Near Field communication (NFC) technology enables a flexible short range communication. It has large amount of envisaged applications in consumer, welfare and industrial sector. Compared with other short range communication technologies such as Bluetooth or Wibree it provides advantages that we will introduce in this paper. In this paper, we present an example of applying NFC technology to industrial application where simple tasks can be automatized and industrial assembly process can be improved radically by replacing manual paperwork and increasing trace of the products during the production.
In this paper a concept for industrial ubiquitous robotics is presented. The concept combines two different approaches to manage agile, adaptable production: firstly the human operator is strongly in the production loop and secondly, the robot workcell will be more autonomous and smarter to manage production. This kind of autonomous robot cell can be called production island. Communication to the human operator working in this kind of smart industrial environment can be divided into two levels: body area communication and operator-infrastructure communication including devices, machines and infra. Body area communication can be supportive in two directions: data is recorded by means of measuring physical actions, such as hand movements, body gestures or supportive when it will provide information to user such as guides or manuals for operation. Body area communication can be carried out using short range communication technologies such as NFC (Near Field communication) which is RFID type of communication. In the operator-infrastructure communication, WLAN or Bluetooth -communication can be used. Beyond the current Human Machine interaction HMI systems, the presented system concept is designed to fulfill the requirements for hybrid, knowledge intensive manufacturing in the future, where humans and robots operate in close co-operation.
In this paper, we present a method to generate a set of samples which decreases the uncertainties of the estimated parameters. The goal is to carry out hand-eye calibration, i.e. estimate the transformation from wrist of the robot to the coordinate origin of the sensor attached in the wrist of the robot. Using the presented method we decrease the spatial uncertainties and avoid cases where the set of samples are poor and estimator fails or gives an unreliable estimate for both parameters and related uncertainties. This is important especially in the noised conditions and the cases where only a sparse set of samples is available, e.g. hand-eye calibration with a singlepoint laser rangefinder tactile sensor. The planning method is compared with pattern and random sets of samples and results for the new method are promising.
In this paper, we present a method to estimate spatial uncertainties of a localized workobject using Bayesian estimation. We approch the problem of a sensor eye-in-hand calibration with error covariances by comparing the covariance propagation with Monte Carlo simulation and actual tests when the system noise level is changing. The spatial uncertainties are analysed using eigenvalues of the covariances in the direction of the respective eigenvectors. Results from the comparison between the different methods gives encouraging results and we believe that covariance propagation can be used in uncertainty estimation in different levels of noise.
This paper presents methods to improve flexibility and accuracy of deburring of castings. We apply several methods including off-line programming of the deburring paths, accurate localization of the work object, surface measuring of the work object and a force-guided motion control during the deburring task. The eye-in-hand calibration as well as the localization of the work object we carry out using the Bayesian-form estimation method with recursive sensor fusion. As a result from the work object localization we obtain a 3 DOF location increment (position difference between the simulation model and actual workcell) and actual deburring paths are corrected using that increment. The simulation phase includes octree-based collision check and the faceting uses octree principle too. The paper includes results from actual tests which are promising. The methods are designed to be easy-to-implement in any industrial robot.
In this paper a method to locate work objects with splined surfaces and estimate the spatial uncertainties of the estimated parameters is presented. The reference B-spline surface patch is selected from a work object CAD-model and is defined in the form of control vertices. The process includes the hang-eye calibration of the sensor, determination of the work object localization and surface treating, e.g. inspection. The hand-eye calibration and work object localization are carried out using the Bayesian form estimation with sensor fusion. Use of the recursive sensor fusion method makes calibration more flexible and accurate in handling large data sets. The spatial uncertainties in the form of eigenvalues in the direction of the eigenvectors are analyzed from the error covariance matrices of the estimated parameters.
KEYWORDS: Sensors, Sensor calibration, Calibration, Condition numbers, Inspection, Monte Carlo methods, Matrices, 3D metrology, 3D modeling, Robotic systems
This paper presents a robot based surface inspection application for measuring mould surface in foundries. The surface measuring its performed using a robot with six degrees of freedom equipped with a laser-triangulation-based distance sensor. The measuring process is divided into four phases: sensor calibration, calibration of the mould location, surface inspection measurements and measurement analysis. In the sensor calibration phase, tool correction is calculated by using algorithms based on least square estimation with Newton iteration. Equations derived for calibration uncertainty estimation are verified using Monte Carlo simulations. The real covariance of sensor tool correction has been obtained with test measurements. Calibration of the mould location is based on the same estimation principle as was used in sensor calibration. Total uncertainty of the measuring system is obtained by transforming all separate uncertainties into one total uncertainty covariance. Spatial uncertainties are expressed and manipulated in the form of covariance matrices. The volume of the uncertainty ellipsoid in 3D space is calculated for each sensor calibration and mould location calibration. For comparison, the goodness of the measurement model is evaluated also by the condition number of the Jacobian matrix.
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