<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1794-1237</journal-id>
<journal-title><![CDATA[Revista EIA]]></journal-title>
<abbrev-journal-title><![CDATA[Rev.EIA.Esc.Ing.Antioq]]></abbrev-journal-title>
<issn>1794-1237</issn>
<publisher>
<publisher-name><![CDATA[Escuela de ingenieria de Antioquia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1794-12372018000100109</article-id>
<article-id pub-id-type="doi">10.24050/reia.v15i29.690</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[GENERALIZACIÓN DE LAS TRAYECTORIAS DE UN BRAZO ROBÓTICO UTILIZANDO PRIMITIVAS DE MOVIMIENTO DINÁMICO Y REGRESIÓN DE PROCESOS GAUSSIANOS]]></article-title>
<article-title xml:lang="en"><![CDATA[GENERALIZATION OF THE TRAJECTORIES OF A ROBOTIC ARM USING DYNAMIC MOVEMENT PRIMITIVES AND GAUSSIAN PROCESS REGRESSION]]></article-title>
<article-title xml:lang="pt"><![CDATA[GENERALIZAÇÃO DAS TRAJETÓRIAS DE UM BRAÇO ROBÓTICO USANDO PRIMITIVAS MOVIMENTO DINÂMICO E REGRESSÃO DOS PROCESSOS DE GAUSS]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Peña-Solórzano]]></surname>
<given-names><![CDATA[Carlos Andrés]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hoyos-Gutiérrez]]></surname>
<given-names><![CDATA[José Gabriel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Prieto-Ortiz]]></surname>
<given-names><![CDATA[Flavio Augusto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional de Colombia  ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad del Quindío  ]]></institution>
<addr-line><![CDATA[Armenia ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional de Colombia  ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2018</year>
</pub-date>
<volume>15</volume>
<numero>29</numero>
<fpage>109</fpage>
<lpage>123</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S1794-12372018000100109&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S1794-12372018000100109&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S1794-12372018000100109&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Es común encontrar robots realizando tareas en áreas compartidas con humanos, donde se espera que sean capaces de aprender de las acciones realizadas por otros y de adaptarse a nuevas situaciones. En este trabajo, se capturan las trayectorias del brazo de un operario mientras se mueve para agarrar un objeto, realizando seguimiento de articulaciones con el sensor kinect de Microsoft. La técnica utilizada para la codificación de las señales de entrenamiento se denominan primitivas de movimiento dinámico (DMP), mientras que la reconstrucción se realiza mediante regresión de procesos gaussianos (GPR). GPR permite además, generalizar los movimientos de entrenamiento a nuevas trayectorias, cuando cambian tanto la posición inicial de la mano como la ubicación del objeto. La técnica de generalización se compara contra un algoritmo basado en distancia de Mahalanobis y distribución gaussiana, que utiliza los datos de la trayectoria sin codificar, para realizar la estimación. La técnica propuesta presentó bajos tiempos de codificación y errores pequeños con respecto a los valores objetivo al probarlo con 30 puntos de consulta para el valor inicial de la mano, y 30 puntos para la posición final.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT It is common to find robots performing tasks in areas shared with humans, where they are expected to be able to learn from the actions taken by others and adapt to new situations. In this paper, the trajectories of the arm of an operator are captured while moving to grab an object, making joint tracking with the Microsoft kinect sensor. The technique used for the coding of the training signals is called dynamic movement primitives (DMP), while reconstruction is performed using gaussian process regression (GPR). GPR also allows generalize training movements to new paths when changing both the initial hand position and the location of the object. The technique is compared against a generalization based on Mahalanobis distance and gaussian distribution, which uses the path data to make the estimate. The proposed technique presented low encoding times and small errors regarding the target values when tested with 30 points of consultation for the initial value of the hand, and 30 points for the final position.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[RESUMO No percurso dos dias é comum encontrar robôs fazendo tarefas em áreas compartilhadas com os seres humanos, se deseja que eles sejam capaces de aprender com as ações realizadas por pessoas e se adaptarem a novas situações. Neste trabalho, as trajetórias do braço de um operario são capturadas enquanto se movimenta para agarrar um objeto. O seguimento das articulaões é feito com um sensor Kinect da Microsoft. A técnica utilizada para codificar os sinais de treinamento é chamada de Primitivas de movimento dinâmico (DMP), enquanto a reconstrução é feita por meio de regressão processos de Gauss (GPR). A técnica GPR permite também generalizar os movimentos de treinamento para novas trajetórias quando mudam tanto a posição inicial da mão como a localização do objeto. A técnica de generalização é comparada com um algoritmo baseado na distância de Mahalanobis e a distribuição de Gauss, ela usa os dados da trajetória não codificados, para fazer a estimativa. A técnica proposta mostrou tempos de codificação baixos e erros menores em relação aos valores-alvo, quando testado com 30 pontos de consulta para o valor inicial da mão, e 30 pontos para a posição final do objeto.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Robótica]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje por imitación]]></kwd>
<kwd lng="es"><![CDATA[programación por demostración]]></kwd>
<kwd lng="es"><![CDATA[primitivas de movimiento dinámico]]></kwd>
<kwd lng="es"><![CDATA[regresión de procesos gaussianos]]></kwd>
<kwd lng="en"><![CDATA[Robotics]]></kwd>
<kwd lng="en"><![CDATA[learning by imitation]]></kwd>
<kwd lng="en"><![CDATA[programming by demonstration]]></kwd>
<kwd lng="en"><![CDATA[dynamic movement primitives]]></kwd>
<kwd lng="en"><![CDATA[gaussian process regression]]></kwd>
<kwd lng="pt"><![CDATA[Robótica]]></kwd>
<kwd lng="pt"><![CDATA[aprendizagem por imitação]]></kwd>
<kwd lng="pt"><![CDATA[programação por demonstração]]></kwd>
<kwd lng="pt"><![CDATA[primitivas de movi-mento dinâmico]]></kwd>
<kwd lng="pt"><![CDATA[regressão de processos de Gauss]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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