This paper presents an unified chassis control with electronic stability control (ESC) and active front steering (AFS) under lateral force constraint on AFS. When generating the control yaw moment, an optimization problem is formulated in order to determine the tire forces, generated by ESC and AFS. With Karush-Kuhn-Tucker optimality condition, the optimum tire forces can be algebraically calculated. On low friction road, the lateral force in front wheels is easily saturation. When saturated, AFS cannot generate the required control yaw moment. To cope with this problem, new constraint on the lateral tire force is added into the original optimization problem. To check the effectiveness of the propose method, simulation is performed on the vehicle simulation package, CarSim.

This paper proposes an algorithm for precise detection of corner points on a coplanar checkerboard in order to perform stereo camera calibration using a single frame. Considering the conditions of automobile production lines where a stereo camera is attached to the windshield of a vehicle, this research focuses on a coplanar calibration methodology. To obtain the accurate values of the stereo camera parameters using the calibration methodology, precise localization of a large number of feature points on a calibration target image should be ensured. To realize this demand, the idea with respect to a checkerboard pattern design and the use of a Homography matrix are provided. The calibration result obtained by the proposed method is also verified by comparing the depth information from stereo matching and a laser scanner.

Implanted telemetry systems provide the ability to monitor different species of animals while they move within their cages. Species monitored include mice, rats, rabbits, dogs, pigs, primates, sheep, horses, cattle, and others. A miniature transmitter implanted in each animal measures one or more parameters. Parameters measured include arterial pressure, intra-pleural pressure, left ventricular pressure, intra-ocular pressure, bladder pressure, ECG, EMG, EEG, EOG, temperature, activity, and other parameters and transmits the data via radio frequency signals to a nearby receiver. Every conventional dedicated transmitter contains one or more sensors, cpu and battery. Due to the expected life of the battery, the measuring time is limited. To overcome these problems, electromagnetic inductive coupling based wireless power transmission technology using multiple transmit coils were proposed, with each coil having a different active area driven by the coil driver. In this research, a parallel resonance based coil driver and serial resonance based coil driver are proposed. From the experiments we see that the parallel coil driver shows better performance under a low impedance and multiple coils configuration. However, the serial coil driver is more efficient for high impedance transmit coils.

DOB (Disturbance Observer) is an useful control method for estimating the disturbance applied to dynamic systems. Disturbance observer can be used to implement a robust control system to generate a control input for rejecting the disturbance, and it can be also used to estimate the disturbance to obtain information. The system that uses disturbance estimation is investigated for high performance control such as automatic door systems, walking robot and electric power steering system in vehicles. In this paper, a novel disturbance observer which is called disturbance and current observer for estimating load torque in the motor system is proposed. The difference between the DOB for disturbance rejection and DCOB is mathematically verified. Current and angular velocity are required for estimating the load torque of the motor in DOB. However, the DCOB can estimate load torque and current without current sensor. DCOB is designed based on modeling of the motor system. Appropriate Q-filter is selected and the applicability of DCOB is verified by simulation. The estimated disturbance and current of the electric motor can be verified without current sensor, as experiments of the actual motor system.

This paper presents a camera calibration method in order to estimate the lane detection and inter-vehicle distance estimation system for an automotive safety driving system. In order to implement the lane detection and vision-based inter-vehicle distance estimation to the embedded navigations or black box systems, it is necessary to consider the computation time and algorithm complexity. The process of camera calibration estimates the horizon, the position of the car's hood and the lane width for extraction of region of interest (ROI) from input image sequences. The precision of the calibration method is very important to the lane detection and inter-vehicle distance estimation. The proposed calibration method consists of three main steps: 1) horizon area determination; 2) estimation of the car's hood area; and 3) estimation of initial lane width. Various experimental results show the effectiveness of the proposed method.

Accurate heading information is crucial for the navigation of intelligent vehicles. In outdoor environments, GPS is usually used for the navigation of vehicles. However, in GPS-denied environments such as dense building areas, tunnels, underground areas and indoor environments, non-GPS solutions are required. Yaw-rates from a single gyro sensor could be one of the solutions. In dealing with gyro sensors, the drift problem should be resolved. HDR (Heuristic Drift Reduction) can reduce the average heading error in straight line movement. However, it shows rather large errors in some moving environments, especially along curved lines. This paper presents a method called VDR (Vision-based Drift Reduction), a system which uses a low-cost vision sensor as compensation for HDR errors.

This paper proposes a novel three-dimensional mapping algorithm in Omni-Directional Vision SLAM based on a fisheye image and laser scanner data. The performance of SLAM has been improved by various estimation methods, sensors with multiple functions, or sensor fusion. Conventional 3D SLAM approaches which mainly employed RGB-D cameras to obtain depth information are not suitable for mobile robot applications because RGB-D camera system with multiple cameras have a greater size and slow processing time for the calculation of the depth information for omni-directional images. In this paper, we used a fisheye camera installed facing downwards and a two-dimensional laser scanner separate from the camera at a constant distance. We calculated fusion points from the plane coordinates of obstacles obtained by the information of the two-dimensional laser scanner and the outline of obstacles obtained by the omni-directional image sensor that can acquire surround view at the same time. The effectiveness of the proposed method is confirmed through comparison between maps obtained using the proposed algorithm and real maps.

This paper proposes a new strategy for a quad-rotor to track a moving object efficiently by using image processing and an extended Kalman filter. The goal of path planning for the quad-rotor is to design an optimal path from the start point to the destination point. To lengthen the freight time of the quad-rotor, an optimal path is required to reduce the energy consumption. To track a moving object, the mark signed on the moving object has been detected by a camera mounted first on the quad-rotor. The center coordinates of the mark and its area are calculated through the blob analysis which is one type of image processing. The mark coordinates are utilized to obtain information on the motion direction and the area of the mark is utilized to recognize whether the object moves backward or forward from the camera on the quad-rotor. In addition, an extended Kalman filter has been applied to predict the direction and speed of the dynamically moving object. Through these schemes, it is aimed that the quad-rotor can track the dynamic object efficiently in terms of flight distance and time. Through the two different route freights of the quad-rotor, the performance of the proposed system has been demonstrated.

The kinematics with the instantaneous motion and statics of a manipulator has generally been proven algebraically. The algebraic solutions give very simple and straightforward results but the solutions do not have any meaning in physics or geometry. Therefore it is not easy to extend the algebraic results to design or control a robotic manipulator efficiently. Recently, geometrical approach to define the instantaneous motion or static relation of a manipulator is popularly researched and the results have very strong advantages to have a physical insight in the solution. In this paper, the instantaneous motion and static relation of a planar manipulator are described by geometrical approach, specifically by an axis screw and a line screw. The mass center of a triangle with weight and a perpendicular distance between the two screws are useful geometric measures for geometric analysis. This study provides a geometric interpretation of the kinematics and statics of a planar manipulator, and the method can be applied to design or control procedure from the geometric information in the equations.

Stairs are the most popular obstacles in buildings and factories. To enlarge the application areas of a field robotic platform, stair-climbing is very important mission. One important reason why a stair-climbing is difficult is that stairs are various in sizes. To achieve autonomous climbing of various-sized stairs, dynamic modeling is essential. In this research, an inverse dynamic modeling is performed to enable an autonomous stair climbing. Stair-climbing robotic platform with flip locomotion, named FilpBot, is analyzed. The FlipBot platform has advantages of robust stair-climbing of various sizes with constant speed, but the autonomous operation is not yet capable. Based on external constraints and the postures of the robot, inverse dynamic models are derived. The models are switched by the constraints and postures to analyze the continuous motion during stair-climbing. The constraints are changed according to the stair size, therefore the analysis results are different each other. The results of the inverse dynamic modeling are going to be used in motor design and autonomous control of the robotic platform.

This paper presents an improved predictive control of an induction machine fed by a matrix converter using N-switching vectors as the control action during a complete sampling period of the controller. The conventional model predictive control scheme based matrix converter uses a single switching vector over the same period which introduces high torque ripple. The proposed switching scheme for a matrix converter based model predictive control of an induction machine drive selects the appropriate switching vectors for control of electromagnetic torque with small variations of the stator flux. The proposed method can reduce the ripple of the electrical variables by selecting the switching state as well as the method used in the space vector modulation techniques. Simulation results are presented to verify the effectiveness of the improved predictive control strategy for induction machine fed by a matrix converter.

In this paper, we described the simulation results of the phase offset performance of a clock in holdover mode which was normally operated in GPS Disciplined Oscillator (GPSDO). In the TIE model, we included the time error term caused by environmental temperature variation because one of the most important parameters of clock phase error is the frequency offset and drift caused by the variation of temperature. For the simulation, we employed Maximum Time Interval Error (MTIE) for the performance evaluation when the frequency offset and drift are estimated by using an Unbiased Finite Impulse Response (UFIR) filter with ladder algorithm. We assumed that the noise in the GPS measurement is white Gaussian with zero mean and 1 ns standard deviation, and temperature linearly varies with a slope of $1{^{\circ}C}$ per hour. From the simulation results, the followings were observed. First, with the estimation error of temperature of less than 3 % and the temperature compensation period of less than 900 seconds, the requirement of CDMA2000 phase synchronization under 10 us could be achieved for more than 40,000 seconds holdover time if we employ an OCXO (Oven Controlled Crystal Oscillator) clock. Second, in order to achieve the requirement of LTE-TDD under 1.5 us for more than 10,000 seconds holdover time, below 3 % estimation error and 500 seconds should be retained if a Rubidium clock is adopted.

This paper proposes a helicopter direction adjustment system using barycenter shift. Most conventional methods for direction adjustment of uniaxial helicopters rely on the angle of inclination of the main rotor. However, the inherent burden of the bearing of the main rotor and serious abrasion of the helicopter using the above methods may results in loss of balance. To decrease abrasion and enhance the barycenter stability, the proposed method was used to shift the barycenter of the helicopter instead of the main rotor for direction adjustment. We set a biaxial ARM on a uniaxial helicopter to adjust the direction of ARM pointing as well as to realize stable direction control when the helicopter loses its balance. The method may enhance the landing safety of helicopters in emergencies. Uniaxial helicopters can be controlled under any environment by adjusting the motor parameters of the ARM which is dependent on the center of mass using neural network. The experiment results show that the helicopter can return to the starting position quickly under the external disturbance.

This paper presents a modified residual-based EKF (Extended Kalman Filter) for performance improvement of indoor positioning using WiFi RSSI (Received Signal Strength Indicator) measurement. Radio signal strength in indoor environments may have irregular attenuation characteristics due to obstacles such as walls, furniture, etc. Therefore, the performance of the RSSI-based positioning with the conventional trilateration method or Kalman filter is insufficient to provide location-based accurate information services. In order to enhance the performance of indoor positioning, in this paper, error analysis of the distance calculated by using the WiFi RSSI measurement is performed based on the radio propagation model. Then, an IARM (Irregularly Attenuated RSSI Measurement) error is defined. Also, it shows that the IARM error is included in the residual of the positioning filter. The IARM error is always positive. So, it is presented that the IARM error can be estimated by taking the absolute value of the residual. Consequently, accurate positioning can be achieved based on the IEM (IARM Error Mitigated) EKF with the residual modified by using the estimated IARM error. The performance of the presented IEM EKF is verified experimentally.

Inertial Navigation Systems (INS) are widely used as the main navigation device for aircraft. To get the initial attitude, the INS requires the initial alignment before navigation starts. An aircraft also needs an engine test procedure that causes some vibrations before flight. An INS can't be aligned in a vibration environment so the initial alignment is performed before the aircraft engine test. Therefore, the initial alignment time of an INS has been a major factor in limiting an aircraft's takeoff response time. In this paper, we designed an initial alignment algorithm that can be executed even in disturbances such as aircraft run-up. We demonstrated verification of the algorithm that is embedded on the real INS and testing methods to evaluate the alignment of the INS. We also analyzed the test results of the proposed initial alignment algorithm that is performed during a real aircraft run-up.

In the testing of a software component-based robot system, generating test cases for the system is a time-consuming and difficult task that requires the combining of test data. This paper proposes an adaptive combinatorial testing method which is based on the input/output relationship among components and which automatically generates the test cases for the system. The proposed algorithm first generates an input/output relationship graph in order to analyze the input/output relationship of the system. It then generates the reduced set of test cases according to the analyzed type of input/output relationship. To validate the proposed algorithm some comparisons are given in terms of the time complexity and the number of test cases.