• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.81-90
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• 2016

As an autonomous driving vehicle (AV) need to cope with external road conditions by itself, its perception performance for road environment should be better than that of a human driver. A vision sensor, one of AV sensors, performs lane detection function to percept road environment for performing safe vehicle steering, which relates to define vehicle heading and lane departure prevention. Performance standards for a vision sensor in an ADAS(Advanced Driver Assistance System) focus on the function of 'driver assistance', not on the perception of 'independent situation'. So the performance requirements for a vision sensor in AV may different from those in an ADAS. In assuming that an AV keep previous steering due to lane detection failure, this study calculated lane departure distances between the AV location following curved road alignment and the other one driving to the straight in a curved section. We analysed lane departure distance and time with respect to the allowance of lane detection malfunction of an AV vision sensor. With the results, we found that an AV would encounter a critical lane departure situation if a vision sensor loses lane detection over 1 second. Therefore, it is concluded that the performance standards for an AV should contain more severe lane departure situations than those of an ADAS.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.2
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• pp.132-137
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• 2014

In this paper, An LQR controller is proposed for way-point tracking of AUV (Autonomous Underwater Vehicle). The LQR controller aims at tracking a series of way-points which operator registers arbitrarily in advance. It consists of a depth controller and a steering controller and AUV's surge speed is assumed varying to consider the dynamic environment of the underwater. In order to show the performance, a conventional state feedback controller is compared with the proposed controller by the simulation using Matlab/Simulink. The parameters of AUV developed by the author's laboratory are used. In the simulation, we verify that the LQR controller can track all the way-points within 1 m error range under the varying surge speed, which proves the robustness of the LQR controller.

• Journal of Biosystems Engineering
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• v.21 no.4
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• pp.422-428
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• 1996

This study was conducted to developed to develop a simple battery-powered autonomous vehicle for greenhouse works. A steering method using speed difference of two independent driving motors was adopted. DC motor driving circuit, speed control circuit and controller using one-chip microcomputer were constructed. The inputs of controller are rolling of the vehicle and current speed of driving motors. Using these signals, automatic guidance system along furrow was developed. A computer simulation program by the kenematic analysis was developed to find out optimal control algorithm. The results of this study are as follows. 1. Automatic guidance system along the furrow that adopted two independent driving motors and rolling of vehicle was developed. 2. The results of simulation showed that PID control was adequate to automatic guidance system along furrow. 3. Two commercial 12V battery serially connected were able to drive the vehicle on the soil ground for five hours in continuous operation and for four hours in intermittent operation without recharging the battery. 4. The speed range was 0-0.7m/s and the rolling of vehicle could be controlled within $pm5^{\circ}$ range. 5. From a series of tests, developed vehicle was found to be a useful tool for greenhouse works.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2000.04a
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• pp.56-63
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• 2000

Abstract - In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied for the autonomous guided vehicle(AGV) driving. 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 adjusted almost randomly. To solve this problem, a neural network is used to model the plant and the parameter tuning of the model is performed by the immune algorithm. 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 immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough intially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. The computer simulation for the control of steering and speed of AGV is performed. The results show that the proposed controller has better performances than other conventional controllers.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2000.11a
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• pp.207-217
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• 2000

In this paper, we describe an image processing algorithm which is able to recognize the road lane. This algorithm performs to recognize the interrelation between AGV and the other vehicle. We experimented on AGV driving test with color CCD camera which is setup on the top of vehicle and acquires the digital signal. This paper is composed of two parts. One is image preprocessing part to measure the condition of the lane and vehicle. This finds the information of lines using RGB ratio cutting algorithm, the edge detection and Hough transform. The other obtains the situation of other vehicles using the image processing and viewport. At 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, if vehicle knows the driving conditions which are angle, distance error and real position of other vehicles, we should calculate the reference steering angle.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.340-347
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• 1999

Recently, the progress of industrialization has been taken concern of material handling automation. So far, the conveyor belt has been popular for material handling. However, this system has many disadvantages such as the space, cost, In this paper, a new navigation algorithm using fuzzy is introduced. The mobile robot follows a line installed on the roads. These informations are inputted with three approximate sensors. These obtained informations are analyzed with fuzzy technique for autonomous steering. Therefore, unlike existing systems, high reliability is guaranteed under bad environment conditions. The installation and maintenance of a line is easily made at lower cost. The use of the mobile robot can be extended to materials handing purposes in manufacturing, hospital, inter-of fire documents deliveries.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.179-186
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• 2015

In this paper, a novel lateral control system is proposed for the purpose of improving lane keeping performance which is independent from GPS signals. Lane keeping is a key function for the realization of unmanned driving systems. In order to obtain this objective, a vision sensor based real-time lane detection scheme is developed. Furthermore, we employ a data fusion along with a real-time steering angle of the test vehicle to improve its lane keeping performance. The fused direction data can be obtained by an IMU sensor and vision sensor. The performance of the proposed system was verified by computer simulations along with field tests using MOHAVE, a commercial vehicle from Kia Motors of Korea.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.6
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• pp.74-80
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• 2009

Path tracking of unmanned vehicle is a basis of autonomous driving and navigation. For the path tracking, it is very important to find the exact position of a vehicle. GPS is used to get the position of vehicle and a direction sensor and a velocity sensor is used to compensate the position error of GPS. To detect path lines in a road image, the bird's eye view transform is employed, which makes it easy to design a lateral control algorithm simply than from the perspective view of image. Because the driving speed of vehicle should be decreased at a curved lane and crossroads, so we suggest the speed control algorithm used GPS and image data. The control algorithm is simulated and experimented from the basis of expert driver's knowledge data. In the experiments, the results show that bird's eye view transform are good for the steering control and a speed control algorithm also shows a stability in real driving.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.12
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• pp.1217-1224
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• 2009

A simple and effective guidance and control scheme that enables autonomous three-dimensional path-following for a fixed wing aircraft is presented. The method utilizes the nonlinear path-following guidance law for the outer loop that creates steering acceleration command based on the desired flight path and the current position and velocity of the vehicle. The scheme considers the gravity in the guidance level, where it is subtracted from the acceleration command to form the specific force acceleration command which the aircraft is better suited to follow than the total acceleration command in the inner-loop. A roll attitude control scheme is also presented that enables inverted flight or sideslip maneuvers such as slow roll and knife-edge. A series of aerobatic maneuvers are demonstrated through simulations to show the potential of the proposed scheme.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.4
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• pp.201-212
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• 2000

In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied for the autonomous guided vehicle(AGV) driving. When the immune algorithm is applied to the PID controller, there exists the cast that the plant is damaged due to the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network is used to model the plant and the parameter tuning of the model is performed by the immune algorithm. 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 immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough intially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. The computer simulation for the control of steering and speed of AGV is performed. The results show that the proposed controller has better performances than other conventional controllers.