2-DOF robot modelling by SimMechanics and PD-FL integrated controller for position control and trajectory tracking

Background Due to the high demand of robots to perform several industrial tasks, such as welding, machining, pick and place, position control in robotics has attracted high attention recently. Controllers’ improvement is also continuous specifically in terms of design simplicity and performance accuracy. This research plans to obtain the SimMechanics model of a two-degree of freedom (DOF) robot and to propose an integrated controller of a proportional–derivative (PD) controller and a fuzzy logic (FL) controller. Methodology The SimMechanics model of the 2-DOF robot is obtained using MATLAB SimMechanics toolbox from a CAD assembly design of the 2-DOF robot. Then, the proposed PD-FL integrated controller is designed and simulated in MATLAB Simulink. The PD controller is widely used for its simplicity, but it doesn’t have a satisfactory performance in difficult tasks. Furthermore, the FL controller is also easy for design and implementation even by non-experts in control theory, but it has the disadvantage of long computational time for multi-input systems due to the increased fuzzy rules. Results The FL controller is integrated with the PD controller for enhanced performance of the 2-DOF robot. The PD-FL integrated controller is developed and tested to control the 2-DOF robot for point-to-point position control and also tip trajectory tracking (TTT) such as triangular TTT and rhombic TTT. Conclusion The PD-FL integrated controller demonstrates enhanced performance compared to the conventional PD controller in both point-to-point position control and TTT. Furthermore, the PD-FL integrated controller has the advantage of less fuzzy rules which helps to overcome the computational time issue of the FL controller.

Design of RLC Circuit Parameter and Fault Location Test Device

In order to improve the quality and performance of electronic equipment, circuit parameters and fault detection technology are also very important. The impedance value, which differs obviously under different input signals in the analog circuit, is also an important parameter. Through the analysis of this parameter, RLC circuit parameters and fault location detection can be realized. In this paper, STM32 is used as the main controller to control the signal source to generate sinusoidal signal. The signal processing is completed by designing the amplifier module, and the signal acquisition is completed by the digital to analog conversion module. In the controller, the impedance analysis, the measurement of component parameters, the detection of load network structure and the measurement of short-circuit point position are completed. Finally, the designed system was used to test different structural loads, and the detection results of component parameters, load network structure and short-circuit point position are accurate and reliable.

2-DOF robot modelling by SimMechanics and PD-FL integrated controller for position control and trajectory tracking

Background Due to the high demand of robots to perform several industrial tasks, such as welding, machining, pick and place, position control in robotics has attracted high attention recently. Controllers’ improvement is also continuous specifically in terms of design simplicity and performance accuracy. This research plans to obtain the SimMechanics model of a two-degree of freedom (DOF) robot and to propose an integrated controller of a proportional–derivative (PD) controller and a fuzzy logic (FL) controller. Methodology The SimMechanics model of the 2-DOF robot is obtained using MATLAB SimMechanics toolbox from a CAD assembly design of the 2-DOF robot. Then, the proposed PD-FL integrated controller is designed and simulated in MATLAB Simulink. The PD controller is widely used for its simplicity, but it doesn’t have a satisfactory performance in difficult tasks. Furthermore, the FL controller is also easy for design and implementation even by non-experts in control theory, but it has the disadvantage of long computational time for multi-input systems due to the increased fuzzy rules. Results The FL controller is integrated with the PD controller for enhanced performance of the 2-DOF robot. The PD-FL integrated controller is developed and tested to control the 2-DOF robot for point-to-point position control and also tip trajectory tracking (TTT) such as triangular TTT and rhombic TTT. Conclusion The PD-FL integrated controller demonstrates enhanced performance compared to the conventional PD controller in both point-to-point position control and TTT. Furthermore, the PD-FL integrated controller has the advantage of less fuzzy rules which helps to overcome the computational time issue of the FL controller.

Clinical Testing on External Ventricular Drainage Guidance using Mixed Reality

Augmented reality surgery systems have played an important role in assisting physicians in their operations. However, applying the system to brain neurosurgery is challenging. In addition to using the augmented reality technology to display the 3D position of the surgical target position in real time, we also need to consider the display of the scalpel entry point and scalpel orientation, and their accurate superposition on patients. This paper proposes a mixed reality surgical navigation system, which accurately superimposes the surgical target position, scalpel entry point and scalpel direction on a patient's head and displays it on a tablet, facilitating the visual and intuitive way for the brain neurosurgery. Based on the current neurosurgery navigation system, we integrated mixed reality technology on it. We first independently tested the accuracy of the optical measurement system-NDI Polaris Vicra, and then designed functions that a physician can quickly point out the surgical target position and decide an entry point position, and a tablet can display the superimposed images of surgical target, entry point, and scalpel, and perform the correctness of scalpel orientation. Then we used the Dicom of the patient CT to create a phantom and it’s AR model, imported this AR model into the APP, and installed and executed the APP on the tablet. In the preoperative phase, the technician first superimposed the AR image of head and the scalpel in 5-7 minutes, and then the physician point out and set the target position and entry point position in 2 minutes on a tablet, which then dynamically displayed the superimposed image of the head, target position, entry point position, and scalpel (including the scalpel tip and scalpel spatial direction). We successfully conducted multiple experiments on phantom and six experiments on clinical neurosurgical EVD practice. In the 2D-plane-superposition model (n = 60), the optical measurement system (NDI Polaris Vicra) was feasible of the visualization space with high accuracy (mean error ± standard deviation (SD): 2.013 ± 1.118 mm). In the clinical trials in the hospital (n = 4), the average technician preparation time was 6.317 minutes. The average time (n = 4) required for the physician to set the target position and the entry-point position and accurately overlay the orientation with a surgical stick was 3.5 minutes. In the preparation phase, the average time required for the Dicom image processing and program importing was 120 ± 30 minutes. The designed mixed reality optical surgical navigation system can successfully achieve clinical accuracy, and guide physicians to perform brain surgery visually and intuitively. In addition, the physician can use the APP of the tablet device to instantly obtain Dicom images with the designated patient, change the position of the surgical entry point, and instantly obtain the accurate surgical path and surgical angle after the modification. This design can be used as the basis for various AR or MR brain surgery navigation systems in the future.

A Cost Function for the Uncertainty of Matching Point Distribution on Image Registration

Computing the homography matrix using the known matching points is a key step in computer vision for image registration. In practice, the number, accuracy, and distribution of the known matching points can affect the uncertainty of the homography matrix. This study mainly focuses on the effect of matching point distribution on image registration. First, horizontal dilution of precision (HDOP) is derived to measure the influence of the distribution of known points on fixed point position accuracy on the image. The quantization function, which is the average of the center points’ HDOP* of the overlapping region, is then constructed to measure the uncertainty of matching distribution. Finally, the experiments in the field of image registration are performed to verify the proposed function. We test the consistency of the relationship between the proposed function and the average of symmetric transfer errors. Consequently, the proposed function is appropriate for measuring the uncertainty of matching point distribution on image registration.

Optimal fusion of multi-focus image: Integrating WNMF and focal point analysis

Image fusion can be used to improve the image utilization, spatial resolution and spectral resolution, which has been widely applied on medicine, remote sensing, computer vision, weather forecast and military target recognition. The goal of image fusion is to reduce the uncertainty and redundancy of the output and increase the reliability of the image on the basis of the maximum combination of relevant information. In this paper, a multi-focus image fusion algorithm based on WNMF and Focal point position analysis is proposed to improve the image fusion method based on nonnegative matrix factorization. In the imaging process, the Gaussian function is used to approximate the point spread function in the optical system. Then calculate the difference between the original image and the approximate point spread function and get the weighted matrix [Formula: see text]. Finally, we apply the weighted nonnegative matrix algorithm to image fusion, and the new fusion image with clear parts is obtained. Experimental results show that the multi-focus image fusion algorithm based on WNMF and Focal point position analysis (MFWF) is better.