• 한국항공우주학회지
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• 제38권2호
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• pp.180-187
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• 2010

본 논문에서는 위성항법 기반의 위치 정보만을 이용하여 저속으로 운행하는 이동체의 제어 성능을 확인하기 위해서 골프장에 무인으로 운행하는 AGV(Autonomous Guided Vehicle)를 위성항법 기반의 AGV로 구성하여 조향 시험을 수행하였다. 이를 위해 기존 AGV 시스템의 구성에 대한 분석을 수행한 후 위성항법 기반의 위치 정보를 이용하여 조향 제어가 가능하도록 제어기 및 조향 제어 알고리즘을 개발하였다. AGV의 조향 성능을 알기 위해서 직선과 원형으로 이루어진 기준궤적을 생성하여 시험을 수행하였으며 시험 결과 기준궤적에서 ${\pm}0.2m$ 범위 안으로 조향 제어가 가능함을 확인하였다.

• 제어로봇시스템학회논문지
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• 제21권2호
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• pp.95-101
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• 2015

In this paper, we develop a sliding mode control for steering wheel angle control based on torque overlay in order to resolve the problem of previous methods for Electric Power Steering (EPS) systems in the Lane Keeping System (LKS) of autonomous vehicles. For the controller design, we propose a 2nd order model of the electric power steering system in an autonomous LKS. The desired state model is designed to prevent a rapid change of the steering wheel angle. The sliding mode steering wheel angle controller is developed for the robustness of the disturbance. Since the proposed method is designed based on torque overlay, torque integration with basic functions of the EPS system for the steering wheel angle control is available for the driver's convenience. The performance of the proposed method was validated via experiments.

• 센서학회지
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• 제10권5호
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• pp.329-336
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• 2001

This paper describes the steering control and geomagnetism cancellation for an autonomous vehicle using an MR sensor. The magneto-resistive (MR) sensor obtains the vector summation of the magnetic fields from embedded magnets and the Earth. The vehicle is controlled by the magnetic fields from embedded magnets. So, geomagnetism is the disturbance in the steering control system. In this paper, we propose a new method of the sensor arrangement in order to remove the geomagnetism and vehicle body interference. The proposed method uses two MR sensors located in a level plane and the steering controller has been developed. The controller has three input variables ($dB_x$, $dB_y$, $dB_z$) using the measured magnetic field difference, and an output variable (the steering angle). A simulation program was developed to acquire the data to teach the neural network, in order to test the ability of a neural network to learn the steering control process. Also, the computer simulation of the vehicle (including vehicle dynamics and steering) was used to verify the steering performance of the vehicle controller using the neural network. From the simulation and field test, good result was obtained and we confirmed the robustness of the neural network controller in a real autonomous vehicle.

• 자동차안전학회지
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• 제14권4호
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• pp.84-90
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• 2022

With the popularization of autonomous driving technology, safety has emerged as a more important criterion. However, there are no assessment protocol or methods for AES (Autonomous Emergency Steering). So, this study proposes AES assessment protocol and scenario corresponding to collision avoidance Car-to-Car scenario of Euro NCAP in order to prepare for obstacles that appear after the emergency steering of LV (Leading Vehicle) avoiding obstacles in front of. Autoware-based autonomous driving stack is developed to test and simulate scenario in CARLA. Using developed stack, it is confirmed that obstacle avoidance is successfully performed in CARLA, and the AES performance of VUT (Vehicle Under Test) is evaluated by applying the proposed assessment protocol and scenario.

• 한국기계가공학회지
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• 제20권9호
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• pp.70-75
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• 2021

We developed a platform that was capable of autonomous steering in a furrow environment. It was developed to autonomously control steering by recognizing the furrow using a laser distance, three-axis tilt, and temperature sensor. The performance evaluation indicated that the autonomous steering success rate was 99.17%, and it was possible to climb up to 5° on the slope. The usage time was approximately 40 h, and the maximum speed was 6.7 km/h.

• 드라이브 ㆍ 컨트롤
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• 제19권4호
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• pp.110-116
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• 2022

Recently, various studies have been conducted to apply deep learning and AI to various fields of autonomous driving, such as recognition, sensor processing, decision-making, and control. This paper proposes a controller applicable to path following, static obstacle avoidance, and pedestrian avoidance situations by utilizing reinforcement learning in autonomous vehicles. For repetitive driving simulation, a reinforcement learning environment was constructed using virtual environments. After learning path following scenarios, we compared control performance with Pure-Pursuit controllers and Stanley controllers, which are widely used due to their good performance and simplicity. Based on the test case of the KNCAP test and assessment protocol, autonomous emergency steering scenarios and autonomous emergency braking scenarios were created and used for learning. Experimental results from zero collisions demonstrated that the reinforcement learning controller was successful in the stationary obstacle avoidance scenario and pedestrian collision scenario under a given condition.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(5)
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• pp.127-130
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• 2001

In this paper, we describes a method of estimating traveling direction of a autonomous vehicle. For the development of autonomous vehicle, it is important to detect road lane and to reckon traveling direction. The object of a propose algorithm is to perform lane detection in real-time for standalone vision system. And we calculate efficent traveling direction to find steering angie for lateral control system. Therefore autonomous vehicle go forward the center of lane by adjusting the current steering angle using traveling direction.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.274-281
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• 1998

This paper describes a visual control of autonomous vehicle using neural network. Visual control for road-following of autonomous vehicle is based on road image from camera. Road points on image are inputs of controller and vehicle speed and steering angle are outputs of controller using neural network. Simulation study confirmed the visual control of road-following using neural network. For experimental test, autonomous electric vehicle is designed and driving test is realized

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2002년도 춘계학술대회 논문집
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• pp.608-613
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• 2002

In this paper we developed the MPC sensor for steering control and steering control of the AGVDS(Autonomous Guided Vehicle Driving System) for Durability test. Among durability tests, the accelerated durability test has been widely used to evaluate the durability of vehicle structure and chassis parts in a short period of time on the designed road that has severe surface conditions. However it increased the drivers fatigue mainly caused by the severe driving conditions. The driver's difficulty to maintain the constant speed and control the steering wheel reduces the reliability of test results. In addition to the general detecting sensor for steering control was restricted by surrounding condition. So we need to develop steering control sensor was robust in the bad driving condition. In this paper we developed steering control sensor using magnetic induction which is robust in the bad driving condition and implemented the AGVDS.

• 제어로봇시스템학회논문지
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• 제13권5호
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• pp.406-413
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• 2007

This paper proposes a self-recurrent wavelet neural network(SRWNN) based adaptive backstepping control technique for the robust steering control of autonomous underwater vehicles(AUVs) with unknown model uncertainties and external disturbance. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the steering model of AUV. The adaptation laws for the weights of SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for the on-line control of AUV. Finally, simulation results for steering control of an AUV with unknown model uncertainties and external disturbance are included to illustrate the effectiveness of the proposed method.