Multiple Automated Guided Vehicles Research Articles

Hierarchical planning for multiple AGVs in warehouse based on global vision

In automated warehouses, multiple automated guided vehicles (AGVs) are widely used to sort parcels for improving storage efficiency. However, as the number of AGVs increases, the scheduling complexity also increases dramatically. This paper proposes a hierarchical planning algorithm for efficiently coordinating a fleet of AGVs in warehouses based on global vision. We use the camera installed on the top of the warehouse to capture the global image that is used to track AGVs. Based on a network congestion diffusion model, we quantify the road congestion and add it to the evaluation index to form a time-varying dynamic evaluation function. Then the improved A* algorithm and time window algorithm are combined as the hierarchical planning algorithm to search the idle path and avoid collisions. Extensive simulations show that the hierarchical planning based on global vision not only effectively schedules multiple AGVs in the warehouse, but also has lower time complexity than other real-time path planning algorithms.

A fault-tolerant control strategy for multiple automated guided vehicles

An advanced control of manufacturing and transportation systems forms a prominent research field with powerful algorithms developed in the last decades. Challenges still arise, if several automated guide vehicles (AGV) have to be coordinated. This paper focuses on the modelling and fault-tolerant control of multiple AGVs. The considered application concerns a highly flexible AGV transportation system delivering product items to transfer stations at a high-storage warehouse in a manufacturing system. The research contribution concerns the development of a mathematical description of a set of multiple AGVs along with an algorithm that can generate an optimum sequence of item outlet delivery times. The proposed solution addresses both synchronization and concurrency issues, which are inevitable in this kind of multiple-vehicle systems. Apart from these issues, modelling inaccuracy is also addressed using interval analysis coupled with max-plus algebra. Subsequently, fault diagnosis and fault-tolerant control are also investigated and addressed in the proposed approach. This leads to a fault-tolerant control framework, which is based on a fusion of the predictive control and interval max-plus algebra. The distinct quality of the proposed approach is that the optimization can be carried out in a reliable way and that certain faults and modeling uncertainties can be tolerated. The paper concludes with illustrative examples, which show the performance of the proposed approach using both fault-free and faulty scenarios.

A Mathematical Model for Multiple-Load AGVs in Tandem Layout

Reducing cost of material handling has been a big challenge for companies. Flexible manufacturing system employed automated guided vehicles (AGV) to maintain efficiency and flexibility. This paper presents a new non-linear mathematical programming model to group n machines into N loops, to make an efficient configuration for AGV system in Tandem layout. The model minimizes both inter-loop, intra-loop flow and use balanced-loops strategy to balance workload in system simultaneously. This paper significantly considers multiple-load AGVs, which has capability of reducing fleet size and waiting time of works. A modified variable neighborhood search method is applied for large size problems, which has good accuracy for small and medium size problems. The results indicate that using multiple load AGV instead of single load AGV will reduce system penalty cost up to 44%.

A Novel Tabu Search Algorithm for Multi-AGV Routing Problem

In this paper, we propose a novel tabu search (NTS) algorithm that improves the efficiencies of picking goods of automated guided vehicles (AGVs) in an automatic warehouse by solving the conflicts that happen when multiple AGVs work at the same time. Relocation and exchanging operations are designed for the neighborhood searching process based on each pickup-point’s location in the warehouse, along with the initial solution generation and the termination condition in the proposed algorithm. The experimental results show that the tabu search algorithm can effectively optimize the order of pickup points, which could further reduce the total travel distance and improve the efficiencies of AGVs in automatic warehouses.

Winner determination problem in multiple automated guided vehicle considering cost and flexibility

This study solves the integration difficulty between scheduling and routing aspects of the multiple Automated Guided Vehicle (AGV) problem through the winner determination problem (WDP). Our model reduces the cost of using multiple AGV for logistics and warehousing applications. We show that solving the WDP by the mixed integer linear programming (MILP) is inefficient as the assignment of routes is made complicated by the combination of large number of AGVs. We illustrate an efficient approach through our proposed genetic algorithm using knowledge based operators, which decomposes a non-linear combinatorial auction model into a linear model. Simulation results showed the efficacy of MILP, the conventional GA and our proposed GA-based method. However, MILP only works well for small scale data. When the number of routes and AGV increases, the proposed GA method supersedes the other methods as indicated by cost and route flexibilities.

Dynamic Resource Reservation Based Collision and Deadlock Prevention for Multi-AGVs

Automated guided vehicles (AGVs) are widely used for material handling in warehouses and automated production lines due to their high efficiency and low cost. However, AGVs usually interact with each other because of the restricted capacity of the layout. Although many algorithms have been proposed to address the problem, most of them are inefficient for collision and deadlock avoidance in dynamic environments. This paper proposes a dynamic resource reservation (DRR) based method supporting time-efficient scheduling and collision avoidance of multiple AGVs. In this method, the layout is divided into square blocks with the same size that are abstracted as points in the undirected graph. In order to solve the collision and deadlock problem dynamically, the shared resource points of each vehicle are extracted from their guide paths in real time. Unlike the traditional approaches most of which adopt a static point occupation policy, DRR exploits dynamical reservations of shared resource points to change AGV movement states for avoiding collisions and deadlocks, resulting in better time efficiency. We jointly implement the algorithm on both central and local controllers. Extensive simulation results demonstrate the feasibility and efficiency of the proposed collision and deadlock prevention method.

Multi-AGVs Path Planning in Intelligent Parking Lot

The problem of path planning for multiple Automated Guided vehicles (AGVs) in intelligent parking lot. Proposing a path planning algorithm that combining K shortest path with time window. Firstly, using the visibility graph algorithm to model the parking lot. According to the established model, the K short paths of each AGV are obtained by combining the A star and path deviation algorithm. Then, the time window algorithm is used to predict the time that each AGV occupies path. If some AGVs’s time windows have overlapped, comparing the waiting strategy with the alternative K paths strategy, and the shortest strategy is adapted to solve the conflict. Finally, an example is given to verify the proposed algorithm, and the results show that the proposed algorithm can plan a collision-free path for each AGV in the intelligent parking lot of multiple AGVs. And the searching speed is relatively fast.

A prioritized planning algorithm of trajectory coordination based on time windows for multiple AGVs with delay disturbance

PurposeIn the running of multiple automated guided vehicles (AGVs) in warehouses, delay problems in motions happen unavoidably as there might exist some disabled components of robots, the instability of networks and the interference of people walking. Under this case, robots would not follow the designed paths and the coupled relationship between temporal and space domain for paths is broken. And there is no doubt that other robots are disturbed by the ones where delays happen. Finally, this brings about chaos or even breakdown of the whole system. Therefore, taking the delay disturbance into consideration in the path planning of multiple robots is an issue worthy of attention and research.Design/methodology/approachThis paper proposes a prioritized path planning algorithm based on time windows to solve the delay problems of multiple AGVs. The architecture is a unity consisting of three components which are focused on scheduling AGVs under normal operations, delays of AGVs, and recovery of AGVs. In the components of scheduling AGVs under normal operations and recovery, this paper adopts a dynamic routing method based on time windows to ensure the coordination of multiple AGVs and efficient completion of tasks. In the component for scheduling AGVs under delays, a dynamical prioritized local path planning algorithm based on time windows is designed to solve delay problems. The introduced planning principle of time windows would enable the algorithm to plan new solutions of trajectories for multiple AGVs, which could lower the makespan. At the same time, the real-time performance is acceptable based on the planning principle which stipulates the parameters of local time windows to ensure that the computation of the designed algorithm would not be too large.FindingsThe simulation results demonstrate that the proposed algorithm is more efficient than the state-of-the-art method based on homotopy classes, which aims at solving the delay problems. What is more, it is validated that the proposed algorithm can achieve the acceptable real-time performance for the scheduling in warehousing applications.Originality/valueBy introducing the planning principle and generating delay space and local adjustable paths, the proposed algorithm in this paper can not only solve the delay problems in real time, but also lower the makespan compared with the previous method. The designed algorithm guarantees the scheduling of multiple AGVs with delay disturbance and enhances the robustness of the scheduling algorithm in multi-AGV system.

WIA-FA and Its Applications to Digital Factory: A Wireless Network Solution for Factory Automation

Intelligent factory automation systems strongly rely on industrial wireless control networks which have to ensure timely and reliable data exchange among their components. This paper presents a comprehensive survey on recently approved International Electrotechnical Commission standard Wireless networks for Industrial Automation–Factory Automation (WIA-FA). This paper first introduces the system architecture of WIA-FA including network device, network topology, and system management, and then illustrates WIA-FA protocol stack and key technologies. Furthermore, two WIA-FA testbeds are described to demonstrate the high performance of WIA-FA. After that, three examples of practical applications are provided in this paper. One application deploys a WIA-FA network to monitor and control industrial robots in a digital workshop. The second application adopts the deployment of WIA-FA as a real-time wireless network that connects automated guided vehicles (AGVs) in a logistic sorting system. The last application coordinates multiple cooperative AGVs via the WIA-FA network to carry large and complex components. Finally, the open issues and future directions for WIA-FA networks are presented.

Multiple AGV fault-tolerant within an agile manufacturing warehouse

The current trend towards Industry 4.0 leads to several important aspects that concern future warehouse systems. The holistic digitalization efforts behind this trend may enable sustainable and resilient processes within warehouses and may increase their agility, but require elaborate control and diagnosis systems. This paper concentrates on one prominent means for increasing the agility - the employment of multiple automated guided vehicles (AGVs). Due to the complexity of a system with multiple AGVs, model predictive control (MPC) algorithms are required for effective and efficient operation. In industrial reality, uncertainties cannot be avoided, it is therefore highly desirable that these algorithms can handle such uncertainties and minor inconsistencies. The precedent necessities led to a strategy based on a fault-tolerant control framework, which is based on max-plus algebra with a model predictive control scheme. The applicability of this strategy is validated on the example of the feedings system of an high-bay warehouse.

Optimization of Material Transportation Assignment for Automated Guided Vehicle (AGV) System

This article focuses on Material Transportation Assignment problem that is identified as an Automated Guided Vehicles (AGV) multi-load task assignment. The primary goal of this paper is to determine the factors needed to optimize material transportation system. This study also explores the optimization and performance enhancement of the Flexible Manufacturing System (FMS) environment. The implementation of Genetic Algorithm (GA) in this model is to obtain the optimal solution for FMS layout. The combination of delivery and pickup task are addressed by modified algorithm for advancement in multiple loads AGV. The result obtained depicts that the proposed task assignment method with a modified genetic algorithm can produce acceptable performance compared to conventional task assignment method.

Coordination of multiple AGVs: a quadratic optimization method

This paper presents an optimization strategy to coordinate a fleet of Automated Guided Vehicles (AGVs) traveling on ad-hoc pre-defined roadmaps. Specifically, the objective is to maximize traffic throughput of AGVs navigating in an automated warehouse by minimizing the time AGVs spend negotiating complex traffic patterns to avoid collisions with other AGVs. In this work, the coordination problem is posed as a Quadratic Program where the optimization is performed in a centralized manner. The proposed method is validated by means of simulations and experiments for different industrial warehouse scenarios. The performance of the proposed strategy is then compared with a recently proposed decentralized coordination strategy that relies on local negotiations for shared resources. The results show that the proposed coordination strategy successfully maximizes vehicle throughput and significantly minimizes the time vehicles spend negotiating traffic under different scenarios.

Collision-Free Route Planning for Multiple AGVs in an Automated Warehouse Based on Collision Classification

An automated warehouse system contains a number of materials, workstations, and multiple Automated Guided Vehicles (AGVs). The automated warehouse is server-controlled. This paper proposes a collision-free routing method for AGVs based on collision classification. This method can deal with collisions arising in the automated warehouse. It first divides the warehouse environment into five areas, and then performs route planning. In this paper, the environment map for AGVs is described by using the grid method. The initial route of each task is predetermined by improved Dijkstra’s algorithm. The server detects the potential collisions by comparing each workstation’s ID and corresponding time window in every route. This paper presents four collision classifications and three solutions. Based upon the analyses and experiments, we select the corresponding solution for each type of collision. Presented case studies demonstrate the efficiency of the proposed collision-free route planning approach.

Monte Carlo Simulation to Compare Markovian and Neural Network Models for Reliability Assessment in Multiple AGV Manufacturing System

We compare two approaches for a Markovian model in flexible manufacturing systems (FMSs) using Monte Carlo simulation. The model which is a development of Fazlollahtabar and Saidi-Mehrabad (2013), considers two features of automated flexible manufacturing systems equipped with automated guided vehicle (AGV) namely, the reliability of machines and the reliability of AGVs in a multiple AGV jobshop manufacturing system. The current methods for modeling reliability of a system involve determination of system state probabilities and transition states. Since, the failure of the machines and AGVs could be considered in different states, therefore a Markovian model is proposed for reliability assessment. The traditional Markovian computation is compared with a neural network methodology. Monte Carlo simulation has verified the neural network method having better performance for Markovian computations.We compare two approaches for a Markovian model in flexible manufacturing systems (FMSs) using Monte Carlo simulation. The model which is a development of Fazlollahtabar and Saidi-Mehrabad (2013), considers two features of automated flexible manufacturing systems equipped with automated guided vehicle (AGV) namely, the reliability of machines and the reliability of AGVs in a multiple AGV jobshop manufacturing system. The current methods for modeling reliability of a system involve determination of system state probabilities and transition states. Since, the failure of the machines and AGVs could be considered in different states, therefore a Markovian model is proposed for reliability assessment.

Semi-automated map creation for fast deployment of AGV fleets in modern logistics

Today, Automated Guided Vehicles (AGVs) still have a low market share in logistics, compared to manual forklifts. We identified one of the main bottlenecks in the rather long deployment time which involves precise 2D mapping of the plant, 3D geo-referencing of pick-up/ drop positions and the manual design of the roadmap. The long deployment time has various reasons: in state-of-the-art plant installations, designated infrastructure is still necessary for the localization; the mapping process requires highly skilled personnel; in many cases unavailable or inappropriate position information of drop points for goods must be corrected on site. Finally, the design of the roadmap, performed by expert technicians is manually optimised in a tedious process to achieve maximum flow of goods for the plant operator.In total the setup of a plant to be ready for AGV operation is taking several months, binding highly skilled technicians and involves very time-consuming and costly on-site procedures.Therefore, we present an approach to AGV deployment which aims to drastically reduce the time, cost and involved personnel. First, we propose the employment of a novel, industrial-ready SICK 3D laser scanning technology in order to build an accurate and consistent virtual representation of the whole warehouse environment. By utilizing suitable segmentation and processing a semantic map is generated that contains 3D geo-referenced positions as well as a 2D localization map eliminating the need for dedicated solution to 2D mapping. Second, the semantic map provides a free space map which is used as a basis for the automatic roadmap creation in order to achieve optimal flow.So, this paper proposes an innovative methodology for obtaining, in a semi-automated manner, the map of an industrial environment where a system of multiple AGVs will be installed with less time and cost.

A Probabilistic Eulerian Traffic Model for the Coordination of Multiple AGVs in Automatic Warehouses

The co-ordination of multiple automated guided vehicles (AGVs) is one of the main issues to address for the implementation of an efficient autonomous warehouse. Traffic jams are highly undesirable and, therefore, the motion of the AGVs should be planned by considering the current and future state of the fleet. This letter proposes a probabilistic model of the traffic in an autonomous warehouse to predict the evolution of possibly congested areas. Such a model is then exploited for building a predictive planner that is embedded in the traffic manager recently proposed in [1] to explicitely consider the evolution of the traffic up to a given horizon and to increase the efficiency of the fleet of AGVs in terms of delivery time. The proposed traffic manager is validated by means of comparative simulations on real plants.

Risk assessment for multiple automated guided vehicle manufacturing network

Automated guided vehicles (AGVs) are moving devices for material handling in a manufacturing system through a network of guide paths. The network is configured of nodes of work stations and arcs of guide paths. To obtain this more availability of the system having reliable arcs are desirable. Advanced manufacturing systems feasibility is considered via economic evaluation. Thus, this work proposes a risk based dynamic program to determine more reliable arcs for fortification purposes. With respect to the multi stage decision making process of the multiple AGVs on different arcs, we develop a dynamic program being a useful tool for multi stage decision making. To counteract the dynamism of data in different time periods, Bayesian approach is employed to determine the loss function of moving through the stages of the proposed AGV routing network. By increasing the number of nodes and AGVs the problem is considered in NP-hard class, and thus the required effort for optimization motivates to develop a heuristic solution approach.

Mathematical model for deadlock resolution in multiple AGV scheduling and routing network: a case study

Purpose This paper aims to propose and formulate a complicated routing/scheduling problem for multiple automated guided vehicles (AGVs) in a manufacturing system. Design/methodology/approach Considering the due date of AGVs requiring for material handling among shops in a jobshop layout, their earliness and tardiness are significant in satisfying the expected cycle time and from an economic view point. Therefore, the authors propose a mathematical program to minimize the penalized earliness and tardiness for a conflict-free and just-in-time production. Findings The model considers a new concept of turning point for deadlock resolution. As the mathematical program is difficult to solve with a conventional method, an optimization method in two stages, namely, searching the solution space and finding optimal solutions are proposed. The performance of the proposed mathematical model is tested in a numerical example. Practical implications A case study in real industrial environment is conducted. The findings lead the decision-makers to develop a user interface decision support as a simulator to plan the AGVs’ movement through the manufacturing network and help AGVs to prevent deadlock trap or conflicts. The proposed decision support can easily be commercialized. Originality/value The benefits of such commercialization are increase in the quality of material handling, improve the delivery time and prevent delays, decrease the cost of traditional handling, capability of computerized planning and control, intelligent tracking and validation experiments in simulation environment.

Optimising a multi-objective reliability assessment in multiple AGV manufacturing system

A mathematical model is developed to assess the reliability of machines and AGVs in flexible manufacturing systems (FMSs). The model considers two features of automated flexible manufacturing systems equipped with automated guided vehicle (AGV) namely, the reliability of machines and the reliability of AGVs in a multiple AGV jobshop manufacturing system. The optimisation objectives in the proposed model are maximising the total reliability of machines in shops in the whole jobshop system, maximising the total reliability of the AGVs, and minimising the total repair cost in the system. To solve the proposed multi-objective mathematical model, fuzzy goal programming is adapted and the results show the effectiveness and applicability of the proposed approach.

Petri Net Decomposition Approach to Optimization of Route Planning Problems for AGV Systems

In this paper, we propose a Petri Net (PN) decomposition approach to the optimization of route planning problems for automated guided vehicles (AGVs) in semiconductor fabrication bays. An augmented PN is developed to model the concurrent dynamics for multiple AGVs. The route planning problem to minimize the total transportation time is formulated as an optimal transition firing sequence problem for the PN. The PN is decomposed into several subnets such that the subnets are made independent by removing the original shared places and creating its own set of resource places for each subnet with the appropriate connections. The partial solution derived at each subnet is not usually making a feasible solution for the entire PN. The penalty function algorithm is used to integrate the solutions derived at the decomposed subnets. The optimal solution for each subnet is repeatedly generated by using the shortest-path algorithm in polynomial time with a penalty function embedded in the objective function. The effectiveness of the proposed method is demonstrated for a practical-sized route planning problem in semiconductor fabrication bay from computational experiments.