Applied Mechanics and Materials Vols. 764-765

Abstract: The estimation performance of interactive multiple model (IMM) estimator for tracking a maneuvering target is influenced by the target motion models and application of filters. An improved IMM estimation algorithm combined with the intelligent input estimation technique is proposed in this study. The target motion models include the constant velocity (CV) model and the modified Singer acceleration model. The intelligent fuzzy weighted input estimation (IFWIE) is used to compute the acceleration input for the modified Singer acceleration model besides the application of standard Kalman filter (KF). The combination of KF and IFWIE can estimate the target motion state precisely and the proposed method is compared with the common IMM estimator. The simulation results prove the improved IMM estimator has superior estimation performance than the common IMM estimator, especially when the target changes the acceleration violently. The utilization of IFWIE for the improved IMM estimator can estimate the acceleration input effectively.

Abstract: In this study presents a hybrid numerical scheme comprising the differential transformation method and the finite difference approximation technique to analyze the nonlinear dynamic response of the micro circular plate. The analysis takes account of the axial residual stress and hydrostatic pressure acting on micro circular plate upper surface. The effects of the residual stress, initial gap height, and thickness on the pull-in voltage of the micro circular plate are systematically explored. Overall, the proposed method provides an accurate and versatile means of analyzing the complex nonlinear behavior of the electrostatically-actuated microstructures.

Abstract: Enabling FIRA medium-sized soccer robots to recognize target objects according to color information requires that competing teams manually set the range of colors according to ambient lighting conditions prior to games. This color information is used to differentiate features of target objects, such as the ball, the goals, and the field. Constructing a color-feature model such as this is extremely time-consuming and the resulting model is unable to adapt dynamically to changes in lighting conditions. This study applied a look-up table method to execute RGB-HSV color space conversion to accelerate video processing. A particle swarm optimization (PSO) scheme was developed to detect the color-feature parameters of the target objects in the HSV color space. This enables the automatic completion of color-feature modeling and the construction of the knowledge model required by the robot for object recognition. Experiment results demonstrate that the proposed method is capable of enhancing the robustness of the robot vision system in determining changes in lighting conditions. In addition, the manpower and time required to set robot parameters prior to games were reduced significantly.

Abstract: The purpose of this paper is to develop an intelligent mobile robot using image processing technology. The mobile robot is composed of a visual tracking system, a loading platform, a balance control system, a PC-based controller, four ultrasonic sensors and a power system. We develop a PC based control system for image processing and path planning. The mobile robot can track a moving target and adjust the loading platform by the balance control system simultaneously. The Image processing based on OpenCV use two different tracking methods, MTLT (Match Template Learning Tracking) and TLD (Tracking, Learning and Detection), to track moving targets. The efficiencies of both methods for tracking the moving target on the mobile robot are compared in this paper. The loading platform control system uses HOLTEK Semiconductor Company's HT66F Series 8-bit microprocessor as the processor, and receives the feedback data from the FAS-A inclinometer sensor. The controller of the loading platform uses the PID control law according to the feedback signals of the inclinometer sensor, and controls the rotation speed of the platform motor to tune the balance level. Keywords— Intelligent mobile robot, Image processing, OpenCV, MTLT, TLD, HOLTEK, FAS-A inclinometer sensor, PID control.

Abstract: Based on the model of electric direct drive motor, the development of a steering loading system is presented, and the effect of the rotation speed and system rigidity on the surplus torque of a certain steering loading system is analyzed. Compromising system rigidity is needed both to buffer the surplus torque caused by target motion and to maintain system bandwidth and responsibility.

Abstract: A fault detection and diagnosis method for the hydraulic servo system based on adaptive threshold and self-organizing map (SOM) neural network is proposed in this study. The nonlinear, time-varying, fluid-solid coupling properties of the hydraulic servo system are considered. Fault detection is realized based on a two-stage radial basis function (RBF) neural network model. The first-stage RBF neural network is adopted as a fault observer for the hydraulic servo system; the residual error signal is generated by comparing the estimated observer output with the actual measurements. To overcome the drawback of false alarms when the traditional fixed fault threshold is used, an adaptive threshold producer is established by the second-stage RBF neural network. Fault occurrence is detected by comparing the residual error signal with the adaptive threshold. When a system fault is detected, the SOM neural network is employed to implement fault classification and isolation by analyzing the features of the residual error signal. Three types of common faults are simulated to verify the performance and effectiveness of the proposed method. Experimental results demonstrate that the proposed method based on adaptive threshold and SOM neural network is effective in detecting and isolating the failure of the hydraulic servo system.

Abstract: The autonomous stratagem combines the concepts of traveling salesman problems (TSP), radio frequency identification (RFID), E-compass wireless networks sensors and the evolutional particle swarm optimization (PSO) to identify the optimal routing paths in the dynamic indoor space. A desired the setting lot as soon as possible. Four active RFID tags amounted in the rectangle type environments and indication of E-compass are proposed to identify the robot position. The routing concept of the TSP is developed by the evolutionary PSO learning scheme to approach the valid routing-path of the mobile robot. The developed mobile robot system, with the proper number of fuzzy membership functions, simultaneously achieves the multiple objectives of avoiding obstacles by the selected shortest routing path.

Abstract: A hydraulic servo system is a typical feedback control system. Health assessment of a hydraulic servo system is usually difficult to realize when traditional methods based on sensor signals are utilized. An approach for health assessment of hydraulic servo systems based on multi-fractal analysis and Gaussian mixture model (GMM) is proposed in this study. A GRNN neural network is employed to establish a fault observer for the hydraulic servo system. The observer is utilized to simulate the system output under normal state. The residue is then generated by subtracting the estimated output from the actual output. The residue’s feature is extracted by fractal analysis. After the feature extraction, the overlap between the current feature vectors and the normal feature vectors is obtained by applying GMM. The confidence value (CV) can be obtained in advance; this value is employed to characterize the health degree of the current state and consequently implement the health assessment of the hydraulic servo system. Lastly, two common types of fault, namely, burst and gradual, are applied to validate the effectiveness of the proposed method.

Abstract: With the rapid development of unmanned aerial vehicles (UAV), the applications of the UAV are more and more extensively, and one of the important applications is aerial search and rescue. Using the UAV for search and rescue can save cost and avoid the risk of personal injury. Nowadays, the UAV usually use analog 1.2GHz, 2.4GHz or 5.8GHz real-time video transmission system. Because of the limited transmission bandwidth, the maximum resolution is only 640*480, which is not enough for aerial search. It is difficult to search and identify the small target by this aerial image with only 640*480 resolutions. Because the mobile devices and 3G base stations are widespread, this research integrated the UAV with a mobile device to perform time lapse photography App. The high resolution (2688*1520 pixels) photo can be transmitted to the cloud server via 3G network. The GCS (ground control station) can receive these high resolution photos from cloud servers. With these high resolution aerial photos, the rescued targets can be found and identified more easily. Using the EXIF (Exchangeable image file format) information of the aerial photo, the GPS coordinate of the target can be acquired.

Abstract: An Unmanned Aerial vehicle (UAV), commonly known as drone, is an aircraft without a human pilot aboard. In recent years, some UAVs are deployed for civil applications, such as policing, firefighting, air pollution monitoring, and aerial search and rescue, etc. Unmanned Aerial vehicle generally include fixed-wing and multi-rotor aircrafts. This research had developed a high endurance quadcopter for search and rescue mission. Target position correction software also was developed in this research. This program uses GPS coordinates embedded in the EXIF information of the aerial photos and more accurately calculates the target’s position. The target’s position error is less than ten meters in 75 meters altitude. Keywords: target position correction software, search and rescue, UAV, quadcopter, and GPS.