Automated Guided Vehicle Systems Trends in Technology and Application

In this paper, the AGV (Automatic Guided Vehicles) system based on laser navigation is the research object. Layout map path network construction, shortest path search algorithm and dynamic time window planning are studied.Firstly, this paper establishes the UGNL (Unidirectional guide-path network layout) of the AGV system in the job shop environment, and the experiment uses the Dijkstra path search algorithm to apply the superiority in the UGNL map.Then, for the type of opposite, interference, chasing and deadlock conflicts of the AGV system, combined with the node time window resource allocation principle, the AGV requests the traffic management module to allocate idle node resources before performing the operation, and completes the dynamic path planning of the AGVS problem.Finally, the example simulation test is carried out on the Plant Simulation platform, and the program is used to realize the optimal scheduling application of the path search algorithm and the time window allocation strategy, so that the dynamic path planning method in this paper can better meet the actual situation and further guiding the production practice.

This paper presents a new approach for distributed control of automatic guided vehicle system (AGVS) that uses constraint logic programming formalism to model system operation. The proposed method constructs a feasible schedule by exploiting the constraint propagation mechanism determining the flow of automated guided vehicles. The AGVS is treated as a distributed system composed of autonomous and repetitive processes of automated guided vehicle (AGV) flows. Its periodic behaviour follows from a class of repetitive job shops considered. The approach employs a concept of asynchronous traffic semaphores that provide local mechanism (a dispatching rule) for vehicle flows synchronization. Setting and allocation of particular dispatching rules results in distributed control of vehicle flows possessing self-synchronization features.

Multi-load Automated Guided Vehicle (AGV) has been proposed for a while, but the advantage of its application is still not fully understood today. In order to fill this knowledge gap, a novel integrated model of a multi-load AGV system is developed in this paper by using an advanced form of Petri Nets, namely Coloured Petri Nets (CPNs), to simulate the operation of the AGV system in various scenarios. The study reported in this paper is focused to answer a few key questions, i.e. whether system performance can be continuously improved by increasing the load capacity of the multi-load AGVs; if not, whether there is an optimal load capacity of the multi-load AGV for a particular system; and whether the multi-load AGVs can still work well in a system with flexible loading and unloading points. The research results have shown that the efficiency of the AGV system can be improved by increasing the load capacity at the beginning, but the effectiveness of such an approach will decrease when the load capacity increases above a certain value. In other words, an AGV system may not perform better after using a larger capacity of multi-load AGV and there must be an optimal load capacity of the multi-load AGV for a specific AGV system. In addition, it is found that a system with flexible loading and unloading points can perform better after using a multi-load AGV.

Dispatching automated guided vehicles (AGVs) is the common approach for AGVs scheduling in practice, the information about load arrivals in advance was not used to optimize the performance of the automated guided vehicles system (AGVsS). According to the characteristics of the AGVsS, the mathematical model of AGVs scheduling was established. A heuristic algorithm called Annealing Genetic Algorithm (AGA) was presented to deal with the AGVs scheduling problem,and applied the algorithm dynamically by using it repeatedly under a combined rolling optimization strategy. the performance of the proposed approach for AGVs scheduling was compared with the dispatching rules by simulation. Results showed that the approach performs significantly better than the dispatching rules and proved that it is really effective for AGVsS. DOI: http://dx.doi.org/10.11591/telkomnika.v11i5.2481

With the recent progress in flexible manufacturing systems (FMS) in industry, increasing attention has been given to Automatic Guided Vehicle (AGV) systems. An AGV is a self-powered unit for transporting materials between stations without needing to be controlled by an operator. Such a system has several sensors to recognize the external state, and it is designed to travel between stations without external assistance. To manage each device quickly and independently it requires a distributed controller with a main computer as the host, as well as a number of micro-controllers. In this study, an AGV system with dual motor drive was constructed. A Pentium 4 personal computer was set up as the main host for the distributed control, and this communicated with other micro-controllers in the management of the motor. The speed of each motor was also controlled by a micro-controller.

Pick up‐dispatching problem together with delivery‐dispatching problem of a multiple‐load automated guided vehicle (AGV) system have been studied. By mixing different pick up‐dispatching rules, several control strategies (alternatives) have been generated and the best control strategy has been determined considering some important criteria such as System Throughput (ST), Mean Flow Time of Parts (MFTP), Mean Tardiness of Parts (MFTP), AGV Idle Time (AGVIT), AGV Travel Full (AGVTF), AGV Travel Empty (AGVTE), AGV Load Time (AGVLT), AGV Unload Time (AGVUT), Mean Queue Length (MQL) and Mean Queue Waiting (MQW). For ranking the control strategies, a new framework based on MADM methods including fuzzy MADM and TOPSIS method were developed. Then several simulation experiments which had been based on a flow path layout to find the results were conducted. Finally, by using TOPSIS method, the control strategies were ranked. Furthermore, a similar approach was used for determining the optimal fleet size. The main contribution of this paper is developing a new approach combining the top managers′ views in selecting the best control strategy for AGV systems while trying to optimize the fleet size at the mean time by combining MADM, MCDM and simulation methods.

Recent advances in the technology permit automated guided vehicles (AGVs) to travel without any physical guide path: free-ranging AGVs. These virtual flow paths provide more flexibility since vehicles can travel in both directions on the path. This paper presents a two-phase model, which regards the AGV system network as a graph consisting of nodes connected by a set of arcs, and the AGV system as an M/G/N queueing system with the vehicles representing servers and the materials representing customers. The nodes represent points such as pickup and delivery stations. The decisions are to find the direction on the arcs, such that the materials will have the least expected waiting time when the number of vehicles is given. Phase I, which is an integer programming-based approach, determines the minimum number of vehicles required to satisfy the system designer's specification on waiting time. Phase II, a heuristic rule-based approach, is to find a flow path which gives the least overall expected waiting time and service time.

Automated guided vehicles (AGVs) are being extensively used due to their attributions of high efficiency and low costs. To assure their added value, taking a typical AGV transport system as an example, the reliability issues in AGVs are investigated in this paper. First of all, the AGV transport system was mod-elled as a phased mission that comprises a few key phases. Then, the Petri net (PN) method is applied to describe the logic of the whole phase mission and based on this, the reliability of the mission is assessed via Monte-Carlo simulation. In order to validate the reliability assessment result by the PN method, the theoretical reliability of the AGV system is also assessed through performing fault tree analysis (FTA). The comparison indicates that both methods give very similar results. Thus, it can be concluded that apart from FTA, the PNs method is also a reliable tool for AGV system reliability assessment.

Diagnostic-constrained fault-tolerant control of bi-directional AGV transport systems with fault-prone sensors.

Systems of automated guided vehicles (AGV) often support internal transportation in flexible manufacturing facilities and warehouses. One of the critical aspects for the efficiency of these systems is the transport order (TO) assignment. Typically, AGVs bid for TOs so that the behavior of the transportation system is affected by the auction type and model parameters, and by AGV estimates on transport costs.

The most fundamental requirements for achieving accurate navigation of mobile robots are localization, map building and path planning. Currently, the mainstream methods include magnetic peg navigation, laser navigation, and 2D code navigation, etc. SLAM technology has the advantages of automation, intelligence, environmental adaptability, and low cost, and AGVs using this algorithm have been widely used in indoor applications and logistics transportation. Therefore, the SLAM (Simultaneous Localization and Mapping) algorithm based Automated Guided Vehicle (AGV) has become a hot research topic at home and abroad. In this paper, we investigate the SLAM algorithm in AGV system, and build indoor and logistics simulation environment maps based on 2D laser SLAM technology of Hector, Cartographer, Gmapping and Karto algorithms, and compare them by building accuracy index. The results show that the Hector algorithm has faster map building speed and accuracy, and is more suitable for indoor and logistics AGV systems.

This paper focuses on the integration of advanced sensing and control technologies into advanced systems of multiple Automated Guided Vehicles (AGVs). In particular, we focus on the key technologies developed within the Plug-and-Navigate Robots (PAN-Robots) project, highlighting how those results contribute to improving the performance of AGV systems.

An automated manufacturing system (AMS) contains a number of versatile machines (or workstations), buffers, an automated material handling system (MHS), and is computer-controlled. An effective and flexible alternative for implementing MHS is to use automated guided vehicle (AGV) system. The deadlock issue in AMS is very important in its operation and has extensively been studied. The deadlock problems were separately treated for parts in production and transportation and many techniques were developed for each problem. However, such treatment does not take the advantage of the flexibility offered by multiple AGVs. In general, it is intractable to obtain maximally permissive control policy for either problem. Instead, this paper investigates these two problems in an integrated way. First we model an AGV system and part processing processes by resource-oriented Petri nets, respectively. Then the two models are integrated by using macro transitions. Based on the combined model, a novel control policy for deadlock avoidance is proposed. It is shown to be maximally permissive with computational complexity of O (n2) where n is the number of machines in AMS if the complexity for controlling the part transportation by AGVs is not considered. Thus, the complexity of deadlock avoidance for the whole system is bounded by the complexity in controlling the AGV system. An illustrative example shows its application and power.

Recently, automated guided vehicle (AGV) systems have been popular in factories. In general, their performance is evaluated only by simulation or some similar method. In this paper, the worst case finishing time of vehicle movement is analyzed theoretically by the maximum travel time of vehicles in the AGV system. © 1997 Scripta Technica, Inc. Electron Comm Jpn Pt 3, 80(5): 85–95, 1997

Automated guided vehicles (AGVs) have long been identified as a potential driver to improve system efficiency and lower labour costs in material handling systems. Accordingly, the reliability and availability of AGV systems is crucial to assure the stability and efficiency of these systems. However, the reliability issues and maintenance strategies of AGVs have not previously been studied sufficiently. This is even more marked in the case of multi-AGV systems that consist of fleets of AGVs. To fill this knowledge gap, research is conducted considering a multi-AGV system, consisting of three AGVs, in order to develop a scientific methodology for optimising the layout design, operation and maintenance of a multi-AGV system. Once an AGV is failed, it will be towed to the maintenance site for repair by a recycle vehicle to prevent deadlock and conflict. The efficiency of the recycling process of failed AGVs in a multi-AGV system, with respect to the change of location of the maintenance site, is analysed by the approach of coloured Petri nets (CPNs). A CPN model simulating the corrective and periodic preventive maintenance processes of failed AGVs is also developed in order to investigate the impact of different AGV maintenance strategies on the operation efficiency of the multi-AGV system. The simulation results obtained clearly show that the location of maintenance sites and maintenance strategies do have significant influence on the performance of a multi-AGV system, where corrective maintenance is an effective measure to maintain the long-term reliability and stability of the system.