In this paper, we propose a modularized architecture for a robot arm object fetching system integrated with 3D CAD-model based object recognition system that can cope with objects in random types and ...poses. The interface and the core functionality of each module in the architecture are discussed in detail. Implementation of each module is also conducted. Furthermore, the assumptions and the working conditions behind each module are carefully examined. We develop the system based on our previous work and enhance the recognition module. To proof the feasibility of our architecture, 3D object recognition and fetching demonstration are successfully implemented, and the result of object recognition, teaching by touching, and fast grasp synthesis are successfully demonstrated.
The purpose of this study was to investigate the prevalence and risk factors of dyslipidemia in a rural population of Henan Province, China.
A total of 20 194 participants aged ≥18 years were ...selected randomly by cluster sampling from two townships(towns)in Henan Province from July to August 2007 and July to August 2008. Investigations included questionnaires, anthropometric measurements, fasting plasma glucose, and lipid profile examination at baseline. A total of 16 155 participants were followed up from July to August 2013 and July to October 2014. Overall, 13 869 participants were included in the study, after excluding 2 286 participants with incomplete dyslipidemia follow-up data. Distributions of the characteristics of dyslipidemia were determined, and prevalence was standardized by age according to data of the 2010 Sixth National Population Census. Risk factors for dyslipidemia were analyzed using a logistic regression model after adjusting for sex, age, education level, marital status, and income stat
Internet-based robotic systems have received much attention in the past years. In this paper, we review the networked mobile robot systems and suggest taxonomy based on the three levels of control ...commands. The performance analysis result shows that direct control has potential difficulty for implementation due to the unpredicted transmission delay of the network. To attack this problem, we have suggested the behavior-programming control concept to avoid disturbances of the Internet latency. For this purpose, primitive local intelligence of the mobile robot is grouped into motion planner, motion executor, and motion assistant, where each of a group is treated as an agent. They are integrated by centralized control architecture based on multiagent concept, communicated through a center information memory. The event-driven concept is applied on the robot to switch the behaviors to accommodate the unpredicted mission autonomously. We have successfully demonstrated experimentally the feasibility and reliability for system through a performance comparison with direct remote control. The behavior-programming control of the networked robot can be extended to explore the unknown environment and to perform remote learning through linguistic interaction.
This paper presents an innovative proximity sensor using microelectromechanical systems (MEMS) technology. The proximity sensor works on the principle of fringe capacitance. The target object does ...not need to be part of the measuring system and could be either a conductor or nonconductor. Modeling of the proximity sensor is performed and closed-form analytical solution is obtained for a ring-shaped sensing pattern. The proximity sensors could be batch fabricated using MEMS technology, and the fabrication process is relatively simple. Measurement of the prototype sensors revealed promising results. The size of the proximity sensor could vary from a few hundred micrometers to the size of the substrate. The flexibility on sensor size, sensing patterns, and sensing pattern geometrical parameters makes the sensor very versatile and capable of precision measurement of proximity in the range from micrometers to centimeters. The small size of the sensor makes it possible to surface mount the sensor in many space-constrained places. This advantage is vital in many areas, such as MEMS, microrobotics, precision engineering, machine automation, inspection tools, and many other applications. The ability of the proximity sensor in measuring relative permittivity of materials also finds the sensor useful applications in biomedical and tissue engineering. In addition, this micro proximity sensor is an ideal building block for many other types of sensors, such as force, tactile, and flow sensors.
The Probabilistic Movement Primitives (ProMPs) is an essential issue and framework for robotics Learning from Demonstration (LfD). It has been successfully applied to the robotics field in tasks such ...as skill acquisition and Human-Robot Collaboration (HRC). In this paper, we focus on its adaptability in the HRC scenario, in which the adaptability of the ProMPs allows the robot to predict the future movement of its human partner and plan its movement accordingly, given the observed human movement. Most of the existing works about the application of the ProMPs in HRC either only focus on the estimation of the weights on-line and lack the estimation of the phase parameter or merely depend on the prior distribution of the phase parameter. As a result, these methods can lead to a misinterpretation of the basis matrix when the divergence between the prior distribution and the posterior distribution of the phase parameter becomes large, resulting in a divergence of the estimation of the weights. In this paper, we propose a Dual-Filtering method for the ProMPs, which is able to simultaneously on-line estimate the weights and phase parameter for the ProMPs. The preliminary experimental result demonstrates the proposed method is able to provide better prediction performance and more accurate estimation of the phase parameter in comparison with the previous works.
Due to the widespread use of industrial robots in market, its application has extended to welding, painting, and freight handling. And tool coordinate calibration is regularly modified after tool ...replacement due to collision accident or routine maintenance. After tool replacement, operators often rebuild tool coordinates. This is the traditional mode of operation in the current industrial practices. However, smart factory will make artificial intelligence method replace manual method. This paper presents a system independent method for automatic calibration of the tool coordinate system which is faster, simpler, cheaper and more effective than the manual method. The proposed method required images to be captured using two "eye to hand" cameras and one "eye in hand" camera. Tool position data is then acquired through CamShift and MeanShift algorithm for image trajectory tracking along with coordinate system conversion, several methods like PCA, LDA can deal with the vision data. Optimal Deep Neural Network (DNN) method error compensation of a robot allows the tool to automatically run with the calibration system functions. We have developed a 6 degrees of freedom(DoF) industrial robot for this experiment. Nine different kinds of DNN models are built and finally with suitable tool coordinate error compensation for the current robot, tool calibration can be achieved adaptively and efficiently.
Diagnosis and prediction of the health status of vehicle components production line machine is the core requirement for global manufacturing system. With the development of Internet of things (IoT), ...there are enormous big data of production line could be collected quickly and stored in large quantities. The development of artificial intelligence makes it possible to deal with big data efficiently. Due to the industrial requirement of health self-diagnosis for vehicle production line, this paper presents a method based on Artificial Neural Network (ANN) to diagnose the health status of production line machines using the data produced by the machines. The PID control parameters of motors are segmented to simulate the health status of the machines in a long duration. We use three kinds of artificial neural network (ANN) methods to train the model of the relationship between the large data trend and the diagnostic score of the machine, it is demonstrated that it becomes more efficient than traditional empirical analysis to improve the speed and accuracy for diagnostic and prediction of machines health status.
