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Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges, and Opportunities

The objective of this paper is to present a systematic review of existing sensor-based control methodologies for applications that involve direct interaction between humans and robots, in the form of either physical collaboration or safe coexistence. To this end, we first introduce the basic formula...

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Detalles Bibliográficos
Autores principales: Cherubini, Andrea, Navarro-Alarcon, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7817623/
https://www.ncbi.nlm.nih.gov/pubmed/33488375
http://dx.doi.org/10.3389/fnbot.2020.576846
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author Cherubini, Andrea
Navarro-Alarcon, David
author_facet Cherubini, Andrea
Navarro-Alarcon, David
author_sort Cherubini, Andrea
collection PubMed
description The objective of this paper is to present a systematic review of existing sensor-based control methodologies for applications that involve direct interaction between humans and robots, in the form of either physical collaboration or safe coexistence. To this end, we first introduce the basic formulation of the sensor-servo problem, and then, present its most common approaches: vision-based, touch-based, audio-based, and distance-based control. Afterwards, we discuss and formalize the methods that integrate heterogeneous sensors at the control level. The surveyed body of literature is classified according to various factors such as: sensor type, sensor integration method, and application domain. Finally, we discuss open problems, potential applications, and future research directions.
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spelling pubmed-78176232021-01-22 Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges, and Opportunities Cherubini, Andrea Navarro-Alarcon, David Front Neurorobot Neuroscience The objective of this paper is to present a systematic review of existing sensor-based control methodologies for applications that involve direct interaction between humans and robots, in the form of either physical collaboration or safe coexistence. To this end, we first introduce the basic formulation of the sensor-servo problem, and then, present its most common approaches: vision-based, touch-based, audio-based, and distance-based control. Afterwards, we discuss and formalize the methods that integrate heterogeneous sensors at the control level. The surveyed body of literature is classified according to various factors such as: sensor type, sensor integration method, and application domain. Finally, we discuss open problems, potential applications, and future research directions. Frontiers Media S.A. 2021-01-07 /pmc/articles/PMC7817623/ /pubmed/33488375 http://dx.doi.org/10.3389/fnbot.2020.576846 Text en Copyright © 2021 Cherubini and Navarro-Alarcon. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Cherubini, Andrea
Navarro-Alarcon, David
Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges, and Opportunities
title Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges, and Opportunities
title_full Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges, and Opportunities
title_fullStr Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges, and Opportunities
title_full_unstemmed Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges, and Opportunities
title_short Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges, and Opportunities
title_sort sensor-based control for collaborative robots: fundamentals, challenges, and opportunities
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7817623/
https://www.ncbi.nlm.nih.gov/pubmed/33488375
http://dx.doi.org/10.3389/fnbot.2020.576846
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