AGV自动导航车毕业论文中英文资料外文翻译文献.doc

中英文资料 AGV自动导航车 中英文资料外文翻译文献附录 中文译文AGV介绍 汽车为基础的内部交通系统采用自动导引车（AGV）用通常使用的设施，如制造工厂，仓库，配送中心和中转站。他们被称为自动导引车系统（AGVS）。附图1给出了一个这样的AGVS中在配送中心的计算机硬件和软件（德科斯特等，2004年），其中运输导向车（托盘）地点之间负载，如接收行车线，以储存区，储存区和航运通道。 AGVS中的一个流程的设计和控制涉及的许多问题。其中主要有：引导路径设计，估算所需的车辆数目（或确定所需车辆），车辆调度，闲置车辆定位，电池管理，车辆路由和僵局的解决。他们属于在决策过程中的不同层面。该指南路径设计可以被看作是在战略层次问题。在现阶段，决定在其他级别上的决策产生巨大影响。在战术层面的问题包括估计的车辆数目，调度车辆（车辆调度决策都可能属于战术和业务水平），定位闲置车辆，管理电池充电计划。最后，车辆路由，死锁的决议（和预防）问题解决在业务水平。在设计和控制过程，一些互动和迭代步骤之间可以看到。例如，该指南路径系统的类型，直接影响到车辆的数目和所需的车辆调度系统的复杂性。 附图1 一个配送中心引导车系统传统的自动导引车系统使用固定的车辆引导，路径。现代AGV系统不同于作为经典的描述，例如，在Jnemann和施密特（2000年）和汤普金斯等人（2003年）的书籍中的描述。在几个方面。而不是用固定的路径，许多现代AGV的是自由放养的，这意味着他们的首选曲目是软件编程，并且可以比较容易地改变时，新站或流量增加。第二个区别是在他们可以控制的方式。代理技术可以决定由这些智能车辆采取的，在过去采取的是由中央控制器。这导致自适应，自学习系统，是特别适合大，许多车辆和巨大的潜力车辆干扰的复杂系统。这些事态发展并不意味着传统的决策问题变得过时。相反，他们的研究带来新的挑战。我们都讨论的传统AGVS中的决策问题和使用的决策自由放养的AGV的影响。AGV系统上有一些评论文章。然而，他们集中讨论的问题（Qiu等人只有有限的部分（2002年）的重点调度和路由的问题），或者是不是最新的（Co和Tanchoco，1991年；King and Wilson, 1991； Sinriech，1995年）。此外，他们忽视了如闲置车辆定位和电池管理的一些地区。本文试图填补这一空白，提供了现有文献，包括最近的贡献扩大的概述，并结构设计与控制AGVS中的一个过程。对于每一个方面，我们审查和分类的关键决策模型。此外，一个新的调度规则的分类，选择一个合适的调度系统以及用于设计和控制决策的AGVS中提出的框架方针。我们还认为闲置车辆定位和电池管理问题，其中许多评论文章忽视。最后，我们提出了一些富有成效的未来研究方向。 引导路径设计 引导路径的设计是在AGVS中设计和控制的重要问题。它是第一个要考虑的问题之一。关于指导路径的设计问题发表的作品大多假设设施布局和皮卡地点/交货（P / D级）站，并给出固定的。主要的问题是决定连接或指导路径段被列入该解决方案。在某些情况下，平行通道的连接数是必须作出决定。这种优化的问题也需要在物资流动的设施部门之间。此信息被用来构建一个“从做”流程图，是为引导路径设计问题的必要。在网络流量模型，车辆引导，路径通常为代表，这样过道路口;回升和交付（P / D级）的位置，可为节点审议了关于一组互相连接的弧线图形。该弧描述的路径可以遵循的车辆移动时，从节点到节点。定向弧注明车流方向。成本可以分配给每个弧代表两者之间的一个部分或车辆须沿圆弧终点的时间距离。该网络流模型可以转换为0-1整数优化模型。一个指导路径设计问题的主要目的是减少汽车的总旅行距离。缺少信息是引导路径设计的一个重要问题。例如，一个仓库内的物资流是可以改变的随着时间的推移，很难估计。 引导路径系统大致可分为附表1所示的特征。该流程拓扑描述了引导路径网络的复杂性。在简单的情况下，指导路径系统由单回路只有一个。几个循环组合在一起形成一个串联配置。一个传统的拓扑结构是一个复杂的网络路径，十字架，快捷方式和路口。如果一个网络路径区段可能只包含一个或几个平行线通道。旅游车辆可以只在一个方向（单向）或双向（双向）。 附表1 特征引导路径系统流拓扑数平行线流动方向常规单线单向流动单回路多重通道双向流串联选择一本指南，道路系统的适当类型是重要的。不幸的是，没有为它指引。该指南路径类型通常是选择基于设施的特点和设计师的经验。专家系统可以是有益的支持引导路径系统的选择过程。在选择的指导路径系统的适当的类型，设计人员可以使用一个合适的（数学）模式以获得最佳的引导路径系统。在实践中，传统的引导路径系统经常可以看到在仓库和配送中心（德科斯特等，2004）；单回路系统使用，例如，在跨码头中心。串联配置可能会制造更多的地方工作站制造单元组合成合适的环境。附录 英文原文IntroductionVehicle-based internal transport systems using automated guided vehicles (AGVs) are commonly used in facilities such as manufacturing plants, warehouses, distribution centers and transshipment terminals. They are referred to as automated guided vehicle systems (AGVSs). Fig. 1 gives an example of such an AGVS in a distribution center of computer hard and software (De Koster et al., 2004), in which guided vehicles transport (pallet) loads between locations, e.g. from receiving lanes to storage areas, and from storage areas to shipping lanes. The design and control processes of an AGVS involve many issues. The main ones are: guide-path design, estimating the number of vehicles required (or determining vehicle requirements), vehicle scheduling, idle-vehicle positioning, battery management, vehicle routing and deadlock resolution. They belong to different levels of the decision-making process. The guide-path design can be seen as a problem at strategic level. The decision at this stage has a strong impact on decisions at other levels. Issues at tactical level include estimating the number of vehicles, scheduling vehicle (vehicle scheduling decision may belong to both tactical and operational levels), positioning idle vehicles and, managing battery-charging scheme. Finally, vehicle routing, deadlock resolution (and prevention) problems are addressed at operational level. During the design and control processes, some interactions and iterations can be seen between steps. For example, the type of the guide-path system directly influences the number of vehicles required and the complexity of the vehicle scheduling system.Fig. 1The guided-vehicle system of a distribution centerTraditional AGV systems use fixed guide-paths for vehicles. Modern AGV systems differ from the classic ones as described, for instance, in the books of Jnemann and Schmidt (2000) and Tompkins et al. (2003) in several respects. Rather than using fixed paths, many modern AGVs are free-ranging, which means their preferred tracks are software programmed, and can be changed relatively easily when new stations or flows are added. A second difference is in the way they can be controlled. Agent technology allows decisions to be taken by these smart vehicles that in the past were taken by central controllers. This leads to adaptive, self-learning systems and is particularly appropriate for large, complex systems with many vehicles and much potential vehicle interference. These developments do not imply that the traditional decision-making problems become obsolete. Rather, they lead to new challenges for research. We both discuss traditional AGVS decision-making problems and impacts of using free-ranging AGVs on decision-making.There are few review papers on AGV systems. However, they concentrate on only limited parts of the problem (Qiu et al. (2002) focus on scheduling and routing problems) or are not up to date (Co and Tanchoco, 1991; King and Wilson, 1991; Sinriech, 1995). Moreover, they ignore some areas such as idle-vehicle positioning and battery management. This paper attempts to fill this gap, by giving an extended overview of existing literature, including the most recent contributions, and also structures the design and control processes of an AGVS. For each area, we review and classify key decision models. In addition, a new classification for dispatching rules, a guideline for selecting a suitable scheduling system and a decision framework for design and control of an AGVS are proposed. We also consider idle-vehicle positioning and battery management problems, which many review papers neglect. Finally, we suggest some fruitful future research directions.Guide-path designGuide-path design is an important issue in AGVS design and control. It is one of the very first problems to be considered. Most published works on the guide-path design problem assume that facility layout and locations of pickup/delivery (P/D) stations are given and fixed. The main problem is to decide the connections or guide-path segments to be included in the solution. In some cases, the number of parallel lanes of a connection is to be decided as well. This optimization problem also needs the material flows between departments in the facility. This information is used to construct a “fromto” flowchart which is necessary for the guide-path design problem. In a network flow model, vehicle guide-paths are usually represented such that aisle intersections; pick-up and delivery (P/D) locations can be considered as nodes on a graph connected by a set of arcs. The arcs describe the paths that vehicles can follow when moving from node to node. Directed arcs indicate directions of vehicle flows. Cost can be assigned to each arc representing the distance between the two end points of a segment or the time required by a vehicle to travel along the arc. The network-flow model can be translated to a 01 integer optimization model. The main objective of a guide-path design problem is minimizing the total vehicle travel distance. Information shortage is an important problem for guide-path design. For example, the flow of materials within a warehouse can be changed over time and it is difficult to estimate.Guide-path systems can be classified roughly by characteristics indicated in Table 1. The flow topology describes the complexity of the guide-path network. In the simplest case, the guide-path system consists of only one single loop. Several loops grouped together form a tandem configuration. A conventional topology is a complicated network with paths, crosses, shortcuts and junctions. A path segment in a network may contain only one lane or few parallel lanes. Vehicles can travel a lane in only one directi