Pub Date : 2020-03-06DOI: 10.17979/SPUDC.9788497497565.0717
Miguel Angel Manrique Alonso, J. Prieto, José Emilio Traver Becerra, A. Hunt, S. Hosseinnia, I. T. Balsera, B. M. V. Jara
Ionic polymer metal composite (IPMC) actuators have promising applications in robotics and medicine in a not distance future, which will require a big knowledge in different fields, such as, manufacturing, material characterization and control theory. In this paper, frequency response of several IPMC actuators, cut from the same bulk IPMC sheet with a micro laser etching machine, is analyzed. Specifically, the objective is to 1) characterize each actuator, i.e., determine how cutting affects to the parameters of the physical model of this kind of actuators, and 2) identify a model for each actuator for control purposes. The frequency responses have been obtained experimentally in LabVIEW by attaching a couple of gold electrodes to each IPMC unit and measuring the tip deection by means of a laser distance meter.
{"title":"Frequency response of IPMC actuators: physical characterization and identification for control","authors":"Miguel Angel Manrique Alonso, J. Prieto, José Emilio Traver Becerra, A. Hunt, S. Hosseinnia, I. T. Balsera, B. M. V. Jara","doi":"10.17979/SPUDC.9788497497565.0717","DOIUrl":"https://doi.org/10.17979/SPUDC.9788497497565.0717","url":null,"abstract":"Ionic polymer metal composite (IPMC) actuators have promising applications in robotics and medicine in a not distance future, which will require a big knowledge in different fields, such as, manufacturing, material characterization and control theory. In this paper, frequency response of several IPMC actuators, cut from the same bulk IPMC sheet with a micro laser etching machine, is analyzed. Specifically, the objective is to 1) characterize each actuator, i.e., determine how cutting affects to the parameters of the physical model of this kind of actuators, and 2) identify a model for each actuator for control purposes. The frequency responses have been obtained experimentally in LabVIEW by attaching a couple of gold electrodes to each IPMC unit and measuring the tip deection by means of a laser distance meter.","PeriodicalId":444871,"journal":{"name":"Actas de las XXXIX Jornadas de Automática, Badajoz, 5-7 de septiembre de 2018","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121519825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-06DOI: 10.17979/spudc.9788497497565.0657
C. Vilas, A. Alonso
Sterilization process is aimed to inactivate potentially harmful microorganisms. To that purpose the packaged food is subject to a time/temperature profile. In the canning industry such profiles are chosen based on the experience of the operator. In the presence of perturbations, such as steam supply problems, operators must react and design new profiles which, in most of the cases, are too conservative and/or may lead to the batch rejection, either because of quality or safety reasons. In order to overcome this problem, we propose in this work a model based real time optimization (RTO) strategy. The model, which describes the different relevant aspects of the plant (retort/can temperature, color dynamics, microorganism lethality, energy consumption, etc.) is used to predict the behavior of the system. Plant measurements are taken periodically and, in the event of a perturbation, an optimization procedure is run to compute a new time/temperature profile based on the past measurements.
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Pub Date : 2020-03-06DOI: 10.17979/SPUDC.9788497497565.0636
Á. Canelo, I. T. Balsera, José Emilio Traver Becerra, B. M. V. Jara, Cristina Nuevo Gallardo
Biologically, the vestibular feedback is critical to the ability of human body to balance in different conditions. This paper presents a human-inspired orientation and balance control of a three degree- of-freedom (DOF) simulator that emulates a person sitting in a platform. In accordance with the role in humans, the control is essentially based on the vestibular system (VS), which regulates and stabilizes gaze during head motion, by means of modeling the behavior of the semicircular canals and otoliths in the presence of stimuli, i.e., linear and angular accelerations/velocities derived by the turns experienced by the robot head on the three Cartesian axes. The semicircular canal is used as the angular velocity sensor to perform the postural control of the robot. Simulation results in the MATLAB/Simulink environment are given to show that the orientation of the head in space (roll, pitch and yaw) can be successfully controlled by a proportional-integral-derivative (PID) with noise filter for each DOF.
{"title":"Modeling of the human vestibular system and integration in a simulator for the study of orientation and balance control","authors":"Á. Canelo, I. T. Balsera, José Emilio Traver Becerra, B. M. V. Jara, Cristina Nuevo Gallardo","doi":"10.17979/SPUDC.9788497497565.0636","DOIUrl":"https://doi.org/10.17979/SPUDC.9788497497565.0636","url":null,"abstract":"Biologically, the vestibular feedback is critical to the ability of human body to balance in different conditions. This paper presents a human-inspired orientation and balance control of a three degree- of-freedom (DOF) simulator that emulates a person sitting in a platform. In accordance with the role in humans, the control is essentially based on the vestibular system (VS), which regulates and stabilizes gaze during head motion, by means of modeling the behavior of the semicircular canals and otoliths in the presence of stimuli, i.e., linear and angular accelerations/velocities derived by the turns experienced by the robot head on the three Cartesian axes. The semicircular canal is used as the angular velocity sensor to perform the postural control of the robot. Simulation results in the MATLAB/Simulink environment are given to show that the orientation of the head in space (roll, pitch and yaw) can be successfully controlled by a proportional-integral-derivative (PID) with noise filter for each DOF.","PeriodicalId":444871,"journal":{"name":"Actas de las XXXIX Jornadas de Automática, Badajoz, 5-7 de septiembre de 2018","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130809440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-06DOI: 10.17979/SPUDC.9788497497565.0224
Edison Velasco Sánchez, Brayan Stiven Zapata Impata, Pablo Gil
espanolEste trabajo se presenta una aproximacion hibrida propioceptiva-tactil para reconocer objetos agarrados. La informacion propioceptiva de una mano robotica es usada para estimar la geometria de contacto y asi, distinguir la forma de cada uno de los objetos que estan siendo agarrados. La geometria de contacto viene determinada por los datos articulares de la mano robotica cuando esta lleva a cabo un agarre en configuracion de cierre sobre la superficie del objeto. Ademas, la informacion tactil permite determinar propiedades de rigidez y flexibilidad del objeto agarrado, mejorando el proceso de reconocimiento cuando la geometria de contacto y por lo tanto, la forma de los objetos es similar. El metodo propuesto emplea tecnicas de clasificacion de aprendizaje supervisado para combinar los datos de ambos tipos de sensores e identificar el tipo de objeto con un porcentaje de acierto medio del 95,5% con metrica ‘accuracy’ y 95.3% con F1(F-score) aun en presencia de incertidumbre de medida y ambiguedad. Estas ratios de acierto se han alcanzado experimentando con 7 objetos domesticos y llevando a cabo mas de 3000 agarres. EnglishThis work presents a hybrid proprioceptive-tactile approach to recognize grasped objects. Proprioceptive data of a robotic hand are used to estimate contact geometry and thus, to distinguish the shape of each of the objects that are being grasped. The contact geometry is determined by the joint data of the robotic hand when it carries out a grip in closure grasps configuration on the object surface. In addition, the tactile data allow to robotic hand to determine rigidity and flexibility properties of the grasped object, improving the recognition process when the contact geometry and therefore, the shapes of different objects are similar. The proposed method employs supervised learning classification techniques to combine the data from both types of sensors and identify the type of object with an average success rate of 95,5% (with accuracy) and 95.3% (with F1 or F-score)even in the presence of measurement with uncertainty and ambiguity of pose. These success ratios have been achieved by experimenting with 7 different objects and performing more than 3000 grasps.
这项工作提出了一种混合的本体感觉-触觉方法来识别被抓住的物体。机器人手的本体感受信息被用来估计接触的几何形状，从而区分被抓住的每个物体的形状。接触几何形状是由机械手在物体表面进行闭合抓地力时的关节数据决定的。此外，触觉信息可以确定被抓取物体的刚度和柔韧性，改进了当接触几何形状和物体形状相似时的识别过程。该方法采用监督学习分类技术，结合来自两种类型传感器的数据，识别物体类型，即使在测量不确定度和模糊性存在的情况下，平均准确率为95.5%(度量精度)，平均准确率为95.3% (F-score)。这些成功的比率已经达到了7个家庭物品的实验和3000多个抓地力。这项工作提出了一种混合本体感觉-触觉方法来识别被抓住的物体。= =地理= =根据美国人口普查，这个县的面积为，其中土地面积为，其中土地面积为。The contact geometry is果决by The joint data of The when it独立robotic hand out a抓地力in封锁类人猿configuration on The object surface。此外，触觉数据使机器人手能够确定被抓物体的刚度和灵活性，改进了接触几何形状时的识别过程，从而使不同物体的形状相似。所提议的方法采用监督学习分类技术，将来自两种类型传感器的数据结合起来，并以95.5%(准确率)和95.3% (F1或F-score)的平均成功率确定对象类型，即使测量存在不确定性和模糊性。这些成功比率已经一by experimenting with 7不同objects and performing more than 3000类人猿。
{"title":"Reconocimiento de objetos agarrados con sensorizado híbrido propioceptivo-táctil","authors":"Edison Velasco Sánchez, Brayan Stiven Zapata Impata, Pablo Gil","doi":"10.17979/SPUDC.9788497497565.0224","DOIUrl":"https://doi.org/10.17979/SPUDC.9788497497565.0224","url":null,"abstract":"espanolEste trabajo se presenta una aproximacion hibrida propioceptiva-tactil para reconocer objetos agarrados. La informacion propioceptiva de una mano robotica es usada para estimar la geometria de contacto y asi, distinguir la forma de cada uno de los objetos que estan siendo agarrados. La geometria de contacto viene determinada por los datos articulares de la mano robotica cuando esta lleva a cabo un agarre en configuracion de cierre sobre la superficie del objeto. Ademas, la informacion tactil permite determinar propiedades de rigidez y flexibilidad del objeto agarrado, mejorando el proceso de reconocimiento cuando la geometria de contacto y por lo tanto, la forma de los objetos es similar. El metodo propuesto emplea tecnicas de clasificacion de aprendizaje supervisado para combinar los datos de ambos tipos de sensores e identificar el tipo de objeto con un porcentaje de acierto medio del 95,5% con metrica ‘accuracy’ y 95.3% con F1(F-score) aun en presencia de incertidumbre de medida y ambiguedad. Estas ratios de acierto se han alcanzado experimentando con 7 objetos domesticos y llevando a cabo mas de 3000 agarres. EnglishThis work presents a hybrid proprioceptive-tactile approach to recognize grasped objects. Proprioceptive data of a robotic hand are used to estimate contact geometry and thus, to distinguish the shape of each of the objects that are being grasped. The contact geometry is determined by the joint data of the robotic hand when it carries out a grip in closure grasps configuration on the object surface. In addition, the tactile data allow to robotic hand to determine rigidity and flexibility properties of the grasped object, improving the recognition process when the contact geometry and therefore, the shapes of different objects are similar. The proposed method employs supervised learning classification techniques to combine the data from both types of sensors and identify the type of object with an average success rate of 95,5% (with accuracy) and 95.3% (with F1 or F-score)even in the presence of measurement with uncertainty and ambiguity of pose. These success ratios have been achieved by experimenting with 7 different objects and performing more than 3000 grasps.","PeriodicalId":444871,"journal":{"name":"Actas de las XXXIX Jornadas de Automática, Badajoz, 5-7 de septiembre de 2018","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129152090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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