A New Robotic Application for COVID-19 Specimen Collection Process

Covid-19 manual specimen collection process is too critical for health care workers due to they are able to getting infection from Covid-19-patient during the medical interaction. The purpose of this study, a novel robotic application is developed to achieve automatic specimen collection process for new corona virus (COVID-19). This application is a protection tool for health care workers for the Covid-19 pandemic. This robotic application easily and safely complete the sampling process task and assist to health care workers to prevent infection. The application is basically consist of a collaborative robot (COBOT), base plate, fixtures and a gripper. There are co-operation activities between the COBOT and health care worker to complete all tasks. The robotic application has been tested in the plant health care center as a prototype. The cycle-time (192 sec) for the robotic process needs to be improved. The Manual process is still %60 faster than robotic application. The biggest challenge in this application is patient’s mouth and nose physical size changes. Robot movements for the specimen collection in nose and mouth are arranged just based on the fixed point. This needs to be improved according to size changes. Covid-19 specimen collection process with a robotic application has been presented which don’t need any health care worker interaction with patient. This application needs to be improved related with above challenges to make a shelf product. It will create valuable impact and save lives in this pandemic

PSO-embedded adaptive Kriging surrogate model method for structural reliability analysis with small failure probability

In the present study, a novel adaptive surrogate model method is proposed for the analysis of structural reliability with small failure probability. In order to address the problems with conventional adaptive Kriging surrogate model method based on candidate sample pool, the adaptive Kriging surrogate model method which integrates Particle Swarm Optimization algorithm (PSO) is put forward. In the course of implementation, the surrogate model is gradually improved through an iterative process and the high-value samples are selected to update the surrogate model through an optimization solution carried out by using PSO. Numerical examples are used to evaluate the computational performance of the proposed method, and a further discussion is conducted around the revision to the learning function. The results show that the introduction of PSO not only increases the possibility of obtaining high-value samples, but also significantly improves the solution accuracy of the adaptive Kriging surrogate model method for structural reliability analysis. Meanwhile, the proposed method overcomes the problem caused by the conventional candidate pool-based selection method through the optimization algorithm to determine high-value samples, achieving an excellent performance in dealing with the small failure probability. In addition, the proposed method is applicable to achieve a reasonable balance between solution accuracy and efficiency through the revised learning function which takes into account local neighborhood effects

Hybrid Learning Algorithm of Radial Basis Function Networks for Reliability Analysis

With the wide application of industrial robots in the field of precision machining, reliability analysis of positioning accuracy becomes increasingly important for industrial robots. Since the industrial robot is a complex nonlinear system, the traditional approximate reliability methods often produce unreliable results in analyzing its positioning accuracy. In order to study the positioning accuracy reliability of industrial robot more efficiently and accurately, a radial basis function network is used to construct the mapping relationship between the uncertain parameters and the position coordinates of the end-effector. Combining with the Monte Carlo simulation method, the positioning accuracy reliability is then evaluated. A novel hybrid learning algorithm for training radial basis function network, which integrates the clustering learning algorithm and the orthogonal least squares learning algorithm, is proposed in this article. Examples are presented to illustrate the high proficiency and reliability of the proposed method