A method for Kinematic Calibration of a Parallel Robot by using one camera in hand and a spherical object

The main purpose of robot calibration is the correction of the possible errors in the robot parameters. This paper presents a method for a kinematic calibration of a parallel robot that is equipped with one camera in hand. In order to preserve the mechanical configuration of the robot, the camera is utilized to acquire incremental positions of the end effector from a spherical object that is fixed in the word reference frame. The positions of the end effector are related to incremental positions of resolvers of the motors of the robot, and a kinematic model of the robot is used to find a new group of parameters which minimizes errors in the kinematic equations. Additionally, properties of the spherical object and intrinsic camera parameters are utilized to model the projection of the object in the image and improving spatial measurements. Finally, the robotic system is designed to carry out tracking tasks and the calibration of the robot is validated by means of integrating the errors of the visual controller.

An Inexpensive Method for Kinematic Calibration of a Parallel Robot by Using One Hand-Held Camera as Main Sensor

This paper presents a novel method for the calibration of a parallel robot, which allows a more accurate configuration instead of a configuration based on nominal parameters. It is used, as the main sensor with one camera installed in the robot hand that determines the relative position of the robot with respect to a spherical object fixed in the working area of the robot. The positions of the end effector are related to the incremental positions of resolvers of the robot motors. A kinematic model of the robot is used to find a new group of parameters, which minimizes errors in the kinematic equations. Additionally, properties of the spherical object and intrinsic camera parameters are utilized to model the projection of the object in the image and thereby improve spatial measurements. Finally, several working tests, static and tracking tests are executed in order to verify how the robotic system behaviour improves by using calibrated parameters against nominal parameters. In order to emphasize that, this proposed new method uses neither external nor expensive sensor. That is why new robots are useful in teaching and research activities.

Predicting temporary wetland plant community responses to changes in the hydroperiod

The expected changes on rainfall in the next decades may cause significant changes of the hydroperiod of temporary wetlands and, consequently, shifts on plant community distributions. Predicting plant community responses to changes in the hydroperiod is a key issue for conservation and management of temporary wetlands. We present a predictive distribution model for Arthrocnemum macrostachyum communities in the Doñana wetland (Southern Spain). Logistic regression was used to fit the model using the number of days of inundation and the mean water height as predictors. The internal validation of the model yielded good performance measures. The model was applied to a set of expected scenarios of changes in the hydroperiod to anticipate the most likely shifts in the distribution of Arthrocnemum macrostachyum communities.

Upper Limb Portable Motion Analysis System Based on Inertial Technology for Neurorehabilitation Purposes

Here an inertial sensor-based monitoring system for measuring and analyzing upper limb movements is presented. The final goal is the integration of this motion-tracking device within a portable rehabilitation system for brain injury patients. A set of four inertial sensors mounted on a special garment worn by the patient provides the quaternions representing the patient upper limb’s orientation in space. A kinematic model is built to estimate 3D upper limb motion for accurate therapeutic evaluation. The human upper limb is represented as a kinematic chain of rigid bodies with three joints and six degrees of freedom. Validation of the system has been performed by co-registration of movements with a commercial optoelectronic tracking system. Successful results are shown that exhibit a high correlation among signals provided by both devices and obtained at the Institut Guttmann Neurorehabilitation Hospital.

Análisis preliminar de las tobas cuaternarias del río Ebrón (Castielfabib, Valencia, Cordillera Ibérica)

In this paper the Alpine cleavage affecting the Permo-Triassic series of the Espadan Range (Castellón) is studied. Cleavage affects to argillites and sandstones in Saxonian and Buntsandstein facies. At cartographic scale it is linked with the Espadan box anticline with constant ONO-ESE trend. At microscoscopic scale it constitutes a “spaced cleavage” with a predominance of pressure solution and passive rotation mechanisms. At outcrop scale the cleavage characterizes by a sigmoidal geometry linked both the post-cleavage flexural slip as a cleavage-related flexural flow mechanism. The proposed kinematic model to explain its origin includes three main stages: 1) incipient development of cleavage linked to layer-parallel shortening, 2) buckling and increasing of cleavage penetrativity and 3) folfing amplification and layer-parallel shear. RESUMEN Se estudia la esquistosidad alpina que afecta a la serie Permo-Triásica de la Sierra de Espadán, (Castellón). La esquistosidad afecta a los tramos argilíticos y areniscosos en facies Saxoniense y Buntsandstein, con distinto grado de penetratividad. A escala cartográfica se asocia al anticlinal de Espadán con geometría en cofre y orientación ONO-ESE. A escala microestructural se clasifica como esquistosidad espaciada con predominio de los mecanismos de disolución por presión y rotación mecánica de filosilicatos. A escala de afloramiento destaca la geometría sigmoidal de las superficies de esquistosidad atribuida tanto a un mecanismo post-esquistoso de flexodeslizamiento en las capas competentes como a flexofluencia sin-esquistosa en capas incompetentes. El modelo cinemático para su génesis contempla tres estadios: 1) desarrollo incipiente de esquistosidad en relación a acortamiento paralelo a las capas, 2) buckling e incremento del grado de penetratividad y 3) amplificación de los pliegues y cizalla simple paralela a las capas

Design and modeling of the multi-agent robotic system: SMART

This article presents the design, kinematic model and communication architecture for the multi-agent robotic system called SMART. The philosophy behind this kind of system requires the communication architecture to contemplate the concurrence of the whole system. The proposed architecture combines different communication technologies (TCP/IP and Bluetooth) under one protocol designed for the cooperation among agents and other elements of the system such as IP-Cameras, image processing library, path planner, user Interface, control block and data block. The high level control is modeled by Work-Flow Petri nets and implemented in C++ and C♯♯. Experimental results show the performance of the designed architecture.

Adaptive control system based on lineal control theory for the path-following problem of a car-like mobile robot

The objective of this paper is to design a path following control system for a car-like mobile robot using classical linear control techniques, so that it adapts on-line to varying conditions during the trajectory following task. The main advantages of the proposed control structure is that well known linear control theory can be applied in calculating the PID controllers to full control requirements, while at the same time it is exible to be applied in non-linear changing conditions of the path following task. For this purpose the Frenet frame kinematic model of the robot is linearised at a varying working point that is calculated as a function of the actual velocity, the path curvature and kinematic parameters of the robot, yielding a transfer function that varies during the trajectory. The proposed controller is formed by a combination of an adaptive PID and a feed-forward controller, which varies accordingly with the working conditions and compensates the non-linearity of the system. The good features and exibility of the proposed control structure have been demonstrated through realistic simulations that include both kinematics and dynamics of the car-like robot.

A novel motion tracking system for evaluation of functional rehabilitation of the upper limbs

Upper limb function impairment is one of the most common sequelae of central nervous system injury, especially in stroke patients and when spinal cord injury produces tetraplegia. Conventional assessment methods cannot provide objective evaluation of patient performance and the tiveness of therapies. The most common assessment tools are based on rating scales, which are inefficient when measuring small changes and can yield subjective bias. In this study, we designed an inertial sensor-based monitoring system composed of five sensors to measure and analyze the complex movements of the upper limbs, which are common in activities of daily living. We developed a kinematic model with nine degrees of freedom to analyze upper limb and head movements in three dimensions. This system was then validated using a commercial optoelectronic system. These findings suggest that an inertial sensor-based motion tracking system can be used in patients who have upper limb impairment through data integration with a virtual reality-based neuroretation system.

A General Purpose Configurable Controller for Indoors and Outdoors GPS-Denied Navigation for Multirotor Unmanned Aerial Vehicles

This research on odometry based GPS-denied navigation on multirotor Unmanned Aerial Vehicles is focused among the interactions between the odometry sensors and the navigation controller. More precisely, we present a controller architecture that allows to specify a speed specified flight envelope where the quality of the odometry measurements is guaranteed. The controller utilizes a simple point mass kinematic model, described by a set of configurable parameters, to generate a complying speed plan. For experimental testing, we have used down-facing camera optical-flow as odometry measurement. This work is a continuation of prior research to outdoors environments using an AR Drone 2.0 vehicle, as it provides reliable optical flow on a wide range of flying conditions and floor textures. Our experiments show that the architecture is realiable for outdoors flight on altitudes lower than 9 m. A prior version of our code was utilized to compete in the International Micro Air Vehicle Conference and Flight Competition IMAV 2012. The code will be released as an open-source ROS stack hosted on GitHub.

Sensorless explicit model predictive control for multi-phase buck converters with variable output voltage

The purpose of this paper is to use the predictive control to take advantage of the future information in order to improve the reference tracking. The control attempts to increase the bandwidth of the conventional regulators by using the future information of the reference, which is supposed to be known in advance. A method for designing a controller is also proposed. A comparison in simulation with a conventional regulator is made controlling a four-phase Buck converter. Advantages and disadvantages are analyzed based on simulation results.

Visualization of a student predictive model for 3D virtual environments driven to procedural training

This document presents theimplementation ofa Student Behavior Predictor Viewer(SBPV)for a student predictive model. The student predictive model is part of an intelligent tutoring system, and is built from logs of students’ behaviors in the “Virtual Laboratory of Agroforestry Biotechnology”implemented in a previous work.The SBPVis a tool for visualizing a 2D graphical representationof the extended automaton associated with any of the clusters ofthe student predictive model. Apart from visualizing the extended automaton, the SBPV supports the navigation across the automaton by means of desktop devices. More precisely, the SBPV allows user to move through the automaton, to zoom in/out the graphic or to locate a given state. In addition, the SBPV also allows user to modify the default layout of the automaton on the screen by changing the position of the states by means of the mouse. To developthe SBPV, a web applicationwas designedand implementedrelying on HTML5, JavaScript and C#.

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence.

Lagrangian tools and the assessment of the predictive capacity of geophysical data sets

We examine, with recently developed Lagrangian tools, altimeter data and numerical simulations obtained from the HYCOM model in the Gulf of Mexico. Our data correspond to the months just after the Deepwater Horizon oil spill in the year 2010. Our Lagrangian analysis provides a skeleton that allows the interpretation of transport routes over the ocean surface. The transport routes are further verified by the simultaneous study of the evolution of several drifters launched during those months in the Gulf of Mexico. We find that there exist Lagrangian structures that justify the dynamics of the drifters, although the agreement depends on the quality of the data. We discuss the impact of the Lagrangian tools on the assessment of the predictive capacity of these data sets.

Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices

The diversity of bibliometric indices today poses the challenge of exploiting the relationships among them. Our research uncovers the best core set of relevant indices for predicting other bibliometric indices. An added difficulty is to select the role of each variable, that is, which bibliometric indices are predictive variables and which are response variables. This results in a novel multioutput regression problem where the role of each variable (predictor or response) is unknown beforehand. We use Gaussian Bayesian networks to solve the this problem and discover multivariate relationships among bibliometric indices. These networks are learnt by a genetic algorithm that looks for the optimal models that best predict bibliometric data. Results show that the optimal induced Gaussian Bayesian networks corroborate previous relationships between several indices, but also suggest new, previously unreported interactions. An extended analysis of the best model illustrates that a set of 12 bibliometric indices can be accurately predicted using only a smaller predictive core subset composed of citations, g-index, q2-index, and hr-index. This research is performed using bibliometric data on Spanish full professors associated with the computer science area.

Evaluation of air quality impacts with an integrated assessment model for Spain

The present paper describes the advancement and evaluation of air quality-related impacts with the Atmospheric Evaluation and Research Integrated system for Spain (AERIS). In its current version, AERIS is able to provide estimates on the impacts of air quality over human health (PM2.5 and O3), crops and vegetation (O3). The modules that allow quantifying the before mentioned impacts were modeled by applying different approaches (mostly for the European context) present in scientific literature to the conditions of the Iberian Peninsula. This application was supported by reliable data sources, as well as by the good predictive capacity of AERIS for ambient concentrations. For validation purposes, the estimates of AERIS for impacts on human health (change in the statistical life expectancy-PM2.5) and vegetation (loss of wheat crops-O3) were compared against results from the SERCA project and GAINS estimates for two emission scenarios. In general, good results evidenced by reasonable correlation coefficients were obtained, therefore confirming the adequateness of the followed modeling approaches and the quality of AERIS predictions.