• Transactions of the Korean Society of Automotive Engineers
• /
• v.17 no.4
• /
• pp.86-92
• /
• 2009

A driver-vehicle model means the integrated dynamic model that is able to estimate the steering wheel angle from the driver's desired path based on the dynamic characteristics of the driver and vehicle. Autonomous driving robot for factory automation has individual four-wheels which are driven by electronic motors. In this paper, the dynamic characteristics of several four-wheel steering systems with the simultaneously steerable front and rear wheels are investigated and compared by means of the driver-vehicle model. A diver-vehicle model is proposed by using the PID control to velocity and trajectory of control autonomous driving robot. To determine the optimum speed of a autonomous driving robot, steady-state circle simulation is carried out with the ADAMS program and MATLAB control model.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.15 no.4
• /
• pp.68-80
• /
• 2016

This study proposes the traffic prediction and optimal traffic control system based on cell transmission model and genetic algorithm in cloud environment. The proposed prediction and control system consists of four parts. 1) Data preprocessing module detects and imputes the corrupted data and missing data points. 2) Data-driven traffic prediction module predicts the future traffic state using Multi-level K-Nearest Neighbor (MK-NN) Algorithm with stored historical data in SQL database. 3) Online traffic simulation module simulates the future traffic state in various situations including accident, road work, and extreme weather condition with predicted traffic data by MK-NN. 4) Optimal road control module produces the control strategy for large road network with cell transmission model and genetic algorithm. The results show that proposed system can effectively reduce the Vehicle Hours Traveled upto 60%.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.196-196
• /
• 2000

This paper describes a lateral guidance system of an autonomous vehicle, using a neural network model of magneto-resistive sensor and magnetic fields. The model equation was compared with experimental sensing data. We found that the experimental result has a negligible difference from the modeling equation result. We verified that the modeling equation can be used in simulations. As the neural network controller acquires magnetic field values(B$\_$x/, B$\_$y/, B$\_$z/) from the three-axis, the controller outputs a steering angle. The controller uses the back-propagation algorithms of neural network. The learning pattern acquisition was obtained using computer simulation, which is more exact than human driving. The simulation program was developed in order to verify the acquisition of the teaming pattern, teaming itself, and the adequacy of the design controller. The performance of the controller can be verified through simulation. The real autonomous electric vehicle using neural network controller verified good results.

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

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.18 no.8
• /
• pp.2062-2072
• /
• 1994

This paper presents the development of a fuzzy controller for driving camera pan/tilt device so that the camera's viewing direction can automatically track a moving object. To achieve computational efficiency a non-contact type displacement follower is used as a feedback sensor instead of a vision camera. The displacement follower, however, is extremely sensitive to object's lighting condition and results in unstable response at high speed. To this end, a fuzzy controller is developed in such a way to provide stable tracking performance at high speed where the sensory signal is subjected to intermittant disturbances of large magnitude. The test result shows stable tracking response even for high speed and non-uniform lighting condition. The resulting camera autotracking system can be adopted as an effective tool for visual transfer in the context of teleoperation and autonomous robotics.

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

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

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38C no.1
• /
• pp.43-56
• /
• 2013

Personal Rapid Transit(PRT), which is one of the next generation convergence transport technology, PRT system requires operation technology for controlling diverse vehicles and dealing with a variety of abnormal driving situations on a large scale trackway structures in expected operational area more efficiently and reliably. Before developing PRT control technology, it is essential that multiple testing procedures stepwise with building small scale test-tracks and develop real unmanned-vehicles. However, it is expected that the experiments demand huge amount of time and physical cost. Thus, simulation in virtual environment is efficient to develop wireless based control technology for multiple PRT vehicles prior to building real-test environment. In this paper, we propose a VR-based integrated simulator which physics engine is applied so that it enables simulation of front-wheel-steering PRT system rather than simple rail track system. The proposed simulator is also developed that it can reflect geographical features, infrastructures and network topology of expected driving region.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.3
• /
• pp.199-209
• /
• 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.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.8
• /
• pp.1611-1618
• /
• 2017

Recently, the development for USV-related technologies are actively growing up in military domain. The USV (unmanned surface vehicle) conducts various missions for national defense at maritime environment. For succeed the missions, the USV essentially needs an automatic and remote control platform which includes propulsion system, steering system, control system, power system and so on. In this paper, we developed an integrated control system for the platform equipments and verified effectiveness of the developed system. To do this, we designed a system architecture and implemented a main control system that processes and controls platform equipments by received command. Also we developed components of designed architecture such as engine control device, water-jet control device and power control device. For test and verify the developed system, we designed and made a test-bed of engine and water-jet with related parts, and proceeded a basin test for verifying the developed system based on the test-bed.