• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.878-879
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• 2023

본 프로젝트에서는 BLDC 인휠모터를 사용하여 조향각에 제약이 없는 다자유도 기동 모빌리티 플랫폼을 구현하고 로봇 모듈화로 정비 편의성과 범용성을 높인 자율 모빌리티 플랫폼의 개발로 사용편의성 뿐만 아니라 산업 전반에서 발생하는 안전사고 리스크 관리에 도움이 되고자 한다.

• Journal of Drive and Control
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• v.12 no.3
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• pp.36-43
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• 2015

An autonomous vehicle control algorithm based on Ackerman Geometry is known to be reliable in low tire slip situation. However, vehicles at high speed make lateral errors due to high tire slip. In this paper, considering the tire slip of vehicles, the steering angle is determined based on the Ackerman Geometry and is supplemented tire slip angle by the Stanley steering algorithm. In addition, to prevent the tire slip, the algorithm, which restricts steering if a certain level of slip occurs, is used to reduce the lateral error. While many studies have been extended to include vehicle slip, studies also need to be carried out on the tire slip depending on hardware performance. The control algorithm of autonomous vehicles is compensated considering the sensor noise and the performance of steering actuator. Through the various simulations, it was found that the performance of steering actuator was the key factor affecting the performance of autonomous driving. Also, it was verified that the usefulness of steering algorithm considering the tire slip and performance of steering actuator.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.240-257
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• 2022

Autonomous vehicles use various local sensors such as camera, radar, and lidar to perceive the surrounding environment. However, it is difficult to predict the movement of vulnerable road users using only local sensors that are subject to limits in cognitive range. This is true especially when these users are blocked from view by obstacles. Hence, this paper developed an algorithm for collision avoidance with VRU using V2X information. The main purpose of this collision avoidance system is to overcome the limitations of the local sensors. The algorithm first evaluates the risk of collision, based on the current driving condition and the V2X information of the VRU. Subsequently, the algorithm takes one of four evasive actions; steering, braking, steering after braking, and braking after steering. A simulation was performed under various conditions. The results of the simulation confirmed that the algorithm could significantly improve the performance of the collision avoidance system while securing vehicle stability during evasive maneuvers.

• Korean Journal of Optics and Photonics
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• v.33 no.1
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• pp.11-21
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• 2022

We have investigated the optimal design of an aperiodic optical phased array (OPA) for use in light detection and ranging applications. Three optimization algorithms - particle-swarm optimization (PSO), a genetic algorithm (GA), and a pattern-search algorithm (PSA) - were employed to obtain the optimal arrangement of optical antennas comprising an OPA. The optimization was performed to obtain the minimal side-lobe level (SLL) of an aperiodic OPA at each steering angle, using the three optimization algorithms. It was found that PSO and GA exhibited similar results for the SLL of the optimized OPA, while the SLL obtained by PSA showed somewhat different features from those obtained by PSO and GA. For an OPA optimized at a steering angle <45°, the SLL value averaged over all steering angles increased as the angle of optimization decreased. However, when the angle of optimization was larger than 45°, low average SLL values of <13 dB were obtained for all three optimization algorithms. This implies that an OPA with high signal quality can be obtained when the arrangement of the optical antennas is optimized at a large steering angle.

• Journal of Biosystems Engineering
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• v.20 no.3
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• pp.226-235
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• 1995

An unmanned speed sprayer was developed using a remote control and an inductive cable guidance systems to protect operators and environment from hazardous pesticides. The sprayer consists of a remote control system, an induction system, obstacle detectors, control actuators and an one-chip microcomputer. The sprayer can be operated by the induction guidance and/or remote control. The following summarize characteristics of the developed speed sprayer. 1) Both the remote control and the induction guidance operation were possible with the developed speed sprayer. 2) Sixteen functions of the forwarding, backing, halting, steering, 3-way valve for nozzles and fan operating etc. were utilized on the remote control system. 3) It was concluded that the DTMF method, having less transmitting error, performed better than the FSK method for an agricultural remote controller. A radio station may be necessary. 4) The digital inductive guidance system, consisting of five low-impedance detection coils and a window comparator circuit, performed better than the analog detecting system, guiding route using inductive voltage differential from tow detection coils.

• Journal of Sensor Science and Technology
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• v.12 no.5
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• pp.211-217
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• 2003

This paper is proposed that roadway recognition performance improvement for autonomous vehicle using magnetic markers that are embedded along the road center and the sensors mounted on a vehicle, and which changing of magnetic field that is measured along with vehicle driving. For Retrenchment of equipment cost, interval of markers is more expensive than existing method. In order to this, This paper is proposed that interval of markers is founded using magnetic field analysis, and which arrangement method of six magnetic sensors and control method of neural network. This paper is carried out magnetic field analysis, the acquiring of the training patterns, the training of the neural network and composition of steering control, and is verified that roadway recognition performance can improve using computer simulation with proposed methods.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.4
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• pp.11-18
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• 2021

We propose a method that combines learning in a virtual environment and a real environment for indoor autonomous driving through reinforcement learning. In case of learning only in the real environment, it takes about 80 hours, but in case of learning in both the real and virtual environments, it takes 40 hours. There is an advantage in that it is possible to obtain optimized parameters through various experiments through fast learning while learning in a virtual environment and a real environment in parallel. After configuring a virtual environment using indoor hallway images, prior learning was carried out on the desktop, and learning in the real environment was conducted by connecting various sensors based on Jetson Xavier. In addition, in order to solve the accuracy problem according to the repeated texture of the indoor corridor environment, it was possible to determine the corridor wall object and increase the accuracy by learning the feature point detection that emphasizes the lower line of the corridor wall. As the learning progresses, the experimental vehicle drives based on the center of the corridor in an indoor corridor environment and moves through an average of 70 steering commands.

• Korean Journal of Agricultural Science
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• v.37 no.1
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• pp.123-130
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• 2010

A steering control system based on CAN(Controller Area Network) for autonomous tractor was developed to reduce duty of a central processing computer and to improve performance of steering control in terms of reduced control interval and error. The steering control system consisted of a SCU (Steering Control Unit), an EHPS system, and a potentiometer. The SCU consisted of an MCU (Micro Controller unit), an A/D converter, and a DC-DC converter, and a PID controller was used to control steering angle. The steering control system was communicated with the computer by CAN-bus. Each actuator and implement was connected to a multi-function board interfacing with the computer through a USB cable. Without CAN, control interval of the autonomous tractor was 1.5 seconds. When the CAN-based steering control system was combined with the autonomous tractor, however, control interval of the integrated system was reduced to those less than 0.05 seconds. When the autonomous tractor was operated with 1.5-s and 0.05-s control cycles at a 0.63-m/s travelling speed, the trajectories were close to straight lines for both of the control cycles. For a 1.34-m/s traveling speed, tractor trajectory was close to sine wave with a 1.5-s control cycle, but was straight line with a 0.05-s control cycle.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2365-2368
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• 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 Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.199-209
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• 2015

This paper presents a model predictive control (MPC) approach to control the steering angle in an autonomous vehicle. In designing a highly automated driving control algorithm, one of the research issues is to cope with probable risky situations for enhancement of safety. While human drivers maneuver the vehicle, they determine the appropriate steering angle and acceleration based on the predictable trajectories of surrounding vehicles. Likewise, it is required that the automated driving control algorithm should determine the desired steering angle and acceleration with the consideration of not only the current states of surrounding vehicles but also their predictable behaviors. Then, in order to guarantee safety to the possible change of traffic situation surrounding the subject vehicle during a finite time-horizon, we define a safe driving envelope with the consideration of probable risky behaviors among the predicted probable behaviors of surrounding vehicles over a finite prediction horizon. For the control of the vehicle while satisfying the safe driving envelope and system constraints over a finite prediction horizon, a MPC approach is used in this research. At each time step, MPC based controller computes the desired steering angle to keep the subject vehicle in the safe driving envelope over a finite prediction horizon. Simulation and experimental tests show the effectiveness of the proposed algorithm.