• Journal of the Korean Society for Precision Engineering
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• v.22 no.9 s.174
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• pp.107-114
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• 2005

With the recent progress in flexible manufacturing systems (FMS) in industry, increasing attention has been given to Automatic Guided Vehicle (AGV) systems. An AGV is a self-powered unit for transporting materials between stations without needing to be controlled by an operator. Such a system has several sensors to recognize the external state, and it is designed to travel between stations automatically without external assistance. To manage each device automatically in real time it requires a distributed controller with a main computer as the host, as well as a number of micro-controllers. In this study, an AGV system with dual motor drive was constructed. A Pentium 4 personal computer was set up as the main host for the distributed control, and this communicated with other micro-controllers in the management of the motor. The speed of each motor was also controlled by a micro-controller.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.5
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• pp.241-248
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• 2000

Until now, all of the port goods are transported manually by container transporter in the port. Recently there are a lot of studies about unmanned vehicle driven automatically. In terms of the vehicle automation, the control of steering and velocity on vehicle systems is very important part in container transporter. In common sense, vehicle systems have lots of nonlinear parameters so we have many difficulties in designing the optimal controller of them. In this paper, we present a design of the TDOF PID controller using a hybrid schematic algorithm to control the steering system optimally. We used the single-track model to pre-test the designed controller before appling to AGV. We also used the ES(evolutionary strategy) and SA(simulated annealing) algorithms to construct the hybrid tuning algorithm for parameters of controller. Finally, we had the computer simulation to verify that our designed controller has better performance than the other one.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.151-151
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• 2000

The objective of this paper is to develop the new robust and adaptive control system against external environments as applying the probabilistic recognition which is one of the inherent properties of immune system, ability of learning and memorization, and regulation theory of immune network to the system under engineering point of view. In this paper, HIA(Humoral Immune Algorithm) PID controller using Neural Network Identifier was proposed to drive the autonomous guided vehicle(AGV) more effectively. To verify the performance of the proposed HIA PID controller, some experiments for the control of steering and speed of that AGV are performed.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.120.3-120
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• 2002

1. Introduction $\textbullet$ Immune system is an evolutionary biological system to protect innumerable foreign materials such as virus, germ cell, and etc. Immune algorithm is the modeling of this system's response that has adaptation and reliableness when disturbance occur. $\textbullet$ In this paper, Immune algorithm is applied to the Steering Controller of AGV in container yard. $\textbullet$ And then the computer simulation result from the viewpoint of yaw rate and lateral displacement is analyzed and compared with result of conventional PID controller. 2. Dynamic Modeling of AGV $\textbullet$ Dynamic modeling has high degree of freedom. But, basic assumptions of this model are that the center of gravity(CG)...

• Journal of the Korean Society for Precision Engineering
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• v.21 no.10
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• pp.65-77
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• 2004

In this paper, we propose an adaptive mechanism based on immune algorithm and neural network identifier technique. It is also applied fur an autonomous guided vehicle (AGV) system. When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged due to the abrupt change of PID parameters since the parameters are almost adjusted randomly. To solve this problem, we use the neural network identifier (NNI) technique fur modeling the plant and humoral immune algorithm (HIA) which performs the parameter tuning of the considered model, respectively. After the PID parameters are determined in this off-line manner, these gains are then applied to the plant for the on-line control using an immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough initially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. Finally, the simulation and experimental result fur the control of steering and speed of AGV system illustrate the validity of the proposed control scheme. These results for the proposed method also show that it has better performance than other conventional controller design methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.54-59
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• 2010

This paper present to study the guidance system as localization technique using sensor fusion and line tracking technique using virtual line for AGV(autonomous guided vehicle). An existing AGV could drive on decided line only. And representative guidance systems of such guidance system are magnet-gyro guidance and wired guidance. However, those have had the high cost of installation and maintenance, and the difficulty of system change according to variation of working environment. To solve such problems, we make the localization system which is fused with a laser navigation and gyro, encoder. The system is robust against noise, and flexible according to working environment through sensor fusion. For line tracking of laser navigation without wire guidance, we set the virtual line in program, and design the driving controller based on difference of angle and distance between AGV's position and decided virtual line. To experiment, we use the AGV which is made by ourselves, and experiment the line tracking repeatedly on same experimental environment. In result, maximum distance error between decided virtual line and AGV's position was less than 49.93mm, and we verified that the proposed system is efficient for line tracking of actual AGV.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.522-524
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• 1998

In this paper, the cross-coupling controller with fuzzy logic for AGV is developed, Cross-coupling control directly minimizes orientation' error by coordinating the motion of the two drive wheels and uses PI controller for compensation. But, the transient response of PI controller results in deviation from trajectory. The Fuzzy Cross-coupling controller enhances transient performance without steady-state error. The performance of the above controller is demonstrated by simulation and is compared with that of PI controller.

• Journal of Korean Port Research
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• v.14 no.3
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• pp.291-301
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• 2000

T his paper presents the lateral and longitudinal control algorithm for the driving of a 4WS AGV(Automated Guided Vehicle). The control law to the lateral and longitudinal control of the AGV includes adaptive agin tuning ability, that is the controller gain of the gravity compensated PD controller can be changed on a real-time. The gain tuning law is derived from the Lyapunov direct method using the output error of the reference model and the actual model, And to show the performance of the presented lateral and longitudinal control algorithm, we simulate toe nonlinear AGV equations of the motion by deriving the Newton-Euler Method, The read path is from quay yard area to docking position in loading yard area. The quay yard area is where the quay crane loads the container to the AGV and the docking position is where the container is transferred to the gantry crane. The road types are constructed in a straight line and J-turn. When driving the straight line, the driving velocity is 6㎧ and the J-turn is 3㎧.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.82-87
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• 1993

A supervisory controller design technique for multiple-AGV systems is presented in this paper. The guidepath is represented in the form of a network, and its modifications are easily tested. The network has two-layered structure, where the path sets between each two nodes are made in advance using the K-shortest path algorithm. Occupation times for all links are stored in link-occupation table, and are updated after the dispatching time. Dispatching and scheduling for each AGV are optimized in terms of minimum-time objectives. In all times, the paths are guaranteed to be conflict-free and deadlock-free. The simplicity and flexibility on this control scheme make the supervisory suitable for real applications.