A review of design and control of automated guided vehicle systems <s> Decentralized control system <s> Abstract A highly decentralized greedy heuristic will enable a fleet of automated guided vehicles to deliver unit loads quickly on a simple loop track. <s> BIB001 </s> A review of design and control of automated guided vehicle systems <s> Decentralized control system <s> With the increasing trend in the use of automated guided vehicles systems (AGVS) in manufacturing, the need for design tools becomes greater. The problem is that optimized static design models do not ensure expected performance due to the difficulty in capturing the dynamic operational characteristics of the vehicle system. This study shows that by using the single-loop guide path configuration some of the dynamic features of the system, like the impact the empty vehicle flow has on the system's performance, is reduced. Thus, a more reliable prediction of system performance is achieved <s> BIB002 </s> A review of design and control of automated guided vehicle systems <s> Decentralized control system <s> Decentralized control of an autonomous automated guided vehicle system (AGVS) used for material handling is expected to lead to high system flexibility and robustness. A complete control methodology for decentralized autonomous AGVS control was developed and implemented in a computer-integrated manufacturing environment. The methodology addresses all aspects of AGVS functionality: system management, navigation and load transfer. Hierarchical fuzzy behaviour-based control, a reactive navigation scheme, was expanded to multirobot control in semistructured environments by incorporating a priori path optimization and right-of-the-way determination. <s> BIB003 </s> A review of design and control of automated guided vehicle systems <s> Decentralized control system <s> Agent technology has been considered as an important approach for developing distributed intelligent manufacturing systems. A number of researchers have attempted to apply agent technology to manufacturing enterprise integration, supply chain management, manufacturing planning, scheduling and control, materials handling, and holonic manufacturing systems. This paper gives a brief survey of some related projects in this area, and discusses some key issues in developing agent-based manufacturing systems such as agent technology for enterprise integration and supply chain management, agent encapsulation, system architectures, dynamic system reconfiguration, learning, design and manufacturability assessments, distributed dynamic scheduling, integration of planning and scheduling, concurrent scheduling and execution, factory control structures, potential tools and standards for developing agent-based manufacturing systems. An extensive annotated bibliography is provided. <s> BIB004
Decentralized control systems dispatch vehicles based on local information only. There is no system to coordinate between AGVs and the central control system. Traditionally, vehicle systems have been implemented and analyzed assuming that every vehicle is allowed to visit any P/D location in the system. One of the simplest implementations is one in which vehicles circulate in a unidirectional single-loop. BIB001 study a decentralized heuristic to control AGVs in a simple loop. In their research, an AGV, which can carry up to three loads, travels in a simple unidirectional loop and transports loads according to the FEFS rule. With the FEFS rule, the AGV circulates a loop continuously. Whenever the vehicle has space available, it picks up the first load it encounters, which will then be delivered whenever the destination is reached. BIB002 provide another study that investigates the performance of single-loop systems. The smart vehicles mentioned in the introduction are modern examples of decentralized control vehicles. BIB003 propose a hierarchical, fuzzy behavior-based methodology to control a AGV system. Central knowledge about the system's state is not available. Agents, representing the smart AGVs, collect the workstations' statuses directly and dynamically decide their next task.  also uses agent-technology to determine the best, conflict-free route an AGV can take (see section 4.4). The agenttechnology becomes more and more important to control AGVs in intelligent manufacturing systems BIB004 .