• Journal of Navigation and Port Research
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• v.32 no.10
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• pp.837-844
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• 2008

In an automated container terminal, various factors affect the operation of container handling equipment such as quay cranes and AGVs, and thus calculating the exact operation time is nearly infeasible. This uncertainty makes it difficult to dispatch AGVs well. In this paper, we propose a simulation-based AGV dispatching algorithm When dispatching an AGV to an operation, the proposed algorithm conducts multiple stochastic simulation for the succeeding AGV operations for the predetermined period to collect stochastic samples of the result of the dispatching. In the stochastic simulation, the uncertainty of crane operations is represented as a simple probability distribution and the operation time of a crane is determined according to this. A dispatching option is evaluated by the total delay time of quay cranes which is estimated by averaging the quay crane delay of each simulation In order to collect a sufficient number of samples that guarantee the credibility of the evaluation, we devised a high-speed simulator that simulates AGV operation The effectiveness of the proposed algorithm is validated by simulation experiments.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2575-2577
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• 2000

We experimented on AGV driving test with color CCD camera which is setup on it. This paper can be divided into two parts. One is image processing part to measure the condition of the guideline and AGV. The other is part that obtains the reference steering angle through using the image processing parts. First, 2 dimension image information derived from vision sensor is interpreted to the 3 dimension information by the angle and position of the CCD camera. Through these processes, AGV knows the driving conditions of AGV. After then using of those information, AGV calculates the reference steering angle changed by the speed of AGV. In the case of low speed, it focuses on the left/right error values of the guide line. As increasing of the speed of AGV, it focuses on the slop of guide line. Lastly, we are to model the above descriptions as the type of PID controller and regulate the coefficient value of it the speed of AGV.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.1
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• pp.1-13
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• 2003

In this paper, LAC(Look-ahead AGV Controller) has been developed for efficient routing of parts in semiconductor and LCD production systems. Several procedures have been developed as sub-modules. LACP(Look-ahead AGV Control Procedure) which controls AGVs using the information on the current and future status of the systems is the main element of the LAC. To support LACP, DSP(Destination Selection Procedure) which determines a destination of a part and AGV call time, SSP(Source Selection Procedure)which selects a part coming next to a buffer when the buffer becomes available. and RTM(Response Time Model) which estimates empty travel time of AGVs and waiting time for an available AGV have been developed. A simulation experiment shows that LAC reduces part's flow time, AGV utilization, average and maximum inventory level of a central buffer, empty travel time of an AGV, and waiting time for an available AGV.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.67-70
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• 2000

AGV 시스템은 자동화된 물류 시스템에 있어서 필수적인 요소이다 하지만 시스템의 규모가 커질수록, 운반할 작업량이 증가할수록 차량의 운영에 있어서 많은 어려움이 발생된다. Tandem AGV 시스템은 전체 작업장을 몇 개의 지역으로 분할하고 각 지역에 한 대의 차량을 할당함으로써 이러한 운영의 문제점을 한번에 해결한 시스템이다. 본 논문에서는 Multi-load AGV를 사용하는 Tandem AGV 시스템의 설계에 필요한 작업장 분할 방법을 제시하고 시뮬레이션을 통해서 그 성능을 평가한다.

• 연구논문집
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• s.22
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• pp.27-38
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• 1992

We deals with stationary layout control system of AGV. It is a intelligent control system to he wholly charged control PC a layout information and guided command and to be controlled a vehicle driving, steering, safety of natural functions of AGV. Fieldbus concentrator of stationary layout control system serves control command from control PC and status information of AGV. Telegram software monitors transmitted command and status information through IR(JnfraRed) modem. Especially it is possible to easily network to use not an exclusive controller of AGV but personal computer(PC) when communicate and interface a different kind of controller.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.1117-1123
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• 2005

This paper deals with a new automated guided vehicle (AGV) control system for distributed control. Proposed AGV control system adapts the multi-agent technology. The system is composed of two types of controller: routing and order. The order controller is in charge of assignment of orders to AGVs. Through the bidding-based negotiation with routing controllers, the order controller assigns a new order to the proper AGV. The order controller announces order information to the routing controllers. Then the routing controllers generate a routing schedule for the order and make a bid according to the routing schedule. If the routing schedule conflicts with other AGV's one, the routing controller makes an alternative through negotiation with other routing controllers. The order controller finally evaluates bids and selects one. Each controller consists of a set of agents: negotiation agent, decision making agent and communication agent. We focus on the agent architecture and negotiation-based AGV scheduling algorithm. Proposed system is validated through an exemplary scenario.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.563-566
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• 2006

In this paper, we studied about AGVs modeling and material handling automation simulation using a virtual AGV. The proposed virtual AGV model that operates independently each other is based on a real AGV. Continuous straight-line and workstation model using vector drawing method that could easily, rapidly work system modeling are suggested. Centralized traffic control, which could collision avoidance in intersection and should not stop AGV as possible, and algorithm for detour routing which performs when another AGV is working in pre-routed path are proposed. The traffic control and the algorithm have been proved efficiently by simulation.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.70-74
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• 2018

In order to ensure the flow of goods available and more flexible, reduce labor costs, many factories and industrial zones around the world are gradually moving to use automated solutions. One of them is to use Automated guided vehicles (AGV). Currently, there are a line tracing method as an AGV operating method, and a method of estimating the current position of the AGV and matching with a factory map and knowing the moving direction of the AGV. In this paper, we propose ceiling Light and guided line based moving direction estimation algorithm using multi-camera on the AGV in smart factory that can operate stable AGV by compensating the disadvantages of existing AGV operation method. The proposed algorithm is able to estimate its position and direction using a general - purpose camera instead of a sensor. Based on this, it can correct its movement error and estimate its own movement path.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.391-393
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• 2005

Automatic guided vehicle(AGV) in the factory has an important role to advance the flexible manufacturing system. In this paper, we propose a novel object-transportation control algorithm of cooperative AGV systems to apply decentralized control scheme based on virtual-passivity. It is shown that the cooperative AGV systems ensure stability and the convergence to scaled multiple of each desired velocity field for multiple AGV systems. Finally, the application of proposed virtual passivity-based decentralized control algorithm via system augmentation is applied to be the tracking a circle. Also. the simulation results for the object-transportation by two AGV systems illustrate the validity of the proposed control scheme.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.17-18
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• 2017

본 논문은 스마트 팩토리의 중요 요소인 무인반송차(AGV)를 자율 주행시키기 위해 오픈 소스 자율 주행차 시뮬레이터인 udacity를 이용해 머신 러닝시키는 시스템을 개발하였다. 공장의 운행 루트를 자율주행 시뮬레이터의 전경으로 가공하고, 3개의 카메라를 부착시킨 AGV를 운행시키면서 머신 러닝시킨다. AGV를 주행하여 얻어진 여러 학습 데이터를 통해 도출된 결과들을 각각 비교하여 우수한 모델을 선정하고 운행시킨 결과 AGV가 정해진 운행 루트를 정확하게 주행하는 것을 확인하였다. 이를 통해, 가상 운행 환경에서 저비용으로 AGV 운행 학습이 가능하다는 것을 보였다.