Fused Smart Sensor Network for Multi-Axis Forward Kinematics Estimation in Industrial Robots

Flexible manipulator robots have a wide industrial application. Robot performance requires sensing its position and orientation adequately, known as forward kinematics. Commercially available, motion controllers use high-resolution optical encoders to sense the position of each joint which cannot detect some mechanical deformations that decrease the accuracy of the robot position and orientation. To overcome those problems, several sensor fusion methods have been proposed but at expenses of high-computational load, which avoids the online measurement of the joint’s angular position and the online forward kinematics estimation. The contribution of this work is to propose a fused smart sensor network to estimate the forward kinematics of an industrial robot. The developed smart processor uses Kalman filters to filter and to fuse the information of the sensor network. Two primary sensors are used: an optical encoder, and a 3-axis accelerometer. In order to obtain the position and orientation of each joint online a field-programmable gate array (FPGA) is used in the hardware implementation taking advantage of the parallel computation capabilities and reconfigurability of this device. With the aim of evaluating the smart sensor network performance, three real-operation-oriented paths are executed and monitored in a 6-degree of freedom robot

The next phases of the Migrante Project: Study protocol to expand an observatory of migrant health on the Mexico—U.S. border

BackgroundMexican migrants traveling across the Mexico-United States (U.S.) border region represent a large, highly mobile, and socially vulnerable subset of Mexican nationals. Population-level health data for this group is hard to obtain given their geographic dispersion, mobility, and largely unauthorized status in the U.S. Over the last 14 years, the Migrante Project has implemented a unique migration framework and novel methodological approach to generate population-level estimates of disease burden and healthcare access for migrants traversing the Mexico-U.S. border. This paper describes the rationale and history of the Migrante Project and the protocol for the next phases of the project.Methods/designIn the next phases, two probability, face-to-face surveys of Mexican migrant flows will be conducted at key crossing points in Tijuana, Ciudad Juarez, and Matamoros (N = 1,200 each). Both survey waves will obtain data on demographics, migration history, health status, health care access, COVID-19 history, and from biometric tests. In addition, the first survey will focus on non-communicable disease (NCD), while the second will dive deeper into mental health and substance use. The project will also pilot test the feasibility of a longitudinal dimension with 90 survey respondents that will be re-interviewed by phone 6 months after completing the face-to-face baseline survey.DiscussionInterview and biometric data from the Migrante project will help to characterize health care access and health status and identify variations in NCD-related outcomes, mental health, and substance use across migration phases. The results will also set the basis for a future longitudinal extension of this migrant health observatory. Analyses of previous Migrante data, paired with data from these upcoming phases, can shed light on the impact of health care and immigration policies on migrants’ health and inform policy and programmatic responses to improve migrant health in sending, transit, and receiving communities

FPGA-Based Fused Smart-Sensor for Tool-Wear Area Quantitative Estimation in CNC Machine Inserts

Manufacturing processes are of great relevance nowadays, when there is a constant claim for better productivity with high quality at low cost. The contribution of this work is the development of a fused smart-sensor, based on FPGA to improve the online quantitative estimation of flank-wear area in CNC machine inserts from the information provided by two primary sensors: the monitoring current output of a servoamplifier, and a 3-axis accelerometer. Results from experimentation show that the fusion of both parameters makes it possible to obtain three times better accuracy when compared with the accuracy obtained from current and vibration signals, individually used

FPGA-Based Fused Smart Sensor for Dynamic and Vibration Parameter Extraction in Industrial Robot Links

Intelligent robotics demands the integration of smart sensors that allow the controller to efficiently measure physical quantities. Industrial manipulator robots require a constant monitoring of several parameters such as motion dynamics, inclination, and vibration. This work presents a novel smart sensor to estimate motion dynamics, inclination, and vibration parameters on industrial manipulator robot links based on two primary sensors: an encoder and a triaxial accelerometer. The proposed smart sensor implements a new methodology based on an oversampling technique, averaging decimation filters, FIR filters, finite differences and linear interpolation to estimate the interest parameters, which are computed online utilizing digital hardware signal processing based on field programmable gate arrays (FPGA)

FPGA-based Fused Smart Sensor for Real-Time Plant-Transpiration Dynamic Estimation

Plant transpiration is considered one of the most important physiological functions because it constitutes the plants evolving adaptation to exchange moisture with a dry atmosphere which can dehydrate or eventually kill the plant. Due to the importance of transpiration, accurate measurement methods are required; therefore, a smart sensor that fuses five primary sensors is proposed which can measure air temperature, leaf temperature, air relative humidity, plant out relative humidity and ambient light. A field programmable gate array based unit is used to perform signal processing algorithms as average decimation and infinite impulse response filters to the primary sensor readings in order to reduce the signal noise and improve its quality. Once the primary sensor readings are filtered, transpiration dynamics such as: transpiration, stomatal conductance, leaf-air-temperature-difference and vapor pressure deficit are calculated in real time by the smart sensor. This permits the user to observe different primary and calculated measurements at the same time and the relationship between these which is very useful in precision agriculture in the detection of abnormal conditions. Finally, transpiration related stress conditions can be detected in real time because of the use of online processing and embedded communications capabilities

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

In electric power systems, there are always power quality disturbances (PQDs). Usually, noise contamination interferes with their detection and classification. Common methods perform frequency or time-frequency analyses on the power distribution signal for detecting and classifying a limited number of PQDs with some difficulties at low signal-to-noise ratio (SNR). In this regard, recently proposed methodologies for PQD detection estimate several parameters and apply distinct signal processing techniques to improve the detection of PQD. In this work, a novel methodology that merges empirical mode decomposition (EMD), the moments of a random variable, and an artificial neural network (ANN) is proposed for detecting and classifying different PQD. The proposed method estimates skewness, kurtosis, and Shannon entropy from the EMD of one-phase voltage/current signal. Then, an ANN is in charge of classifying the input signal into one of nine different classes for PQD, receiving these parameters as inputs. The effectiveness of the proposed method was verified through computer simulations and experimentation with real data. Obtained results demonstrate its high effectiveness reaching an outstanding 100% of accuracy in detecting and classifying all treated PQD through a few number of parameters, outperforming most of previously proposed approaches

FPGA-Based Online PQD Detection and Classification through DWT, Mathematical Morphology and SVD

Power quality disturbances (PQD) in electric distribution systems can be produced by the utilization of non-linear loads or environmental circumstances, causing electrical equipment malfunction and reduction of its useful life. Detecting and classifying different PQDs implies great efforts in planning and structuring the monitoring system. The main disadvantage of most works in the literature is that they treat a limited number of electrical disturbances through personal computer (PC)-based computation techniques, which makes it difficult to perform an online PQD classification. In this work, the novel contribution is a methodology for PQD recognition and classification through discrete wavelet transform, mathematical morphology, decomposition of singular values, and statistical analysis. Furthermore, the timely and reliable classification of different disturbances is necessary; hence, a field programmable gate array (FPGA)-based integrated circuit is developed to offer a portable hardware processing unit to perform fast, online PQD classification. The obtained numerical and experimental results demonstrate that the proposed method guarantees high effectiveness during online PQD detection and classification of real voltage/current signals

Broken Rotor Bar Detection in Induction Motors through Contrast Estimation

Induction motors (IM) are key components of any industrial process; hence, it is important to carry out continuous monitoring to detect incipient faults in them in order to avoid interruptions on production lines. Broken rotor bars (BRBs), which are among the most regular and most complex to detect faults, have attracted the attention of many researchers, who are searching for reliable methods to recognize this condition with high certainty. Most proposed techniques in the literature are applied during the IM startup transient, making it necessary to develop more efficient fault detection techniques able to carry out fault identification during the IM steady state. In this work, a novel methodology based on motor current signal analysis and contrast estimation is introduced for BRB detection. It is worth noting that contrast has mainly been used in image processing for analyzing texture, and, to the best of the authors’ knowledge, it has never been used for diagnosing the operative condition of an induction motor. Experimental results from applying the approach put forward validate Unser and Tamura contrast definitions as useful indicators for identifying and classifying an IM operational condition as healthy, one broken bar (1BB), or two broken bars (2BB), with high certainty during its steady state

Risk behaviours for HIV infection among travelling Mexican migrants: The Mexico–US border as a contextual risk factor

The Mexico–US border region is a transit point in the trajectory of Mexican migrants travelling to and from the USA and a final destination for domestic migrants from other regions in Mexico. This region also represents a high-risk environment that may increase risk for HIV among migrants and the communities they connect. We conducted a cross-sectional, population-based survey, in Tijuana, Mexico, and compared Mexican migrants with a recent stay on the Mexico–US border region (Border, n = 553) with migrants arriving at the border from Mexican sending communities (Northbound, n = 1077). After controlling for demographics and migration history, border migrants were more likely to perceive their risk for HIV infection as high in this region and regard this area as a liberal place for sexual behaviours compared to Northbound migrants reporting on their perceptions of the sending communities (p < .05). Male border migrants were more likely to engage in sex, and have unprotected sex, with female sex workers during their recent stay on the border compared to other contexts (rate ratio = 3.0 and 6.6, respectively, p < .05). Binational and intensified interventions targeting Mexican migrants should be deployed in the Mexican border region to address migration related HIV transmission in Mexico and the USA