This article presents an integrated chassis control with electronic stability control (ESC) and active front steering (AFS) under saturation of front lateral tire force. Regardless of the use of AFS, the front lateral tire forces can be easily saturated. Under the saturated front lateral tire force, AFS cannot be effective to generate a control yaw moment needed for the integrated chassis control. In this paper, new integrated chassis control is proposed in order to limit the use of AFS in case the front lateral tire force is saturated. Weighed pseudo-inverse control allocation (WPCA) with variable weight is adopted to adaptively use the AFS. To check the effectiveness of the proposed scheme, simulation is performed on a vehicle simulation package, CarSim. From simulation, the proposed integrated chassis control is effective for vehicle stability control under saturated front lateral tire force.

A sensorless motor control scheme with conventional back-Electro Motive Force (EMF) sensing based on zero crossing point (ZCP) detection has been widely used in various applications. However, there are several problems with the conventional method for effectively driving sensorless brushless motors. For example, a phase mismatch of 30 degrees occurs between the ZCP and commutation time. Additionally, most of the motor speed/current controls are achieved based on a pulse width modulation (PWM) method, which generates significant noise that distracts the back-EMF sensing. Due to the PWM switching, the ZCP is not deterministic, and thus the efficiency of the motor is reduced because the phase transition points become uncertain. Moreover, the motor driving performance is degraded at a low speed range due to the effect of PWM noise. To solve these problems, an improved back-EMF detection method based on a differential line method is proposed in this paper. In addition, the proposed sensorless BLDC driver addresses the problems by using a variable voltage driver generated from a buck converter. The variable voltage driver does not generate the PWM switching noise. Consequently, the proposed sensorless motor driver improves 1) the signal-to-noise ratio of back-EMF, 2) the operation range of a BLDC motor, and 3) the torque characteristics. The proposed sensorless motor driver is verified through simulations and experiments.

In this paper, the TSE (Triangulation based target Size Estimator) algorithm is presented to estimate static target size at IIR (Imaging Infrared) environment. The size information is important factor for accurate IIR target tracking. But the IIR sensor can't generate distance between missile and target to calculate target size. In order to overcome the problem, we propose TSE algorithm which based on triangulation measurement. The performance of proposed method is tested at target intercept scenario. The experiment results show that the proposed algorithm has suitable performance for the accurate static target size estimating.

In this paper, we suggest a phase difference algorithm inspired by weakly electric fish. Weakly electric fish is a fish which generates electric field though its electric organ in the tail. The weakly electric fish search for prey and detect an object by using electrolocation. The weakly electric fish have Jamming Avoidance Response (JAR) to avoid jamming signal. One of pulse-type weakly electric fish Gymnotus carapo also have JAR to reduce the probability of coincidence of pulses. We analyze this response signal and design the phase difference algorithm. We expect that simple algorithm inspired by weakly electric fish can be used in many engineering fields.

An unmanned forklift requires precise positioning and pallet detection. Therefore, conventional unmanned forklifts use high-cost sensors to find the exact position of the pallet. In this study, a docking algorithm with two cameras is proposed. The proposed method uses vision data to extract the angle difference between the pallet and the forklift. Then the control law is derived from the extracted angle for successful docking. The extracted angle is compared with the actual angle in the real environment. The control law is tested with the Lyapunov stability test and Routh-Hurwitz stability criterion. Through various experiments, the proposed docking algorithm showed the success rate high enough for real-life applications.

This paper presents arc/line segments-based Simultaneous Localization and Mapping (SLAM) by updating accumulated laser sensor data with a mobile robot moving in an unknown environment. For each scan, the sensor data in the set are stored by a small constant number of parameters that can recover the necessary information contained in the raw data of the group. The arc and line segments are then extracted according to different limit values, but based on the same parameters. If two segments, whether they are homogenous features or not, from two scans are matched successfully, the new segment is extracted from the union set with combined data information obtained by means of summing the equivalent parameters of these two sets, not combining the features directly. The covariance matrixes of the segments are also updated and calculated synchronously employing the same parameters. The experiment results obtained in an irregular indoor environment show the good performance of the proposed method.

Recent studies on ankle-foot prostheses used for transtibial amputees have focused on the adaptation of the ankle angle of the prosthesis according to ground conditions. For adaptation to various ground conditions (e.g., incline, decline, and step conditions), ankle-foot prostheses should first recognize the ground conditions as well as the current human motion pattern. For this purpose, the ground reaction forces and orientation angle of the tibia provide fundamental information. The measurement of the orientation angle, however, creates a challenge in practice. Although various sensors, such as accelerometers and gyroscopes, can be utilized to measure the orientation angles of the prosthesis, none of these sensors can be solely used due to their intrinsic drawbacks. In this paper, a time-varying complementary filtering (TVCF) method is proposed to incorporate the measurements from an accelerometer and a gyroscope to obtain a precise orientation angle. The cut-off frequency of TVCF is adaptively determined according to the human gait phase detected by a fuzzy logic algorithm. The performance of the proposed method is verified through experiments.

This Paper considers the heading and depth control problem for an underactuated AUV (Autonomous Underwater Vehicle) HW200. The HW200 is a torpedo-type AUV that is developed from Hanwha corporation R&D Center for military operation such as MCM (Mine Counter Measures). The HW200 controls horizontal and vertical motion with two stern plane and two rudder plane. It is well known that fine control of an AUV motion is not easy because of model uncertainties, highly nonlinear and coupled motions. To overcome those kind of uncertainties, a number of control methods have been presented. In this paper, the motion controllers of the HW200 are designed using PD controller design method based on the linear and perturbed model of the typical 6-DOF equations of an AUV, and confirmed the effectiveness of the controller through simulations and field test.

In this paper, an object recognition method based on the depth information from the RGB-D camera, Xtion, is proposed for an indoor mobile robot. First, the RANdom SAmple Consensus (RANSAC) algorithm is applied to the point cloud obtained from the RGB-D camera to detect and remove the floor points. Next, the removed point cloud is classified by the k-means clustering method as each object's point cloud, and the normal vector of each point is obtained by using the k-d tree search. The obtained normal vectors are classified by the trained multi-layer perceptron as 18 classes and used as features for object recognition. To distinguish an object from another object, the similarity between them is measured by using Levenshtein distance. To verify the effectiveness and feasibility of the proposed object recognition method, the experiments are carried out with several similar boxes.

This paper suggests a feature point-based Iterative Closest Point (ICP) algorithm to compensate for the disparity error in building a two-dimensional map. The ICP algorithm is a typical algorithm for matching a common object in two different images. In the process of building a two-dimensional map using the laser scanner data, warping and distortions exist in the map because of the disparity between the two sensor values. The ICP algorithm has been utilized to reduce the disparity error in matching the scanned line data. For this matching process in the conventional ICP algorithm, pre-known reference data are required. Since the proposed algorithm extracts characteristic points from laser-scanned data, reference data are not required for the matching. The laser scanner starts from the right side of the mobile robot and ends at the left side, which causes disparity in the scanned line data. By finding the matching points between two consecutive frame images, the motion vector of the mobile robot can be obtained. Therefore, the disparity error can be minimized by compensating for the motion vector caused by the mobile robot motion. The validity of the proposed algorithm has been verified by comparing the proposed algorithm in terms of map-building accuracy to conventional ICP algorithm real experiments.

The objective of this research is to verify the quantitative efficiency of a bimanual robotic task. Bimanual robots can realize dexterous and complicated motions using two cooperating arms. However, its motion planning and control method are not simple for implementing flexible tasks such as assembly. In this paper, the proposed motion planning method is used to find an optimal solution satisfying a designed cost function and constraints with regard to the kinematics and redundancy of the bimanual robot. The simulation results show that the lifetime of the manipulator can be changed by the proposed cost function consisting of angular velocity and angular acceleration of each joint in the same assembly task.

We propose a method for the implementation of an EtherCAT communication bus in a Linux-based open source Computerized Numerical Control (CNC) system. Recently, increasingly more CNC systems support real-time Ethernet protocols such as EtherCAT, which is a high-performance industrial communication protocol. For real-time CNC control over an Ethernet-based protocol, an additional layer driver needs to be implemented between the CNC system and the master of industrial communication protocol. Among the various solutions for the connection layer driver, we employ a Hardware Abstraction Layer (HAL) driver based on Linux. The operation of the implemented CNC system is demonstrated and confirmed by Hal Meter, which is used to observe the pins, signals, or parameters of HAL.

The inspection machine in SMT (Surface Mount Technology) line detects the assembly defects such as missing, misalignment, loosing, or tombstone. We propose a new method to classify the defect types of chip components by processing the image of PCB. Two original images are obtained from horizontal lighting and vertical lighting. The image of the component is divided into two soldering regions and one packaging region. The features are extracted by appling the PCA (Principle Component Analysis) to each region. The MLP (Multilayer Perceptron) and SVM (Support Vector Machine) are then used to classify the defect types by learning. The experimental results are presented to show the usefulness of the proposed method.