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Towards Model-Free Tool Dynamic Identification and Calibration Using Multi-Layer Neural Network

In robot control with physical interaction, like robot-assisted surgery and bilateral teleoperation, the availability of reliable interaction force information has proved to be capable of increasing the control precision and of dealing with the surrounding complex environments. Usually, force sensor...

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Autores principales: Su, Hang, Qi, Wen, Hu, Yingbai, Sandoval, Juan, Zhang, Longbin, Schmirander, Yunus, Chen, Guang, Aliverti, Andrea, Knoll, Alois, Ferrigno, Giancarlo, De Momi, Elena
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6749275/
https://www.ncbi.nlm.nih.gov/pubmed/31438529
http://dx.doi.org/10.3390/s19173636
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author Su, Hang
Qi, Wen
Hu, Yingbai
Sandoval, Juan
Zhang, Longbin
Schmirander, Yunus
Chen, Guang
Aliverti, Andrea
Knoll, Alois
Ferrigno, Giancarlo
De Momi, Elena
author_facet Su, Hang
Qi, Wen
Hu, Yingbai
Sandoval, Juan
Zhang, Longbin
Schmirander, Yunus
Chen, Guang
Aliverti, Andrea
Knoll, Alois
Ferrigno, Giancarlo
De Momi, Elena
author_sort Su, Hang
collection PubMed
description In robot control with physical interaction, like robot-assisted surgery and bilateral teleoperation, the availability of reliable interaction force information has proved to be capable of increasing the control precision and of dealing with the surrounding complex environments. Usually, force sensors are mounted between the end effector of the robot manipulator and the tool for measuring the interaction forces on the tooltip. In this case, the force acquired from the force sensor includes not only the interaction force but also the gravity force of the tool. Hence the tool dynamic identification is required for accurate dynamic simulation and model-based control. Although model-based techniques have already been widely used in traditional robotic arms control, their accuracy is limited due to the lack of specific dynamic models. This work proposes a model-free technique for dynamic identification using multi-layer neural networks (MNN). It utilizes two types of MNN architectures based on both feed-forward networks (FF-MNN) and cascade-forward networks (CF-MNN) to model the tool dynamics. Compared with the model-based technique, i.e., curve fitting (CF), the accuracy of the tool identification is improved. After the identification and calibration, a further demonstration of bilateral teleoperation is presented using a serial robot (LWR4+, KUKA, Germany) and a haptic manipulator (SIGMA 7, Force Dimension, Switzerland). Results demonstrate the promising performance of the model-free tool identification technique using MNN, improving the results provided by model-based methods.
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spelling pubmed-67492752019-09-27 Towards Model-Free Tool Dynamic Identification and Calibration Using Multi-Layer Neural Network Su, Hang Qi, Wen Hu, Yingbai Sandoval, Juan Zhang, Longbin Schmirander, Yunus Chen, Guang Aliverti, Andrea Knoll, Alois Ferrigno, Giancarlo De Momi, Elena Sensors (Basel) Article In robot control with physical interaction, like robot-assisted surgery and bilateral teleoperation, the availability of reliable interaction force information has proved to be capable of increasing the control precision and of dealing with the surrounding complex environments. Usually, force sensors are mounted between the end effector of the robot manipulator and the tool for measuring the interaction forces on the tooltip. In this case, the force acquired from the force sensor includes not only the interaction force but also the gravity force of the tool. Hence the tool dynamic identification is required for accurate dynamic simulation and model-based control. Although model-based techniques have already been widely used in traditional robotic arms control, their accuracy is limited due to the lack of specific dynamic models. This work proposes a model-free technique for dynamic identification using multi-layer neural networks (MNN). It utilizes two types of MNN architectures based on both feed-forward networks (FF-MNN) and cascade-forward networks (CF-MNN) to model the tool dynamics. Compared with the model-based technique, i.e., curve fitting (CF), the accuracy of the tool identification is improved. After the identification and calibration, a further demonstration of bilateral teleoperation is presented using a serial robot (LWR4+, KUKA, Germany) and a haptic manipulator (SIGMA 7, Force Dimension, Switzerland). Results demonstrate the promising performance of the model-free tool identification technique using MNN, improving the results provided by model-based methods. MDPI 2019-08-21 /pmc/articles/PMC6749275/ /pubmed/31438529 http://dx.doi.org/10.3390/s19173636 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Su, Hang
Qi, Wen
Hu, Yingbai
Sandoval, Juan
Zhang, Longbin
Schmirander, Yunus
Chen, Guang
Aliverti, Andrea
Knoll, Alois
Ferrigno, Giancarlo
De Momi, Elena
Towards Model-Free Tool Dynamic Identification and Calibration Using Multi-Layer Neural Network
title Towards Model-Free Tool Dynamic Identification and Calibration Using Multi-Layer Neural Network
title_full Towards Model-Free Tool Dynamic Identification and Calibration Using Multi-Layer Neural Network
title_fullStr Towards Model-Free Tool Dynamic Identification and Calibration Using Multi-Layer Neural Network
title_full_unstemmed Towards Model-Free Tool Dynamic Identification and Calibration Using Multi-Layer Neural Network
title_short Towards Model-Free Tool Dynamic Identification and Calibration Using Multi-Layer Neural Network
title_sort towards model-free tool dynamic identification and calibration using multi-layer neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6749275/
https://www.ncbi.nlm.nih.gov/pubmed/31438529
http://dx.doi.org/10.3390/s19173636
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