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Smart Sensing and Adaptive Reasoning for Enabling Industrial Robots with Interactive Human-Robot Capabilities in Dynamic Environments—A Case Study

Traditional industry is seeing an increasing demand for more autonomous and flexible manufacturing in unstructured settings, a shift away from the fixed, isolated workspaces where robots perform predefined actions repetitively. This work presents a case study in which a robotic manipulator, namely a...

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Autores principales: Zabalza, Jaime, Fei, Zixiang, Wong, Cuebong, Yan, Yijun, Mineo, Carmelo, Yang, Erfu, Rodden, Tony, Mehnen, Jorn, Pham, Quang-Cuong, Ren, Jinchang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6472064/
https://www.ncbi.nlm.nih.gov/pubmed/30889902
http://dx.doi.org/10.3390/s19061354
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author Zabalza, Jaime
Fei, Zixiang
Wong, Cuebong
Yan, Yijun
Mineo, Carmelo
Yang, Erfu
Rodden, Tony
Mehnen, Jorn
Pham, Quang-Cuong
Ren, Jinchang
author_facet Zabalza, Jaime
Fei, Zixiang
Wong, Cuebong
Yan, Yijun
Mineo, Carmelo
Yang, Erfu
Rodden, Tony
Mehnen, Jorn
Pham, Quang-Cuong
Ren, Jinchang
author_sort Zabalza, Jaime
collection PubMed
description Traditional industry is seeing an increasing demand for more autonomous and flexible manufacturing in unstructured settings, a shift away from the fixed, isolated workspaces where robots perform predefined actions repetitively. This work presents a case study in which a robotic manipulator, namely a KUKA KR90 R3100, is provided with smart sensing capabilities such as vision and adaptive reasoning for real-time collision avoidance and online path planning in dynamically-changing environments. A machine vision module based on low-cost cameras and color detection in the hue, saturation, value (HSV) space is developed to make the robot aware of its changing environment. Therefore, this vision allows the detection and localization of a randomly moving obstacle. Path correction to avoid collision avoidance for such obstacles with robotic manipulator is achieved by exploiting an adaptive path planning module along with a dedicated robot control module, where the three modules run simultaneously. These sensing/smart capabilities allow the smooth interactions between the robot and its dynamic environment, where the robot needs to react to dynamic changes through autonomous thinking and reasoning with the reaction times below the average human reaction time. The experimental results demonstrate that effective human-robot and robot-robot interactions can be realized through the innovative integration of emerging sensing techniques, efficient planning algorithms and systematic designs.
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spelling pubmed-64720642019-04-26 Smart Sensing and Adaptive Reasoning for Enabling Industrial Robots with Interactive Human-Robot Capabilities in Dynamic Environments—A Case Study Zabalza, Jaime Fei, Zixiang Wong, Cuebong Yan, Yijun Mineo, Carmelo Yang, Erfu Rodden, Tony Mehnen, Jorn Pham, Quang-Cuong Ren, Jinchang Sensors (Basel) Article Traditional industry is seeing an increasing demand for more autonomous and flexible manufacturing in unstructured settings, a shift away from the fixed, isolated workspaces where robots perform predefined actions repetitively. This work presents a case study in which a robotic manipulator, namely a KUKA KR90 R3100, is provided with smart sensing capabilities such as vision and adaptive reasoning for real-time collision avoidance and online path planning in dynamically-changing environments. A machine vision module based on low-cost cameras and color detection in the hue, saturation, value (HSV) space is developed to make the robot aware of its changing environment. Therefore, this vision allows the detection and localization of a randomly moving obstacle. Path correction to avoid collision avoidance for such obstacles with robotic manipulator is achieved by exploiting an adaptive path planning module along with a dedicated robot control module, where the three modules run simultaneously. These sensing/smart capabilities allow the smooth interactions between the robot and its dynamic environment, where the robot needs to react to dynamic changes through autonomous thinking and reasoning with the reaction times below the average human reaction time. The experimental results demonstrate that effective human-robot and robot-robot interactions can be realized through the innovative integration of emerging sensing techniques, efficient planning algorithms and systematic designs. MDPI 2019-03-18 /pmc/articles/PMC6472064/ /pubmed/30889902 http://dx.doi.org/10.3390/s19061354 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
Zabalza, Jaime
Fei, Zixiang
Wong, Cuebong
Yan, Yijun
Mineo, Carmelo
Yang, Erfu
Rodden, Tony
Mehnen, Jorn
Pham, Quang-Cuong
Ren, Jinchang
Smart Sensing and Adaptive Reasoning for Enabling Industrial Robots with Interactive Human-Robot Capabilities in Dynamic Environments—A Case Study
title Smart Sensing and Adaptive Reasoning for Enabling Industrial Robots with Interactive Human-Robot Capabilities in Dynamic Environments—A Case Study
title_full Smart Sensing and Adaptive Reasoning for Enabling Industrial Robots with Interactive Human-Robot Capabilities in Dynamic Environments—A Case Study
title_fullStr Smart Sensing and Adaptive Reasoning for Enabling Industrial Robots with Interactive Human-Robot Capabilities in Dynamic Environments—A Case Study
title_full_unstemmed Smart Sensing and Adaptive Reasoning for Enabling Industrial Robots with Interactive Human-Robot Capabilities in Dynamic Environments—A Case Study
title_short Smart Sensing and Adaptive Reasoning for Enabling Industrial Robots with Interactive Human-Robot Capabilities in Dynamic Environments—A Case Study
title_sort smart sensing and adaptive reasoning for enabling industrial robots with interactive human-robot capabilities in dynamic environments—a case study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6472064/
https://www.ncbi.nlm.nih.gov/pubmed/30889902
http://dx.doi.org/10.3390/s19061354
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