Automated Guided Vehicle System Research Articles

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.

Geometric Zone-Control Algorithm for Collision and Deadlock Avoidance in AGV System

Geometric Zone-Control Algorithm for Collision and Deadlock Avoidance in AGV System

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.

Scheduling the tasks of multiple AGVs in a fault-tolerant control way

The paper concerns a development of an approach for implementing fault-tolerant control (FTC) of automated guided vehicles (AGVs). The main challenge considered in the article is the way of taking into account in the scheduling algorithm the delays occurring in the system resulting from the use of transport means. Mostly, multiple AGVs are designed and implemented as a firm real-time system in which delays are treated as faults. The proposed framework allows to compensate the faults in the real time. In the paper a multiple AGVs that transport item from an output of a production system to a high bay warehouse is presented. The article presents the non-deterministic nature of a multi-AGV system and includes a mathematical description of the system using (max, +) algebra. Having a analytical description of AGVs system, a new FTC strategy is proposed. Moreover, in the paper an illustrative case study showing the performance of the developed approach is presented.

An intelligence-based hybrid PSO-SA for mobile robot path planning in warehouse

Mobile robots play crucial roles in industry and commerce, and automatic guided vehicles (AGV) are one of the primary parts of smart manufactory and intelligent logistics. Path planning is the core task for the AGV system, and it generates the path from origin to destination. The motivation of the study is to improve the scalability, flexibility, adaptability, and performance of the robot path planning systems. We propose the hybrid PSO-SA algorithm for the optimization of AGV path planning. Compared with other heuristic algorithms by benchmark functions, including HS, FA, ABC and GA, the proposed algorithm shows excellent performance in dealing with optimization problems. It reduces the possibility of getting trapped in one local optimum and enhances the efficiency to get the best global solution with faster convergence and less time consumption. It is evaluated with multiple cost functions and path planning with simulations and experiments. The objective of the proposed algorithm is to minimize the path length and produce a smooth path without collision. The proposed PSO-SA algorithm is compared with PSO in the path planning application, and the mean runtime and iteration times are usually significantly lower than PSO.

Risk Related to AGV Systems—Open-Access Literature Review

AGV systems are considered a necessary element of the recently created cyber-physical systems whose task is to support material flows in intelligent factories. The implementation of AGV systems in cyber-physical systems generates new types of threats, and the level of risk of adverse events related to the operation of these systems is changing. This article aims to present the results of an open-access literature review, the purpose of which was to identify issues addressed in studies of the analysis and risk management of AGV systems, to group publications according to the proposed topical framework, and to identify gaps in the current research. We used the PRISMA concept to review the literature. After completing all stages of the procedure, 153 publications on risk in AGV systems were selected for analysis. To ensure the high quality of the analysis, access to the full texts of the selected documents was necessary. For this reason, we have limited the collection to open-access publications. The results of the analysis allowed us to distinguish 12 primary categories and 26 additional subcategories within the literature. These categories are characterized by different numbers of publications and cover different AGV systems operation areas. The simultaneous assignment of documents to several subcategories indicates the need for a holistic view of issues related to the risk of AGV operation in operating environments with varying degrees of automation. An essential scientific contribution of our analysis is the identification of a research gap. The analyzed publications lacked studies on methods of risk identification and assessment that take into account the specificity of AGV operation, especially in the cyber-physical systems of Logistics 4.0.

Simulation and Validation of Optimized PID Controller in AGV (Automated Guided Vehicles) Model Using PSO and BAS Algorithms.

Automated guided vehicles (AGVs) are popular subsets of robots that come in various shapes and sizes. The group's use in the industry ranges from applications for carrying pallets, carts, and utensils to helping the elderly or transporting medicine to hospitals. Even recently, they have been used in libraries to carry books on shelves. The main part of an AGV includes its body, motor, driver, processor, and sensors, which are more or less the same in all types of AGVs, and addons vary depending on the application and the work environment. The part that affects AGV performance is the control strategy, to which researchers have shown different approaches. Using various techniques and simulations to obtain a model is the key and can help to improve and evaluate the performance of the strategy of the robot. In this study, based on the actual design of the AGV system, all data and components are described, and the simulation is performed in MATLAB software. Then, for controlling the platform based on the PID controller tuning, four methods of Ziegler Nichols, empirical, Particle Swarm Optimization (PSO), and Beetle Antennae Searching (BAS) (optimizer) are discussed, and the results are compared in the four paths including the circle, ellipse, Spiral and 8-shaped paths by observing and testing the tuned PID parameters. Finally, a series of subsequent experiences were carried out in CoppeliaSim (VREP) as a famous robot simulator to overcome the environmental constraints for the same paths that were used in Matlab based on the extracted PID values. Based on the results, the empirical methods, PSO, and BAS errors are very close together. But in general, the BAS algorithm is the fastest, and the PSO had better performance. In general, the maximum error is linked to the path of 8 shapes and the minimum is related to circle shape one. Finally, the analysis of results in different paths in both simulators shows the same results. Therefore, concerning the limited test on the real platform and using the PID coefficients obtained from the simulation shows the model's ability for the researchers in robotic research.

Prescribed‐time stabilisation control of differential driven automated guided vehicle

AbstractThe position control problem of differential‐driven automated guided vehicles (AGVs) based on the prescribed‐time control method is studied. First, an innovative orientation error function is proposed by an auxiliary arcsine function about the orientation angle. Thus, the problem of position control of AGV is transformed into the stabilisation control of the kinematic system. Second, by introducing a reserved time parameter and a smooth switching function, a novel time‐varying scaling function is proposed. This novel scaling function avoids the risk of infinite gain in the conventional prescribed‐time control method while ensuring the smoothness of control laws. Then, an improved velocity constraint function is proposed using the Gaussian function. Compared with the existing constraint function, the proposed method not only has more smoothness but also solves the balance point errors caused by the large AGV orientation errors. The presented method ensures that the AGV reaches the target position in a prescribed time. Hence, the upper bound of the AGV system state can be determined by adjusting parameters. Matlab simulation results show that the proposed controller can effectively make the AGV system state satisfy the prescribed bound.

Research on Intelligent Decision Method of Optimal Production Planning and AGV In-time Delivery in Mixed-Model Assembly Line

An automated guided vehicle (AGV) system is an indispensable part of a mixed-model assembly line (MMAL) for flexible and efficient production, which is utilized for the material delivery. With the urgent needs of industry digitalization and market customization, along with the trend of corporate smart decision management, MMAL has been widely adopted in the manufacturing process of modularized products. To address the parameter matching and energy consumption optimization of the AGV system in MMAL, the overall architecture of an intelligent MMAL (IMMAL) is firstly proposed. And the model of this problem is constructed. Then, the research methodology is proposed, which comprehensively adopts the dispatching rule, genetic algorithm, design of experiment, BP neural network, and simulated annealing. Eventually, a real-life case is used to validate the practicality of the proposed solutions. The results show that the optimal solutions of MMAL production and AGV system delivery at the minimum energy consumption are obtained successfully. Furthermore, three factors: the quantity, capacity, and velocity of AGVs greatly impact the energy consumption of the AGV system, where the quantity of AGVs has the most negligible significance, while the capacity of AGVs has the most significance.

CALCULATING THE PAYBACK PERIOD OF THE IMPLEMENTATION OF AN AUTOMATED GUIDED VEHICLES - AGV IN A MANUFACTURING SYSTEM

Automated Guided Vehicle (AGV) systems have been frequently used as material handling equipment in the supply of assembly lines. The use of AGV systems has taken attention of practitioners and researchers. Therefore, in this article, we will present a sizing study of an AGV loop used to supply an assembly line. The study was made by a multidisciplinary team from the Faculty of Mechanics and Technology of the University of Pitesti. The Payback period calculus in the implementation of an AGV network in the supply of an assembly line workstations is essential for the implementation.

Hybrid Task Allocation of an AGV System for Task Groups of an Assembly Line

An AGV system can be used to transport different-size materials in an assembly line. The hybrid task allocation problem is involved in the assembly line, where both single-AGV tasks and multi-AGV tasks exist. However, there is little research on this problem. The goal of solving this problem is to obtain a task allocation scheme with minimum idle time and maximum system throughput. Since all necessary materials must be delivered to the assembly station before the operation can start, the delivery tasks are not independent of each other in a task group serving the operation. To solve the problem above, a hybrid task allocation method based on a task binding strategy and an improved particle swarm optimization (IPSO) is proposed. Firstly, a mathematical model considering the punctuality of material delivery and the cooperative relationship between tasks is established. Secondly, a task binding strategy and four heuristic rules are devised to improve the quality of randomly- and heuristic-generated individuals in the initial population for model optimization. Thirdly, an IPSO is developed to help the optimization algorithm jump out of local optimums. Finally, a simulation is performed to verify the effectiveness of the proposed methods. The simulation results show that a better scheme can be obtained by our hybrid task allocation method, compared to conventional Genetic Algorithms and PSO algorithms.

The parallel AGV scheduling problem with battery constraints: A new formulation and a matheuristic approach

Nowadays, automated guided vehicles (AGVs) are frequently used in larger systems, known as AGV-based transportation systems, for the movement of goods and materials from one location to another. The design of an efficient and effective AGV-based transportation system requires to address many tactical and operational issues. Among the others, the scheduling of transfer jobs on the AGVs represents one of the main operational issues that has to be solved to overcome delays in production and material handling processes. In this context, a significant research activity on AGV systems and on related scheduling problems has been conducted in the last twenty years. However, most of the contributions neglected the issues related to the AGV battery depletion and recharge. Thus, in this work, we study the AGV scheduling problem with battery constraints (ASP-BC). It consists in determining the scheduling of transfer jobs and charging operations of a fleet of homogeneous AGVs such that the makespan of the handling process is minimized. The methodological contribution of our work is twofold. On one side, we propose an original mixed integer linear programming formulation based on the bottleneck generalized assignment problem. On the other side, we propose a three step matheuristic based on the sequential solution of the two subproblems arising from the natural decomposition of the ASP-BC and a local search heuristic. The proposed approaches have been tested and validated on simulated and real instances provided by a manufacturing company. The results show the effectiveness and the scalability of the proposed solution methods.

ARTICLE ON OPTIMIZATION OF THE AGV TROLLEY SYSTEM USING A DIGITAL TWIN

The article discusses the prerequisites and reasons for using AGV, the advantages of their implementation, as well as methods for optimizing the AGV system using a digital twin

Efficient routing for multi-AGV based on optimized Ant-agent

AGV (Automated guided vehicle) systems have been widely used in intelligent logistics due to the efficient transportation. The routing for multi-AGV, one of the key factors influence the efficiency of AGV systems, remains a challenge if several AGVs travel in a complex environment. In this paper, we develop an efficient routing for multi-AGV by combining centralized control with decentralized control, which is based on the Ant-agent optimized by repulsive potential field (Rf-Ant-agent). The congestion of node in the Ant-agent is optimized by visibility, and a guidance factor is introduced to the path node selection model to determine the travel direction of the AGV in nodes with centralized control. Then, a new repulsive potential field function is proposed, and a scheme of varying velocity is designed to adjust the velocity of AGVs with decentralized control. Furthermore, a new transition rule is established to determine the motion state of AGVs with the combination of centralized control and decentralized control. Finally, Netlogo is used to simulate and analyze the performance of the Rf-Ant-agent. The simulation results indicate that the perception distance and safe distance influence the efficiency and stability of Rf-Ant-agent, respectively; relative to the Ant-agent, the collision avoidance, transportation distance and transportation efficiency of the Rf-Ant-agent improved by 16.3%, 4.4% and 21.8%, respectively.

Automatic Guided Vehicles Introduction Impacts to Roll-On/Roll-Off Terminals: Simulation and Cost Model Analysis

Automatic guided vehicles (AGVs) have been successfully applied to cargo terminals to reduce operating costs and improve productivity. However, the focus was on container terminal operations. Ports with roll-on/roll-off (RORO) terminals still heavily depend on human resources for the loading/unloading processes. Work operations are affected by human errors and safety issues. In particular, terminals where vehicles cannot be stacked pressure workers to handle cargo more rapidly, which induces more errors. In this study, we propose automating RORO terminal operations by using AGVs. We assessed the impact of AGVs on the productivity, cost efficiency, and environment. A series of simulation models was developed on the basis of the current loading system at an actual port to test the impact of AGVs. Then, we developed a cost model to analyze the economic benefit of AGVs compared with the current loading system. The environmental benefits were also analyzed. Results revealed that a system using 29 AGVs matched the productivity of the current loading system, and using more AGVs increased the productivity. For a given productivity level, the total operating cost of the AGV system was three times less than that of the current system over a 15-year period. The AGV system also showed great potential for improving the environmental friendliness of terminal operations. This is the first study to propose automating RORO terminal operations to improve productivity and sustainability through AGV technology rather than human factors. AGVs are expected to become a good option in the future to address labor shortages and the “untact” era.

AutomationML as Seamless Data Exchange Format for Integration of Automated Planning Tools for Assembly Line Design

The design of assembly lines is a demanding task, whose outcome is highly dependent on the production engineers’ knowledge and experience. Additionally, it is very time-consuming when performed manually. For this reason, many approaches exist to automate the required planning phases with their sub-tasks. This paper presents an integrated modeling approach using AutomationML as seamless data exchange format for the sequentially required tasks to design and configure an assembly line against multiple user-defined criteria. In its simple form, the design of an assembly line requires the assignment of processes to a set of resources and workstations (conceptual design), and decisions about the spatial arrangement of workstations and their connection through a conveyor or AGV system (physical layout). After every planning phase the model is extended and enriched with additional information required for the next phase and its automated tools.

Application of Automated Guided Vehicles in Smart Automated Warehouse Systems: A Survey

Automated Guided Vehicles (AGVs) have been introduced into various applications, such as automated warehouse systems, flexible manufacturing systems, and container terminal systems. However, few publications have outlined problems in need of attention in AGV applications comprehensively. In this paper, several key issues and essential models are presented. First, the advantages and disadvantages of centralized and decentralized AGVs systems were compared; second, warehouse layout and operation optimization were introduced, including some omitted areas, such as AGVs fleet size and electrical energy management; third, AGVs scheduling algorithms in chessboardlike environments were analyzed; fourth, the classical route-planning algorithms for single AGV and multiple AGVs were presented, and some Artificial Intelligence (AI)-based decision-making algorithms were reviewed. Furthermore, a novel idea for accelerating route planning by combining Reinforcement Learning (RL) and Dijkstra’s algorithm was presented, and a novel idea of the multi-AGV route-planning method of combining dynamic programming and Monte-Carlo tree search was proposed to reduce the energy cost of systems.

Model-Based Research for Aiding Decision-Making During the Design and Operation of Multi-Load Automated Guided Vehicle Systems

Multi-load Automated Guided Vehicle's (AGV) are regarded as a potential tool to tackle the low-efficiency issue that have plagued traditional single-load AGV systems for many years. However, to date, the optimal design and operation of multi-load AGV systems is still an unresolved question. In order to explore the answer to this question and help operators make decisions during the design and operation of these systems, this article will use Coloured Petri nets (CPN) to develop a mathematical model to investigate the performance (i.e., the total number of items delivered within a given time) of the multi-load AGV system in various scenarios. The research has shown that the failure of multi-load AGVs can significantly lower the performance of the AGV system. Although it is possible to maintain high system performance by performing onsite corrective maintenance, the research shows that this can be achieved using a combination of periodic maintenance and backup AGV use. Finally, it is found that increasing the number of multi-load AGVs can increase system performance, but will decrease the efficiency (i.e., the average number of items delivered per AGV) of the individual AGVs in the system due to the increased traffic conflicts and hence longer waiting times.