, we developed strategies for rescheduling, non-rescheduling, and insertion rescheduling. Their performances were experimentally compared across various scales of scheduling problems, providing data support and theoretical basis for the selection of scheduling strategies in practical applications.

Hassle - Free Load Carrying Using Automated Guided Vehicle

In today's rapidly evolving world, where efficiency and sustainability are paramount, businesses are seeking technologies that not only save time and drive profits but also align with their sustainable goals. In response to this demand, a prototype of an Automated Guided Vehicle (AGV) is developed.AGVs are versatile robotic platforms designed to navigate predefined paths while handling heavy loads of materials. The AGV system addresses these needs by utilizing cost-effective RFID tags and an RFID reader for efficient load tracking and navigation. Additionally, it incorporates two infrared (IR) sensors for path detection, an L298N motor driver for precise motor control, an Arduino microcontroller as the central processing unit, and an HC-05 Bluetooth module for wireless communication.By integrating these components, along with feedback control loops and path planning algorithms, The AGV is observed to be an efficient load carrier and can navigate reliably in dynamic environments. Above all, this solution not only saves time and enhances profitability but also promotes sustainability by optimizing resource utilization and minimizing manual intervention. Key Words: AGV(Automated Guided Vehicle),RFID(Radio Frequency Identification) tags, RFID (Radio Frequency Identification)reader, IR sensors, L298N Motor driver, Microcontroller.

Automatic Guided Vehicle Scheduling in Automated Container Terminals Based on a Hybrid Mode of Battery Swapping and Charging

Automatic guided vehicles (AGVs) in the horizontal area play a crucial role in determining the operational efficiency of automated container terminals (ACTs). To improve the operational efficiency of an ACT, it is essential to decrease the impact of battery capacity limitations on AGV scheduling. To address this problem, this paper introduces battery swapping and opportunity charging modes into the AGV system and proposes a new AGV scheduling problem considering the hybrid mode. Firstly, this study describes the AGV scheduling problem of the automated container terminals considering both loading and unloading tasks under the hybrid mode of battery swapping and charging. Thereafter, a mixed-integer programming model is established to minimize the sum of energy costs and delay costs. Secondly, an effective adaptive large neighborhood search algorithm is proposed to solve the problem, in which the initial solution construction, destroy operators, and repair operators are designed according to the hybrid mode. Finally, numerical experiments are conducted to analyze the effectiveness of the model and the optimization performance of the algorithm. The results demonstrate that the hybrid mode of battery swapping and charging can effectively reduce the number of battery swapping times and scheduling costs compared to the existing mode.

A design of intelligent AGV system combined with a robotic arm for flexible production lines

Unmanned mobile robots have broad application prospects in industrial production. With the continuous deepening of Industry 4.0, the complexity of manufacturing workflows has skyrocketed, and researching robots suitable for flexible production is becoming a focus of development. With the rapid development of computer technology and sensor technology, the ability of robots to obtain their own state and environmental information has also been greatly expanded. Studying the work, path planning, and obstacle avoidance of robots has important practical significance. This article designs a flexible production robot that integrates Automated Guided Vehicle (AGV) with industrial robots. It has the ability to perceive the environment, make optimal decisions, and operate independently. It can achieve functions such as mobile transportation, flexible operation, and human-machine interaction and cooperation in the production line. This article uses Gazebo to construct a production environment and simulate robot movement. In addition, MATLAB is used to conduct simulation experiments on the operation of the UR10 robot. The results and planning path map validate the feasibility and effectiveness of this method. The mobile robot designed in this article combines a robotic arm with an AGV, which has certain application value in future flexible manufacturing factories and provides a certain reference value for flexible production.

Multi-AGV Scheduling under Limited Buffer Capacity and Battery Charging Using Simulation Techniques

In recent years, automated guided vehicles (AGVs) have been widely adopted to automate material handling procedures in manufacturing shopfloors and distribution centers. AGV scheduling is the procedure of allocating a transportation task to an AGV, which has large impacts on the efficiency of an AGV system with multiple AGVs. In order to optimize the operations of multi-AGV systems, AGV scheduling decisions should be made with consideration of practical issues such as buffer space limitations and battery charging. However, previous studies have often overlooked those issues. To fill this gap, this paper proposes a simulation-based multi-AGV scheduling procedure for practical shopfloors with limited buffer capacity and battery charging. Furthermore, we propose three kinds of rules: job selection rules, AGV selection rules, and charging station selection rules, for AGV scheduling in practical shopfloors. The performance of the rules is evaluated through multi-scenario simulation experiments. The FlexSim software v.2022 is used to develop a simulation model for the experiments, and the experimental findings indicate that the job selection rules have larger impacts on the average waiting time than the other kinds of rules.

Interaction between a Human and an AGV System in a Shared Workspace—A Literature Review Identifying Research Areas

Background: This article presents the results of a literature review from 2018 to 2023, which focused on research related to human and AGV system cooperation in a shared workspace. This study defines AGV systems as systems using Automated Guided Vehicles or Autonomous Guided Vehicles. An Automated Guided Vehicle is a cart that follows a guided path, while an Autonomous Guided Vehicle is an Automated Guided Vehicle that is autonomously controlled. The analyses conducted answered two research questions: (RQ1) In what aspects are the human factor examined in publications on the implementation and operation of AGV systems? (RQ2) Has the human-AGV collaboration aspect been analyzed in the context of a sustainable work environment? Methods: The literature review was conducted following the systematic literature review method, using the PRISMA approach. Results: Based on the search of two journal databases, according to the indicated keywords, 1219 documents pertaining to the analyzed issues were identified. The selection and elimination of documents that did not meet the defined criteria made it possible to limit the number of publications to 117 articles and proceedings papers. On this basis, the authors defined a classification framework comprising five basic research categories and nine subcategories. The analyzed documents were classified, and each distinguished group was characterized by describing the results. Conclusions: The development of a two-level classification framework for research from the analyzed area according to the assumptions of the concept map and the identification of research gaps in the area of human-AGV interaction.

Using EtherCAT technology to launch online automated guided vehicle manipulation with unity‐based platform for smart warehouse management

AbstractIn Industry 4.0, the innovative concept of a smart warehouse is defined as a progressive development in logistics and operations management. Numerous advanced techniques are recommended for integration, including wearable devices, communication, loading/unloading, optimization, robotics, and automated transportation, among others. Currently, the growing demand for swift cargo distribution, cost‐effective labour utilization, and enhanced productivity stands as an urgent topic for both enterprises and scholars. This paper presents an EtherCAT‐based model of automated guided vehicle (AGV)‐manipulation introduced within the Unity platform for smart warehouse management. The working environment selected for this model is a cross‐docking warehouse with a chessboard layout. Initially, theoretical computations pertaining to mechanical design are established to provide accurate parameters. Subsequently, the structure of the TwinCAT Human–Machine Interface Server and the configuration within the Unity environment are outlined. Following that, the software architecture, encompassing data exchange and a set of motion commands, is meticulously explained. To validate the proposed approach, several laboratory tests are conducted under identical conditions. This research offers several contributions: firstly, it introduces the innovative use of real‐time technology to represent AGV systems; secondly, it elucidates the potential connection between virtual reality and the physical model; and thirdly, it employs fluid motion techniques to illustrate and visualize driving performance. These findings hold significant promise not only for the advancement of smart warehouse management but also for the broader landscape of digital trade.

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.

Model Predictive Path Planning of AGVs: Mixed Logical Dynamical Formulation and Distributed Coordination

Most of the existing path planning methods of automated guided vehicles (AGVs) are static. This paper proposes a new methodology for the path planning of a fleet of AGVs to improve the flexibility, robustness, and scalability of the AGV system. We mathematically describe the transport process as a dynamical system using an ad hoc mixed logical dynamical (MLD) model. Based on our MLD model, model predictive control is proposed to determine the collision paths dynamically, and the corresponding optimization problem is formulated as 0-1 integer linear programming. An alternating direction method of multipliers (ADMM)-based decomposition technique is then developed to coordinate the AGVs and reduce the computational burden, aiming for real-time decisions. The proposed methodology is tested on industrial scenarios, and results from numerical experiments show that the proposed method can obtain high transport productivity of the multi-AGV system at a low computational burden and deal with uncertainties resulting from the industrial environment.

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.

Perspectives of managers and workers on the implementation of automated-guided vehicles (AGVs)—a quantitative survey

Automated-guided vehicles (AGVs) are considered as an advanced technology for improving intralogistics in manufacturing companies. However, diverging perspectives between management and operational staff on the implementation of AGVs in existing production environments can lead to a lack of employee acceptance and highlight the need for appropriate organizational change management initiatives. At present, there is a lack of knowledge about the success factors for deploying AGVs in manufacturing companies, including human factors such as worker acceptance. We therefore conducted a quantitative survey among production and logistics workers, project leaders, and managers (n = 89) in ten German companies that have already introduced AGVs in their production in order to investigate and compare their perspectives and to derive implications for successful AGV implementation projects. Our findings reveal that workers consider some of the most important acceptance factors as insignificantly addressed by the project management. In addition, we found significant differences in the perspectives of logistics and production workers on the implementation of AGVs, with logistics workers being less satisfied and significantly more concerned about job security. Furthermore, project leaders’ ability to accurately anticipate their employees’ perspective (perspective taking accuracy) positively influences employees’ satisfaction with the implementation of the AGV system. These findings have theoretical implications for research on organizational change and practical implications for AGV implementation projects.

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.

A novel multi-tasks chain scheduling algorithm based on capacity prediction to solve AGV dispatching problem in an intelligent manufacturing system

With the rapid development of automated guided vehicle (AGV), the AGV dispatching problem (AGVDP) becomes a major concern in the AGV system owing to its decisive impact on the productivity of the modern industry. However, based on the instantaneous assignment scheduling strategy to arrange tasks for AGVs, most AGVDP researches prove to be incomplete for taking no account of the remaining capacity of AGVs. Therefore, intended to optimize the AGVDP, this paper presents a multi-tasks chain scheduling algorithm (MTCSA) through the fusion of multi-tasks chain model and capacity prediction model based on support vector machine, which is verified by a real intelligent manufacturing system. The results reveal that the makespan and heavy load ratio aided by MTCSA can be reduced and improved by a maximum of 20.0% and 8.5% to the studied manufacturing system respectively, and thus further confirm the superior performance of the presented algorithm.

Designs of Particle-Swarm-Optimization-Based Intelligent PID Controllers and DC/DC Buck Converters for PEM Fuel-Cell-Powered Four-Wheeled Automated Guided Vehicle

For automatic guided vehicles (AGVs), maximizing the operating time with maximum energy efficiency is the most important factor that increases work efficiency. In this study, the fuel-cell-powered AGV (FCAGV) system was modeled and optimized control and design were carried out to obtain high tracking performance with minimum power consumption. Firstly, a full mathematical model of FCAGV, which involves the AGV, the fuel cell, DC/DC converters and motors, was obtained. Then, particle swarm optimization (PSO)-based intelligent PID and I controllers were developed for maximizing the route-tracking performance of AGV and voltage-tracking performance of the DC/DC converter with reduced power consumption. PSO was used to determine the optimal parameters of controllers and the values of DC/DC converters’ components. The performance of the full AGV system was analyzed for different paths. The results show that the sufficient path-tracking and voltage-tracking performance was obtained for AGV and DC/DC converters, respectively. The average tracking errors according to global coordinate system are 0.0061 m at the x axis, 0.0572 m at the y axis and 0.0228 rad at rotational axis. The obtained average voltage-tracking errors for each DC/DC converters were approximately 0.8033 V. These results indicate that the developed controllers with optimal coefficients work successfully with small voltage and path-tracking errors. During this motion, the average consumed power from the fuel cell was observed as 58.2675 W. These results show that the designed optimized intelligent controllers have sufficient performance with high energy efficiency and maximum route tracking.

MLATSO: A method for task scheduling optimization in multi-load AGVs-based systems

In the context of increasingly competitive intelligent manufacturing, the multi-load Automated guided vehicles (AGVs) based Automated Storage and Retrieval System (AS/RS) has been of particular interest, as reductions in the number of AGVs required can significantly decrease potential congestions and increase the system effectiveness. In comparison with the single-load AGVs system, more difficult and critical issue of scheduling multi-load AGVs to automate storage/retrieval missions and to maximize economic benefits remains unresolved. Therefore, we propose a task scheduling optimization method for multi-load AGVs-based systems, with which, the objectives of least number of occupied AGVs, shortest travel time and minimum conflicts can be met simultaneously. The experiments are conducted in various scenarios, and verify that our work can use fewer AGVs to optimize tasks delivery, which enables the AS/RS stakeholders to reach win-win results for system performance and AGVs investment, thus maximizing economic benefit.

Integrating Both Routing and Scheduling Into Motion Planner for Multivehicle System

In multi-automated guided vehicle (AGV) control, optimization and collision avoidance are two of the key issues. To deal with these problems of the AGV fleet, motion planning is a good solution. This method usually comprises two steps as follows: routing and scheduling that are always separately executed in conventional routine. This scheme still exists some drawbacks, such as limitation of candidate paths or lack of flexibility in handling collisions. Besides, with a specific layout, the algorithm needs to be modified to be proper with that application. The warehouse with grid-based layout employed popularly in logistics and supply chain is our concern. To overcome this theme, a time-frame-based routing and scheduling (TFRS) algorithm for motion planning of vehicles is proposed for this warehouse application. In detail, TFRS can also be called an enhanced Dijkstra’s algorithm (EDA) with adaptive weights for every segment and node. It was designed to gain several benefits of time due to the shortest path, free collision, and proper for chessboard layout. The main idea is that while conducting path routing, certain circumstances of potential accidents are detected and dealt by scheduling in every loop. Due to simultaneous policies of routing and scheduling, the optimization and secure operation could be achieved in the AGV system. Numerous situations in danger of collision are experimented to verify the effectiveness, flexibility, and correctness of the proposed algorithm.