Decentralized Traffic Control for Non-Holonomic Flexible Automated Guided Vehicles in Industrial Environments

This paper deals with the problem of coordinating flexible automated guided vehicles (AGVs) in real manufacturing systems. The problem consists of ensuring safe and successful task execution while several AGVs operate as a distributed transportation system in real industrial environments. The proposed solution combines different decentralized techniques to increase the flexibility and scalability of the multirobot system. The coordination is addressed by dividing the problem into path planning, obstacle avoidance and traffic control problems. The path planning method takes into account the location of mates for replanning the routes. The obstacle avoidance technique considers the kinematic constraints of the platform for reactive motion control. The traffic control approach makes use of a decentralized control policy that takes into account the capabilities of vehicles. By combining all these techniques and configuring the system properly, we present the successful development of a distributed transportation system composed of a team of flexible AGVs. The proposed solution has been validated using both a set of custom-modified AGVs operating in a real factory and a simulation of several AGVs operating in a virtual scenario.     

Immunity-based autonomous guided vehicles control

The human immune system is a self-organizing and highly distributed multi-agent system. These properties impart a high degree of robustness and performance that has created great interest in implementing engineering systems. This adopted engineering analogue is called artificial immune system (AIS). This paper presents an immunity-based control framework, which has the ability to detect changes, adapt to dynamic environment and coordinate vehicles activities for goals achievement, to deploy a fleet of autonomous guided vehicles (AGVs) for material handling in an automated warehouse. A robust and flexible automated warehousing system is achieved through the self-organized and fully decentralized origination of AGVs.     

Network model and effective evolutionary approach for AGV dispatching in manufacturing system

Automated guided vehicles (AGVs), are the state-of-the-art, and are often used to facilitate automatic storage and retrieval systems (AS/RS). In this paper, we focus on the dispatching of AGVs in a flexible manufacturing system (FMS). A FMS environment requires a flexible and adaptable material handling system. We model an AGV system by using network structure. This network model of an AGV dispatching has simplexes decision variables with considering most AGV problem’s constraints, for example capacity of AGVs, precedence constraints among the processes, deadlock control. Furthermore, these problems can be solved by using a lot of heuristic algorithms as network optimization problems. We are also proposed an effective evolutionary approach for solving a kind of AGV’s problems in which minimizing time required to complete all jobs (i.e. makespan) and minimizing the number of AGVs, simultaneously. For applying an evolutionary approach for this multicriteria case of AGV problem, priority-based encoding method and Interactive Adaptive-weight GA (i-awGA) were proposed. Numerical analyses for case study show the effectiveness of proposed approach. Received: June 2005 / Accepted: December 2005     

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.     

The capacitated multi-AGV scheduling problem with conflicting products: Model and a decentralized multi-agent approach

Automated guided vehicles (AGVs) are a key technology to facilitate flexible production systems in the context of Industry 4.0. This paper investigates an optimization model and a solution using a decentralized multi-agent approach for a new capacitated multi-AGV scheduling problem with conflicting products (CMASPCP) to take full advantage of AGVs. The novelty of the problem and our model lies in the introduction of AGV capacity constraints and constraints arising from conflicting products, i.e. products that cannot be transported together. As the new I4.0 paradigm tends towards decentralized control, we also present a decentralized multi-agent approach in which AGVs autonomously coordinate to solve the task. The performance of the proposed decentralized approach is compared to a mixed-integer linear programming model on a set of 110 problem instances with different sizes and degrees of complexity. The obtained results show that the proposed decentralized multi-agent approach is effective and competitive in terms of the solution quality and computational time.     

A route and speed optimization model to find conflict-free routes for automated guided vehicles in large warehouses based on quick response code technology

This paper focuses on exploring conflict-free routes for automated guided vehicles (AGVs) based on quick response (QR) code technology. Intelligent warehousing has become an industrial development trend, with an increasing number of enterprises deploying AGVs based on QR code technology for transportation tasks, especially in large warehouses. Unlike the early single lane path following navigation systems, AGVs using QR code technology can freely switch tracks at any position. And two AGVs can be accommodated side by side. The conditions of conflict-free routes are analyzed firstly when AGVs use QR code navigation system. After that, a route and speed optimization model (RSOM) that aims at minimizing energy consumption and taking conflict-free routes with time windows as the constraint condition is established. Then a branch-and-bound (B&B) algorithm scheme is proposed by altering AGV routes or speed to arrive at schemes with conflict-free routes to complete transportation tasks. Lastly, the effectiveness and validity of our proposed model and algorithm are verified by numerical examples and a case study. The results show that resolving conflicts during route planning for AGVs guided through QR code have advantages over avoiding collision in the running phase by technology and path following techniques in both aspects of solution time and energy. In addition, the established B&B algorithm has better performance than the rapid-exploring random tree (RRT) algorithm in numerical examples. The gaps between them widen as the size of AGVs increases. The research work has potential for application to meet the requirements of AGV development.     

A study for further exploring the advantages of using multi-load automated guided vehicles

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.     

Comparison of operations of AGVs and ALVs in an automated container terminal

In an automated container terminal, the automated guided vehicles (AGVs) and the automated lifting vehicles (ALVs) are the most popular candidates to be used for transporting containers between the quayside and the storage yard. In this paper, we compare the operational productivities of the two types of vehicles when used in combination with the quay cranes of various performances. We assume a flexible path layout in which the vehicles can move almost freely in any vertical and horizontal directions. The traffic control scheme employed in our simulation finds a minimum- time route and schedules the travel to avoid deadlocks. Simulation experiments show that the ALVs reach the same productivity level as the AGVs using much less number of vehicles due to its self-lifting capability. However, the results also reveal that the AGVs eventually catch up the performance of the ALVs in most cases if the number of vehicles given is large enough. An exception is when the tandem double-trolley QCs are used for loading, in which case the AGVs cannot catch up the ALVs no matter how many more vehicles are added.     

A simulation study of automated guided vehicle dispatching

This paper reports a simulation study to investigate the relative performance of five automated guided vehicle (AGV) dispatching rules. The study found that distance-based rules are most effective while AGV attribute-based rules are least effective. Both AGV speed and the number of AGVs in use are factors critically affecting the performance of a flexible manufacturing system. This paper demonstrates that computer simulation is a viable tool for assisting FMS design and evaluation.     

Distributed route planning for multiple mobile robots using an augmented Lagrangian decomposition and coordination technique

To enable efficient transportation in semiconductor fabrication bays, it is necessary to generate route planning of multiple automated guided vehicles (AGVs) efficiently to minimize the total transportation time without collision among AGVs. In this paper, we propose a distributed route-planning method for multiple mobile robots using an augmented Lagrangian decomposition and coordination technique. The proposed method features a characteristic that each AGV individually creates a near-optimal routing plan through repetitive data exchange among the AGVs and local optimization for each AGV. Dijkstra's algorithm is used for local optimization. The optimality of the solution generated by the proposed method is evaluated by comparing the solution with an optimal solution derived by solving integer linear programming problems. A near-optimal solution, within 3% of the average gap from the optimal solution for an example transportation system consisting of 143 nodes and 14 AGVs, can be derived in less than 5 s of computation time for 100 types of requests. The proposed method is implemented in an experimental system with three AGVs, and the routing plan is derived in the configuration space, taking the motion of the robot into account. It is experimentally demonstrated that the proposed method is effective for various problems, despite the fact that each route for an AGV is created without minimizing the entire objective function.     

A program for dispatching and routing AGVs

This paper describes a program for dispatching and routing of AGVs in real time in a flexible manufacturing environment. The AGVs are assumed to travel on a bidirectional guidepath. Given a set of open jobs, job priorities, their production routings, number of AGVs, AGV travel speeds and their loading/unloading times, the program decides which AGV to dispatch and the path to be followed by the AGV. Continuous monitoring of the system is made possible by a graphical display of AGV movements. The program can also be used to evaluate the design of the AGV system. Some computational results are presented to illustrate this.     

A network flow based heuristic approach for optimising AGV movements

Automated Guided Vehicles (AGVs) are driverless carriers that automatically navigate along planned paths by means of several guidance and control methods. This paper proposes an approach for solving the dispatching problem in an AGV system. The problem is modelled through a network by relying on the formulation of a Minimum Cost Flow Problem. In the defined graph, the nodes represent transportation tasks and AGVs while the arcs consider, through the associated weights, several system’s aspects such as pick, drop, and travel times, battery recharging, capacity constraints, congestion and error issues. Two objectives can be achieved: (i) minimisation of the average time for carrying out transportation tasks or (ii) maximisation of the utilisation degree of AGVs. The modelling and solution approach adopted has provided a novel Vehicle–Initiated dispatching rule and parameters settings for the dynamic assignments of transportation missions to AGVs. The decision making process concurrently and dynamically considers several factors. The results show a relevant reduction in the average time for transportation order fulfilment and a decrease in its variability. The proposed approach has been exploited for optimising the AGVs performance in a pharmaceutical production system.     

A distributed routing method for AGVs under motion delay disturbance

In real transportation environments for semi-conductor manufacturing, unexpected disturbances such as motion delays and/or sudden requests for transportation may cause collisions among AGVs increasing total transportation time. It is required to generate a collision-free routing for multiple automated guided vehicles (AGVs) within a few seconds. In this paper, we propose a distributed routing method under motion delay disturbance for multiple AGVs. The proposed method features a characteristic that each AGV subsystem derives its optimal route to minimize the sum of the transportation time and the penalties with respect to collision probability with other AGVs. The penalties reflect the collision probability distribution function for motion delay disturbances. The proposed method is applied to a routing problem for transportation in the semiconductor fabrication bay with 143 nodes and 20 AGVs. The computational results show that the total transportation time obtained by the proposed method is shorter than that of the conventional method. For dynamic transportation environments, an optimal timing for re-routing multiple AGVs under motion delay disturbance is systematically determined by the tradeoff relationship between the total computation time to derive a solution and the uncertainties for re-routings. Markov chain is used to represent uncertainty distribution for re-routings. The proposed method is implemented in an experimental transportation system with 51 nodes and 5 AGVs. The experimental results demonstrate that the proposed method is applicable to real transportation environments.     

含有AGV的柔性车间调度优化研究

实际车间生产中,为了实现自动化生产越来越多企业投资AGV（Automated guided vehicle）来搬运工件。为了研究AGV最优调度方案和最佳AGV数量,本文建立使用AGV搬运的柔性车间调度模型。针对该模型的特点,本文提出基于搬运工序、机床和AGV分配的粒子编码方式,并结合遗传算法的思想采用新的粒子群算法更新位置方式。通过数值算例验证了改进粒子群算法的有效性和可行性,并发现AGV符合边际效用递减规律,即随着AGV数量的增加,总加工时间减少,但每增加一台AGV缩短的时间变少。     

协作机器人的构型分析研究

针对协作机器人的构型分析研究,得出了偏置是影响机器人构型的一个重要因素。首先,对现有典型协作机器人的构型进行分析,通过构型间的内在联系与演化过程得到构型之间的差异。然后利用构型之间的差异引出了偏置的定义,并通过偏置对关节运动范围的影响将其分为S型偏置和Y型偏置。最后以全局性能指标、工作空间体积指标和灵活工作空间指标为性能评价标准,对iiwa、Sawyer、Yumi三种典型构型的性能进行了仿真。通过对比分析可知,偏置的存在使构型的全局性能和工作空间体积性能降低,但对灵活工作空间性能有提高作用。结果表明偏置是影响机器人性能的重要因素,该分析研究为协作机器人的构型设计提供了理论依据。     

Hybrid cost and time path planning for multiple autonomous guided vehicles

In this paper, simultaneous scheduling and routing problem for autonomous guided vehicles (AGVs) is investigated. At the beginning of the planning horizon list of orders is processed in the manufacturing system. The produced or semi-produced products are carried among stations using AGVs according to the process plan and the earliest delivery time rule. Thus, a network of stations and AGV paths is configured. The guide path is bi-direction and AGVs can only stop at the end of a node. Two kinds of collisions exist namely: AGVs move directly to a same node and AGVs are on a same path. Delay is defined as an order is carried after the earliest delivery time. Therefore, the problem is defined to consider some AGVs and material handling orders available and assign orders to AGVs so that collision free paths as cost attribute and minimal waiting time as time attribute, are obtained. Solving this problem leads to determine: the number of required AGVs for orders fulfillment assign orders to AGVs schedule delivery and material handling and route different AGVs. The problem is formulated as a network mathematical model and optimized using a modified network simplex algorithm. The proposed mathematical formulation is first adapted to a minimum cost flow (MCF) model and then optimized using a modified network simplex algorithm (NSA). Numerical illustrations verify and validate the proposed modelling and optimization. Also, comparative studies guarantee superiority of the proposed MCF-NSA solution approach.     

基于AGVs路径规划的物流智能调度系统设计与实现

为了解决现有物流调度系统低效缓慢、容错率低的问题,设计了基于自动导引运输车(Automated Guided Vechicle, AGV)和路径规划优化算法的物流智能调度系统。系统搭配了AGV的物流调度硬件,又结合了路径规划理论,开发了基于Petri网络的智能路径规划算法。通过算法性能对比得知,路径规划算法设计了最优调度路径,确保了较高的准确率和工作效率。系统测试结果显示,基于AGVs路径规划的物流智能调度系统能够在各种物流环境或者库房基地完成调度任务,很好地解决了物流企业在忙碌期的繁杂调度问题。基于AGVs路径规划的物流智能调度系统提高了物流调度的自动化程度,保证了物流调度和道路运输的效率,有效推动了商业模式和市场规范的发展。     

Mission design for a group of autonomous guided vehicles

In this paper, a generic approach for the integration of vehicle routing and scheduling and motion planning for a group of autonomous guided vehicles (AGVs) is proposed. The AGVs are requested to serve all the work stations in a two-dimensional environment while taking into account kinematics constraints and the environment’s geometry during their motion. The problem objective is the simultaneous determination of time-optimum and collision-free paths for all AGVs. The proposed method is investigated and discussed through a number of simulated experiments using a variety of environments and different initial conditions.     

Evaluation of automated guided vehicle systems by simulation

An experimental study was conducted in this research to evaluate the design of Automated Guided Vehicle (AGV) systems using discrete event simulation. Given the layout of work stations and several types of jobs to be processed in the system, the constituent elements of the AGV system, including the number of AGVs, the speed of the vehicles, the number of machines at each station, the number of load and unload stations, rules for selection of AGV, routing criteria, and track intersection priorities, were defined in a SIMAN model. Pilot runs were performed first to determine the values of the system parameters which may affect the performance of the AGV systems. A factorial design was then used to generate simulation experiments. Finally, a number of simulation runs were executed under different experimental conditions to obtain preliminary statistics on the following performance measures: utilization of AGVs, average number of jobs waiting in the queue for an AGV, average system throughput time, average waiting time in intersections and total waiting time in queues.