A position control problem is studied for UEV(Unmanned Expedition Vehicles), which is to follow pre-determined paths via fixed way-points. Hybrid control systems are used for position control of UEV depending on the operating condition. Speed control consists of three controllers: PID control, adaptive PI control, and neural network. Heading control consists of two controllers, PID and adaptive PID control. The controllers are selected based on the changes of road conditions. We suggest an adaptive PI control algorithm for speed control and an transition management algorithm among the controllers. The algorithm adapts the road conditions and variation of vehicle dynamical characteristics and selects a suitable controller.

In this paper Haar functions are developed to approximate the solutions of continuous time linear system. Properties of Haar functions are first presented, and an explicit expression for the inverse of the Haar operational matrix is presented. Using the inverse of the Haar operational matrix the full order Stein equation should be solved in terms of the solutions of pure algebraic matrix equations, which reduces the computation time remarkably. Finally a numerical example is illustrated to demonstrate the validity of the proposed algorithm.

This paper presents the fault detection and diagnosis(FDD) algorithm to enhance reliability of a longitudinal controller for an autonomous All-Terrain Vehicle(ATV). The FDD is designed to monitor and identify faults which may occur in distributed hardware used for longitudinal control, e.g., DSPs, CAN, sensors, and actuators. The proposed FDD is an integrated approach of decentralized and centralized FDD. While the former is processed in a DSP and suitable to detect faults in a single hardware, it is sensitive to noise and disturbance. On the other hand, the latter is performed via communication and it detects and diagnoses faults through analyzing concurrent performances of multiple hardware modules, but it is limited to isolate faults specifically in terms of components in the single hardware. To compensate for disadvantages of each FDD approach, two layered structure including both decentralized and centralized FDD is proposed and it allows us to make more robust fault detection and more specific fault isolation. The effectiveness of the proposed method will be validated experimentally.

A new RMRAC scheme far the PMSM current regulation is proposed in a synchronous frame, which is completely free from the parameter's uncertainty. A current regulator of PMSM is the inner most loop of electromechanical driving systems and plays a foundation role in the control hierarchy. When the PMSM runs in high speed, the cross-coupling terms must be compensated precisely for large system BW. In the proposed RMRAC, the input signal is composed of a calculated voltage defined by MRAC law and an output of the disturbance compensator. The gains of feed forward and feedback controller are estimated by the proposed modified gradient method, where the system disturbances are assumed as filtered current regulation errors. After the compensation of the system disturbance from error information, the corresponding voltage is fed forward to control input to compensate for real disturbances. The proposed method robustly compensates the system disturbance and cross-coupling terms. It also shows a good realtime performance due to the simplicity of control structure. Through real experiments, the efficiency of the proposed method is verified.

In this paper, a visual servoing control technique of humanoid robot arms is implemented for tracking a moving object. An embedded time-delayed controller is designed on an FPGA(Programmable field gate array) chip and implemented to control humanoid robot arms. The position of the moving object is detected by a stereo vision camera and converted to joint commands through the inverse kinematics. Then the robot arm performs visual servoing control to track a moving object in real time fashion. Experimental studies are conducted and results demonstrate the feasibility of the visual feedback control method for a moving object tracking task by the humanoid robot arms called the ROBOKER.

This paper describes a new sensor system for 3D range measurement using the structured infrared light. Environment and obstacle sensing is the key issue for mobile robot localization and navigation. Laser scanners and infrared scanners cover $180^{\circ}$ and are accurate but too expensive. Those sensors use rotating light beams so that the range measurements are constrained on a plane. 3D measurements are much more useful in many ways for obstacle detection, map building and localization. Stereo vision is very common way of getting the depth information of 3D environment. However, it requires that the correspondence should be clearly identified and it also heavily depends on the light condition of the environment. Instead of using stereo camera, monocular camera and the projected infrared light are used in order to reduce the effects of the ambient light while getting 3D depth map. Modeling of the projected light pattern enabled precise estimation of the range. Identification of the cells from the pattern is the key issue in the proposed method. Several methods of correctly identifying the cells are discussed and verified with experiments.

This paper proposes an intelligent sliding mode control method for robotic systems with the unknown bound of model uncertainties. In our control structure, the unknown bound of model uncertainties is used as the gain of the sliding controller. Then, we employ the function approximation technique to estimate the unknown nonlinear function including the width of boundary layer and the uncertainty bound of robotic systems. The adaptation laws for all parameters of the self-recurrent wavelet neural network and those for the reconstruction error compensator are derived from the Lyapunov stability theorem, which are used for an on-line control of robotic systems with model uncertainties and external disturbances. Accordingly, the proposed method can not only overcome the chattering phenomenon in the control effort but also have the robustness regardless of model uncertainties and external disturbances. Finally, simulation results for the five-link biped robot are included to illustrate the effectiveness of the proposed method.

This article presents an omni-tread snake robot that designed to locomote on narrow space and rough terrain. The omni-tread snake robot comprises three segment, which are linked to each other by 2 degrees of freedom joints for the pitch and yaw motion. Moving tracks on all four sides of each segment guarantee propulsion even when the robot rolls over. The 2 DOF joint are actuated by 2 servo motors which produce sufficient torque to lift the one leading or trailing segments up and overcome obstacles. This paper applies articulated steering technique to get omni-tread snake robot's kinematics model.

When a humanoid robot pushes an object with its force, it is essential to adequately control its posture so as to maximize the surplus torque far all joints. For such purpose, this study proposes a method to find an optimal posture of a humanoid robot using a genetic algorithm in such a way that the surplus torque for all joints is maximized. In this study, pushing motion of an 18-DOF humanoid robot is considered. When the robot takes a cooperative motion to push an object, the palms and soles are assumed to be fixed at the object and ground respectively, and are subjected to sense the reaction force from the object and the ground. Then, the torques for all joints are calculated and reflected to fitness function of the genetic algorithm. To verify the effectiveness of the proposed method, a number of simulations with different fitness functions are carried out. The simulation result shows that the proposed method can be adopted to find optimized posture in cooperative motion of a humanoid robot.

This paper presents a framework to generate human-like movements of a humanoid in real time using the movement primitive database of a human. The framework consists of two processes: 1) the offline motion imitation learning based on an Evolutionary Algorithm and 2) the online motion generation of a humanoid using the database updated bγ the motion imitation teaming. For the offline process, the initial database contains the kinetic characteristics of a human, since it is full of human's captured motions. The database then develops through the proposed framework of motion teaming based on an Evolutionary Algorithm, having the kinetic characteristics of a humanoid in aspect of minimal torque or joint jerk. The humanoid generates human-like movements far a given purpose in real time by linearly interpolating the primitive motions in the developed database. The movement of catching a ball was examined in simulation.

In this paper, we design an image-based robust controller to compensate uncertainties with image Jacobian and robot dynamics due to uncertain depth measurement and load variations. The proposed controller with eye-in-hand structure has separate terms to compensate each of uncertainties. The ultimate boundedness of the closed-loop system is proved by the Lyapunov approach. The performance of the proposed control system is demonstrated by simulation and experimental results a 5-link robot manipulator with two degree of freedom.

In this paper, a moving target tracking using a binocular vision for an omni-directional mobile robot is addressed. In the binocular vision, three dimensional information on the target is extracted by vision processes including calibration, image correspondence, and 3D reconstruction. The robot controller is constituted with SPI(serial peripheral interface) to communicate effectively between robot master controller and wheel controllers.

When mobile robots perform the mission in the rough terrain, the traversability depended on the terrain characteristic is useful information. In the traversabilities, wheel-terrain maximum friction coefficient can indicate the index to control wheel-terrain traction force or whether mobile robots to go or not. This paper proposes estimating wheel-terrain maximum friction coefficient. The existing method to estimate the maximum friction coefficient is limited in flat terrain or relatively easy driving knowing wheel absolute velocity. But this algorithm is applicable in rough terrain where a lot of slip occurred not knowing wheel absolute velocity. This algorithm applies the tire-friction model to each wheel to express the behavior of wheel friction and classifies slip-friction characteristic into 3 major cases. In each case, the specific algorithm to estimate the maximum friction coefficient is applied. To test the proposed algorithm's feasibility, test bed(ROBHAZ-6WHEEL) simulations are performed. And then the experiment to estimate the maximum friction coefficient of the test bed is performed. To compare the estimated value with the real, we measure the real maximum friction coefficient. As a result of the experiment, the proposed algorithm has high accuracy in estimating the maximum friction coefficient.

A GPS receiver platform has been developed to design an interference rejection filter and the platform is also able to evaluate performance of those filters. This platform consists of RF/IF part, data acquisition part and PC part. The RF/IF part converts RF signals to IF signals, the data acquisition part transmits the IF signals to PC using USB device. The PC part rejects the interferences with a filter and then it does navigation with GPS software receiver. The RF/IF part and data acquisition part had been validated with signal spectrum, and the PC part had been validated with the navigation results of GPS receiver. Finally, the entire platform including interference rejection filter has been confirmed with the navigation results in case that the GPS signals and interference entered this platform. As a result, the GPS receiver operated well against interference with 45dB JSR.

This paper describes limitations and solutions of the existing processor-monitoring concept for a military supersonics aircraft Flight Control Computer (FLCC) equipped with modern architecture processor to perform the system integration test. Safecritical FLCC integration test, which requires automatic test for thousands of test cases and real-time input/output test condition generation, depends on the processor-monitoring device called Processor Interface (PI). The PI, which relies upon on the FLCC processor's external address and data-bus data, has some limitations due to multi-fetching capability of the modern sophisticated military processors, like C6000's VLIW (Very-Long Instruction Word) architecture and PowerPC's Superscalar architecture. Several techniques for limitations were developed and proper monitoring approach was presented for modem processor-adopted FLCC system integration test.