• Journal of Sensor Science and Technology
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• v.21 no.3
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• pp.198-203
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• 2012

In this paper, the driving information system of the bicycle has been studied by using the 3-axis acceleration sensor. The sensor module composed of 3-axis acceleration sensor and MCU(Microcontroller Unit) was mounted onto the handle of bicycle and the experiments were conducted on the flatland, uphill and downhill of bicycle road. Three axis acceleration values were converted to the pitch and roll angles, then four major compensation methods have been applied to achieve meaningful data for driving information system. The experimental results of pitch angles showed 2.46, -1.26, 7.79 degrees in case of flatland, uphill, downhill, respectively. When the steering handle turned to the left direction, roll angles showed -29.35, -41.67, -36.98 degrees at each road condition. With the right-turn, roll angles presented 20.05, 33.75, 24.44 degrees in case of flatland, uphill, and downhill, respectively. The pitch angle has been increased more than 40 degrees at stop mode. By using the change of pitch and roll angles, we could obtain the driving information system of bicycle successfully.

• Transactions of the Korean Society of Automotive Engineers
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• v.5 no.5
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• pp.20-26
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• 1997

A System for reducing vehicle body roll by active control is developed. The stabilizer bar with hydraulic rotary actuator produces anti-roll moment which suppresses roll tendency. This reduction of roll improves the driving safety as well as the ride comfort. Vehicle test data shows considerable reduction of roll angle during steady-state turning. Also improvement of ride comfort is achieved by making the actuator freely rotatable, i.e. by connecting all chambers of actuator in normal driving conditions. A control algorithm using steering wheel angle and vehicle speed signal as input valve is applied. It is compared with signal of the G-sensor.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.9
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• pp.526-532
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• 2003

This paper describes a recognition method of a road direction for an autonomous vehicle based on magnetic sensors. Using the sensors mounted on a vehicle and the magnetic markers embedded along the center of road, the autonomous vehicle can recognize a road direction and control a steering angle. Using the front lateral deviation of a vehicle and the rear one, the road direction is calculated. The analysis of magnetic field, the acquisition technique of training data, the training method of neural network and the computer simulation are presented. According to the computer simulation, the proposed method is simulated, and its performance is verified. Also, the experimental test is confirmed its reliability.

• The Journal of the Acoustical Society of Korea
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• v.30 no.3
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• pp.129-135
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• 2011

An algorithm is presented for estimating the 3-D location (i.e., azimuth angle, elevation angle, and range) of multiple sources with a uniform circular array (UCA) consisting of an even number of sensors. Recently the rank reduction (RARE) algorithm for partly-calibrated sensor arrays was developed. This algorithm is applicable to sensor arrays consisting of several identically oriented and calibrated linear subarrays. Assuming that a UCA consists of M sensors, it can be divided into M/2 identical linear subarrays composed of two facing sensors. Based on the structure of the subarrays, the steering vectors are decomposed into two parts: range-independent 2-D direction-of-arrival (DOA) parameters, and range-relevant 3-D location parameters. Using this property we can estimate range-independent 2-D DOAs by using the RARE algorithm. Once the 2-D DOAs are available, range estimation can be obtained for each source by defining the 1-D MUSIC spectrum. Despite its low computational complexity, the proposed algorithm can provide an estimation performance almost comparable to that of the 3-D MUSIC benchmark estimator.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.59 no.2
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• pp.145-154
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• 2023

In this study, to control the heading angle of a ship, which is constantly subjected to various internal and external disturbances during the voyage, an LADRC (linear active disturbance rejection control) design that focuses more on improving the disturbance removal performance was proposed. The speed rate of change of the ship's heading angle due to the turn of the rudder angle was selected as a significant factor, and the nonlinear model of the ship's maneuvering equation, including the steering gear, was treated as a total disturbance. It is the similar process with an LADRC design for the first-order transfer function model. At this time, the gains of the controller included in LADRC and the gains of the extended state observer were tuned to RCGAs (real-coded genetic algorithms) to minimize the integral time-weighted absolute error as an evaluation function. The simulation was performed by applying the proposed GA-LADRC controller to the heading angle control of the Mariner class vessel. In particular, it was confirmed that the proposed controller satisfactorily maintains and follows the set course even when the disturbances such as nonlinearity, modelling error, uncertainty and noise of the measurement sensor are considered.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1507-1510
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• 2012

본 논문은 EPS(전자식 파워 스티어링)에 사용되는 Torque and Angle sensor 의 절대각 신뢰성 보증 방식에 관한 것으로, Inductive 방식의 센서에서 절대각 신호 신뢰성 보증에 대한 효과적인 방법을 제시한다. 전동식 파워 스티어링 시스템의 ECU(전자제어유닛)는 조타각도를 출력하는 2 개의 소자에서 버니어 알고리즘을 통해 360 도 이상의 멀티 조타각을 인식하게 된다. 토크&조향각 센서는 절대 조향각을 계산하는 1 개의 Hall IC 소자 신호와, 상대 조향각을 계산하는 ASIC 소자 신호를 사용하여 멀티 조타각을 인식한다. 인식된 조타각이 추가적인 검증 절차 없이 제어에 사용된다면, 센서의 이상 발생 시에 운전자의 조타감을 불편해질 수 있고, 나아가서는 차량사고의 위험을 발생시킬 수 있다.따라서 차량 거동 시 절대 조향각의 신뢰성 검증은 제어와 동시에 항상 요구되어야 한다. 특히, 유럽/미국 업계의 ISO 26262 표준 도입에 따라 절대 조향각의 높은 신뢰성이 요구된다. 본 논문에서는 이러한 요구사항을 만족하기 위해 측정된 절대각 신호를 기준으로 상대각 신호 2 개를 측정에 사용하고, Driving 시에도 절대각 기준의 신뢰도 향상을 위해 절대각 신호 1 개를 비교기로 사용한다. 최적화된 에러 기준을 근거로 절대 조향각 신호의 신뢰성을 보장하는 방법을 제안한다. 이러한 방법을 적용한다면, 정확하고 안정적으로 조타각을 결정함으로써 EPS 안전성 확보에 도움을 줄 수 있다.

• 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.

• The Journal of Korea Robotics Society
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• v.3 no.3
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• pp.186-193
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• 2008

We propose a novel real-time obstacle avoidance method for rescue robots. This method, named the ELA(Emergency Level Around), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward safe position. In the ELA, we consider two sensor modules, PSD(Position Sensitive Detector) infrared sensors taking charge of obstacle detection in short distance and LMS(Laser Measurement System) in long distance respectively. Hence if a robot recognizes an obstacle ahead by PSD infrared sensors first, and judges impossibility to overcome the obstacle based on driving mode decision process, the order of priority is transferred to LMS which collects data of radial distance centered on the robot to avoid the confronted obstacle. After gathering radial information, the ELA algorithm estimates emergency level around a robot and generates a polar histogram based on the emergency level to judge where the optimal free space is. Finally, steering angle is determined to guarantee rotation to randomly direction as well as robot width for safe avoidance. Simulation results from wandering in closed local area which includes various obstacles and different conditions demonstrate the power of the ELA.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.2
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• pp.155-163
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• 2000

This paper presents a path planning method of the sensor based intelligent vehicle using fuzzy logic controller for avoidance of moving obstacles in unknown environments. Generally it is too difficult and complicated to control intelligent vehicle properly by recognizing unknown terrain with sensors because the great amount of imprecise and ambiguous information has to be considered. In this respect a fuzzy logic can manage such the enormous information in a quite efficient manner. Furthermore it is necessary to use the relative velocity to consider the mobility of obstacles, In order to avoid moving obstacles we must deliberate not only vehicle's relative speed toward obstacles but also self-determined acceleration and steering for the satisfaction of avoidance efficiency. In this study all the primary factors mentioned before are used as the input elements of fuzzy controllers and output signals to control velocity and steering angle of the vehicle. The main purpose of this study is to develop fuzzy controllers for avoiding collision with moving obstacles when they approach the vehicle travelling with straight line and for returning to original trajectory. The ability are and effectiveness of the proposed algorithm are demonstrated by simulations and experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.5
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• pp.550-557
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• 1999

This paper describes a road-following controller using the proposed neural network for autonomous vehicle. Road-following with visual sensor like camera requires intelligent control algorithm because analysis of relation from road image to steering control is complex. The proposed neural network, relative similarity modular network(RSMN), is composed of some learning networks and a partitioniing network. The partitioning network divides input space into multiple sections by similarity of input data. Because divided section has simlar input patterns, RSMN can learn nonlinear relation such as road-following with visual control easily. Visual control uses two criteria on road image from camera; one is position of vanishing point of road, the other is slope of vanishing line of road. The controller using neural network has input of two criteria and output of steering angle. To confirm performance of the proposed neural network controller, a software is developed to simulate vehicle dynamics, camera image generation, visual control, and road-following. Also, prototype autonomous electric vehicle is developed, and usefulness of the controller is verified by physical driving test.