Earticle

위성 기반 측위 시스템은 위치 기반 기술의 핵심 기술로서, CDMA 시스템을 사용한다. CDMA 시스템에서 원활한 위치 측위를 하기 위해서는 확산 부호의 정확한 동기가 이루어져야 한다. 본 논문은 확산 부호의 동기 기법 가운데 부호 추적 기법에 초점을 맞춘다. 특히 다중 경로 환경에서 기존의 부호 추적 기법인 DLL 방법은 적합하지 않다. 본 논문은 수신 신호에서 다중 경로 신호를 축출하여 이를 부호 추적 기법에 사용하는 방법을 제안한다. Spirent simulator를 이용하여 제안 기법을 검증하고 성능 평가를 보인다.

The navigation system like GPS which is core technology is based on Code Division Multiple Access(CDMA) techniques. To receive satellite signal smoothly in CDMA, received signals have to synchronize with spread code. In this paper, we focus on the code tracking methods among synchronization techniques. The conventional delay lock loop(DLL) is unsuitable for multi-path channel. We will introduce how it overcomes distortion by multi-path. We will propose method that separates out multi-path signals and tracks the each path signals. And we will confirm performance of proposed method using Spirent simulator.

This study proposes a way to generate a virtual lane by using a vehicle’s driving tra-jectory during the operation of a V2V-based vehicle. The existing method of road shape detection using Lidar, Radar, and camera sensors has limitations in the range of detec-tion with blind spots. To overcome such limitations, a method to generate virtual lanes was proposed to detect the road shape along with the driving trajectory of the leading vehicle based on V2V communications. A Clothoid road model was applied for the calcu-lation of the driving trajectory of the leading vehicle. A driving scenario with various changes in curvature of road was also configured. Furthermore, the feasibility of the vir-tual lane generation method was verified by comparing the reference virtual lane and the virtual lane generated by the driving trajectory. The simulation results confirmed the us-ability of the virtual lane generated from Clothoid road model.

In this paper, the calibration path implementation of the Ku band tracking antenna system with about 2000 arrangements and the verification method of the calibration function during the near field test were described. The calibration path of the tracking antenna system is an internal loop consisting of an antenna element assembly, a TR assembly, an RF power combiner assembly, a driving amplifier module, and an RF cable. The gain and phase change for each channel for calibration are performed by the 4CH Beamformer IC inside the TR assembly. After extracting the align data and calibration data, during the near field test process, the change in the antenna system state was imitated by changing the RF cable inside the antenna system. The CLUT(Calibration Look Up Table) was calculated by re-extracting the antenna system calibration data after the state change and calculating the difference from the antenna system calibration data before the state change. Thereafter, by applying the alignment LUT(Look Up Table) loaded into the antenna system before the state change and the CLUT through the scenario of the RF test equipment, it was verified whether the antenna system maintains the alignment state, the calibration function, and the effectiveness of the calibration value when the state of the antenna system changes.

Object tracking is important and challenging task in many computer vision applications such as surveillance, vehicle navigation, and autonomous robot navigation. Video surveillance in a dynamic environment, especially for humans and vehicles, is one of the current challenging research topics in computer vision. It is a key technology to fight against terrorism, crime, public safety and for efficient management of traffic. In this paper, in order To get an accurate description of the trajectory points, regression analysis technique is used. This technique has the ability to summarize the collection of trajectory points by fitting it to mathematical models which will accurately describe these points and consequently describe object behavior. The regression analysis technique uses the least square method to obtain the best fit of equations for the given set of trajectory points. The least square method assumes that the best fit curve has the minimal sum of the deviations squared error from the given set of data. In this paper, propose new method to deal with the trajectory by converting the trajectory points into 3D approximation function using best fit plane after interpolation the time factor this method offers high flexibility as well as statistical tools for the analysis behavior of object. Planar regression calculates the best fit plane through a group of 3 or more data points. The plane is calculated by minimizing the residuals (or errors) between the plane and the original points using least squares minimization. The objective of this paper was to develop methods for optimization of least square best fit geometry for planes.

In Korea, Lunar exploration program has been prepared with the aim of launching in the 2020's. As a part of it, a lunar lander demonstrator was developed, which was the model for verifying the system such as structure, propulsion, and control system, before launching into the deep space. This paper deals with the path tracking performance of the lunar lander demonstrator with respect to the thruster controller based on Pulse Width Pulse Frequency Modulator (PWPFM) and Pulse Width Modulator (PWM). First, we derived equations of motion, considering the allocation of the thrusters, and designed the path tracking controller based on Euler angle. The signal generated from the path tracking controller is continuous, so PWPFM and PWM modulator are adopted for generating ON/OFF signal. Finally, MATLAB simulation is performed for evaluating the path tracking ability. We compared the path tracking performances of PWPFM and PWM based thrust controller, using performance measures such as the total impulse and the position error with respect to the desired path.

This paper addresses a path tracking problem with obstacle avoidance for Lagrange systems. The proposed method is based on field potential methods in combination with navigation functions for obstacle avoidance. First, it is shown that a simple combination of the navigation function with the conventional path tracking controller does not work. Therefore, in order to cope with this problem, a new feedback law is proposed for a path parameter which characterizes the reference path. It is proved that the proposed controller achieves both path following and collision avoidance. Moreover, since the method adopts bounded navigation functions, the proposed controllers generate bounded input signals even when target systems approach obstacles. Finally, an experimental evaluation is performed with a two-link manipulator to illustrate the effectiveness of the proposed method.

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.

In this paper, we present a wavelet neural network (WNN) approach to the solution of the path tracking problem for mobile robots that possess complexity, nonlinearity and noise. First, we discuss a WNN based control system where the control signals are directly obtained by minimizing the difference between the reference track and the pose of a mobile robot. This compact network structure is helpful to determine the number of hidden nodes and the initial value of weights. Then, the data with various noises provided by odometric and external sensors are here fused together by means of an Extended Kalman Filter (EKF) approach for the pose estimation problem of mobile robots. This control process is a dynamic on-line process that uses the wavelet neural network trained via the gradient-descent method with estimates from EKF. Finally, we verify the effectiveness and feasibility of the proposed control system through simulations.

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

In this paper, we propose a wavelet based fuzzy neural network(WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges advantages of neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of mobile robot using the gradient descent(GD) method. In addition, an approach that uses adaptive learning rates for the training of WFNN controller is driven via a Lyapunov stability analysis to guarantee the fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control performance of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

Autonomous Mobile Robot(AMR) is a field of study which is under active research along with rapid development of the engineering technology. The main reasons for the high interest in AMR are because of its ability to change work space freely and its capability to replace human being for difficult and dangerous jobs. Also the fact that AMR provides a variety of research fields, such as path planning, navigation algorithm, sensor fusion, image processing, and controller design is part of the reason for its popularity. But relatively few researches are concerned with controller. So in this paper, a control strategy of mobile robot with nonholonomic constraint for tracking ordered discontinuous motion is proposed. The proposed control strategy has been designed as a state feedback shape to allow the AMR to obtain continuous velocity and track the path which is composed of discontinuous motions. In order to design such controller, 3 states have been reduced to 2 states through coordinate projection. These ideas are tested for validity through simulation and simulation result is compared with experiments result.

In this paper, a novel feasible real-time system was researched for a differential driven wheeled autonomous mobile robot so that the mobile robot can move in a smooth, safe and elegant way. Least Square Minimum Path Planning was well used for the system to generate a smooth executable path for the mobile robot, and the point-to-point tracking algorithm was presented as well as its application in arbitrary path tracking. In order to make sure the robot can run elegantly and safely, trapezoidal speed was integrated into the point-to-point path tracking algorithm. The application to guest following for the autonomous mobile robot shows its wide application of the algorithm. The novel design was successfully proved to be feasible by our experiments on our mobile robot Interactive Robot Usher (IRU) in National University of Singapore.

In this paper, a steering control system for the path tracking of autonomous vehicles is described. The steering control system consists of a path tracker and primitive driver. The path tracker generates the desired steering angle by using the look-ahead distance, vehicle heading, and a lateral offset. A method for applying an autonomous vehicle to path tracking is an advanced pure pursuit method that can reduce cutting corners, which is a weakness of the pure pursuit method. The steering controller controls the steering actuator to follow the desired steering angle. A servo motor is installed to control the steering handle, and it can transmit the steering force using a belt and pulley. We designed a steering controller that is applied to a proportional integral differential controller. However, because of a dead band, the path tracking performance and stability of autonomous vehicles are reduced. To overcome the dead band, a dead band compensator was developed. As a result of the compensator, the path tracking performance and stability are improved.