• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.539-542
• /
• 2005

This paper contains studies which are Cruise speed control which is made by PID algorithm and automated steering system for avoiding the obstacle coming from the front which is using Fuzzy algorithm. This mobile car uses DC motor whose speed is detected by encoder. Ultrasonic Waves Sensor established in the front detects the obstacle and the curve. And the sensor established in the side detects the distance of the space of the road. If the sensor detects the obstacle or the curve, the car is controlled by using Fuzzy algorithm. The Fuzzy algorithm calculates the speed and steering angle by using the value which is obtained from sensor.

• Journal of Navigation and Port Research
• /
• v.47 no.6
• /
• pp.386-393
• /
• 2023

Recently, the demand for autonomous navigation technology has increased, and related research is also increasing. Autonomous ships generally follow the planned route, calculate the avoidance route according to the risk situation while sailing, and follow a calculated route. In general, an automatic steering device is used to follow the route, and among the operational automatic steering device methods, the route control mode is the most appropriate method to apply to autonomous ships. Therefore, in this study, we developed a route-tracking algorithm to apply an avoidance route using the navigation control mode of an automatic steering device. The algorithm was developed by dividing the straight and turning sections. A performance test was conducted to satisfy the performance suggested by IEC 62065, the relevant international standard, using simulator equipment that had acquired international certification to verify its performance. The results of the performance verification confirmed that the cross-track error, which represents the straight distance between the ship and the route, satisfied the performance standards suggested by IEC 62065 when the ship followed the route.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.1
• /
• pp.27-32
• /
• 2002

This paper describes a fuzzy steering controller for an outdoor autonomous mobile robot using MR(magneto-resistive) sensor. Using the magnetic field difference values(dBy, dBz) obtained from the MR sensor, we designed fuzzy logic controller for driving the robot on the road center and proposed a method to eliminate the Earth magnetic field. To develop an autonomous mobile robot simulation program, we have done modeling MR sensor, mobile robot and coordinate transformation. A computer simulation of the robot including mobile robot dynamics and steering was used to verify the driving performance of the mobile robot controller using the fuzzy logic. So, we confirmed the robustness of the proposed fuzzy controller by computer simulation.

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.2365-2368
• /
• 2001

This paper describes a fuzzy steering controller for an autonomous mobile robot with MR sensor. Using the magnetic field($B_{x}$, $B_{y}$, $B_{z}$) obtained from the MR sensor, we designed fuzzy controller for driving on the road center. Fuzzy rule base was built to magnetic field($B_{x}$, $B_{y}$, $B_{z}$). To develop an autonomous mobile robot simulation program, we have done modeling MR sensor, dynamic model of mobile robot and coordinate transformation. A computer simulation of the robot (including mobile robot dynamics and steering) was used to verify the steering performance of the mobile robot controller using the fuzzy logic. Good results were obtained by computer simulation. So, we confirmed the robustness of the proposed fuzzy controller by computer simulation. Also, we know that proposed control algorithm was applied to real autonomous mobile robot.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.7
• /
• pp.834-841
• /
• 2020

Among the autonomous driving systems based on visual sensors, the control method using a vanishing point is the most general method for autonomous driving. However, if the lane is lost or does not exist, it is very difficult to detect this and estimate the vanishing point. In this paper, we predict the vanishing point of the road and the vanishing point lines on the left and right sides using CNN for the camera image and design the steering controller for autonomous driving from the predicted results. As a result of the simulation, it was confirmed that the proposed method well tracked the center of the road regardless of the presence or absence of a solid lane, and was superior to the control method using a general method using the vanishing point.

• Proceedings of the KSME Conference
• /
• 2007.05a
• /
• pp.861-866
• /
• 2007

This paper presents development of a vehicle lateral and longitudinal control for autonomous driving control and test results obtained using an electric vehicle. Sliding control theory has been used to develop a vehicle speed and distance control algorithm. The longitudinal control algorithm that maintains safety and comfort of the vehicle consists of a cruise and STOP&GO control depending on traffic conditions. Desired steering angle is determined through the lateral position error and the yaw angle error based on preview optimal control. Motor control inputs have been directly derived from the sliding control law. The performance of the autonomous driving control which is integrated with a lateral and longitudinal control is investigated by computer simulations and driving test using an electric vehicle. Electric vehicle system consists of DC driving motor, an electric power steering system, main controller (Autobox)

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.7
• /
• pp.1302-1308
• /
• 2007

In this paper, a real-time collision avoidance algorithm is proposed for experimental Autonomous Vehicle(XAV). To ensure real-time implementation, a virtual potential field is calculated in one dimensional space. The attractive force is generated by the steering command either transmitted in the remote control station or calculated in the Autonomous Navigation System(ANS) of the XAV. The repulsive force is generated by obstacle information obtained from Laser Range Finder(LRF) mounted on the XAV. Using these attractive and repulsive forces, modified steering, velocity and emergency stop commands are created to avoid obstacles and follow a planned path. The suggested algorithm is inserted as one component in the XAV system. Through various real experiments and technical demonstration using the XAV, the usefulness and practicality of the proposed algorithm are verified.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.134.6-134
• /
• 2001

The purpose of navigation algorithm is to reach a given target point without collision with obstacles while an autonomous mobile robot is navigating. To achieve a safe navigation, this paper presents an effective navigation algorithm for the autonomous mobile robot equipped with ultrasonic sensors in unknown environments. The proposed navigation algorithm consists of an obstacle-avoidance behavior, a target-reaching behavior and a fuzzy-based decision maker. In the obstacle-avoidance behavior and the target-reaching behavior, artificial immune networks are used to select a proper steering angle, make the autonomous mobile robot avoid obstacles and approach a given target point. The decision maker using fuzzy inference systems weights the steering angles selected ...

• Journal of Biosystems Engineering
• /
• v.25 no.1
• /
• pp.55-62
• /
• 2000

Autonomous farm operation needs to be developed for safety, labor shortage problem, health etc. In this research, an autonomous tractor for tillage was investigated using machine vision and a fuzzy logic controller(FLC). Tractor heading and offset were determined by image processing and a geomagnetic sensor. The FLC took the tractor heading and offset as inputs and generated the steering angle for tractor guidance as output. A color CCD camera was used fro the image processing . The heading and offset were obtained using Hough transform of the G-value color images. 15 fuzzy rules were used for inferencing the tractor steering angle. The tractor was tested in the file and it was proved that the tillage operation could be done autonomously within 20 cm deviation with the machine vision and the FLC.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.6
• /
• pp.1259-1268
• /
• 2023

This study proposes a throttle and steering control technology using visual sensors based on deep learning's convolutional and recurrent neural networks. It collects camera image and control value data while driving a training track in clockwise and counterclockwise directions, and generates a model to predict throttle and steering through data sampling and preprocessing for efficient learning. Afterward, the model was validated on a test track in a different environment that was not used for training to find the optimal model and compare it with a CNN (Convolutional Neural Network). As a result, we found that the proposed deep learning model has excellent performance.