Human–Robot Collaboration in Smart Manufacturing: Robot Reactive Behavior IntelligenceSource: Journal of Manufacturing Science and Engineering:;2020:;volume( 143 ):;issue: 003::page 031009-1DOI: 10.1115/1.4048950Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: To enable safe and effective human–robot collaboration (HRC) in smart manufacturing, seamless integration of sensing, cognition, and prediction into the robot controller is critical for real-time awareness, response, and communication inside a heterogeneous environment (robots, humans, and equipment). The specific research objective is to provide the robot Proactive Adaptive Collaboration Intelligence (PACI) and switching logic within its control architecture in order to give the robot the ability to optimally and dynamically adapt its motions, given a priori knowledge and predefined execution plans for its assigned tasks. The challenge lies in augmenting the robot’s decision-making process to have greater situation awareness and to yield smart robot behaviors/reactions when subject to different levels of human–robot interaction, while maintaining safety and production efficiency. Robot reactive behaviors were achieved via cost function-based switching logic activating the best suited high-level controller. The PACI’s underlying segmentation and switching logic framework is demonstrated to yield a high degree of modularity and flexibility. The performance of the developed control structure subjected to different levels of human–robot interactions was validated in a simulated environment. Open-loop commands were sent to the physical e.DO robot to demonstrate how the proposed framework would behave in a real application.
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contributor author | Nicora, Matteo Lavit | |
contributor author | Ambrosetti, Roberto | |
contributor author | Wiens, Gloria J. | |
contributor author | Fassi, Irene | |
date accessioned | 2022-02-05T21:41:32Z | |
date available | 2022-02-05T21:41:32Z | |
date copyright | 12/16/2020 12:00:00 AM | |
date issued | 2020 | |
identifier issn | 1087-1357 | |
identifier other | manu_143_3_031009.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4276148 | |
description abstract | To enable safe and effective human–robot collaboration (HRC) in smart manufacturing, seamless integration of sensing, cognition, and prediction into the robot controller is critical for real-time awareness, response, and communication inside a heterogeneous environment (robots, humans, and equipment). The specific research objective is to provide the robot Proactive Adaptive Collaboration Intelligence (PACI) and switching logic within its control architecture in order to give the robot the ability to optimally and dynamically adapt its motions, given a priori knowledge and predefined execution plans for its assigned tasks. The challenge lies in augmenting the robot’s decision-making process to have greater situation awareness and to yield smart robot behaviors/reactions when subject to different levels of human–robot interaction, while maintaining safety and production efficiency. Robot reactive behaviors were achieved via cost function-based switching logic activating the best suited high-level controller. The PACI’s underlying segmentation and switching logic framework is demonstrated to yield a high degree of modularity and flexibility. The performance of the developed control structure subjected to different levels of human–robot interactions was validated in a simulated environment. Open-loop commands were sent to the physical e.DO robot to demonstrate how the proposed framework would behave in a real application. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Human–Robot Collaboration in Smart Manufacturing: Robot Reactive Behavior Intelligence | |
type | Journal Paper | |
journal volume | 143 | |
journal issue | 3 | |
journal title | Journal of Manufacturing Science and Engineering | |
identifier doi | 10.1115/1.4048950 | |
journal fristpage | 031009-1 | |
journal lastpage | 031009-9 | |
page | 9 | |
tree | Journal of Manufacturing Science and Engineering:;2020:;volume( 143 ):;issue: 003 | |
contenttype | Fulltext |