Robust assembly sequence generation in a Human-Robot Collaborative workcell by reinforcement learning

Human-Robot Collaborative (HRC) workcells could enhance the inclusive employment of human workers regardless their force or skills. Collaborative robots not only substitute humans in dangerous and heavy tasks, but also make the related processes within the reach of all workers, overcoming lack of sk...

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Autores principales:	Antonelli Dario, Zeng Qingfei, Aliev Khurshid, Liu Xuemei
Formato:	article
Lenguaje:	EN
Publicado:	University of Belgrade - Faculty of Mechanical Engineering, Belgrade 2021
Materias:	reinforcement learning machine learning task-based robot programming human-robot collaboration markov decision process assembly Engineering (General). Civil engineering (General) TA1-2040 Mechanics of engineering. Applied mechanics TA349-359
Acceso en línea:	https://doaj.org/article/727ac0a9feb54581983f467e81f9ce70
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spelling	oai:doaj.org-article:727ac0a9feb54581983f467e81f9ce702021-12-05T21:01:52ZRobust assembly sequence generation in a Human-Robot Collaborative workcell by reinforcement learning1451-20922406-128X10.5937/fme2104851Ahttps://doaj.org/article/727ac0a9feb54581983f467e81f9ce702021-01-01T00:00:00Zhttps://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2021/1451-20922104851A.pdfhttps://doaj.org/toc/1451-2092https://doaj.org/toc/2406-128XHuman-Robot Collaborative (HRC) workcells could enhance the inclusive employment of human workers regardless their force or skills. Collaborative robots not only substitute humans in dangerous and heavy tasks, but also make the related processes within the reach of all workers, overcoming lack of skills and physical limitations. To enable the full exploitation of collaborative robots traditional robot programming must be overcome. Reduction of robot programming time and worker cognitive effort during the job become compelling requirements to be satisfied. Reinforcement learning (RL) plays a core role to allow robot to adapt to a changing and unstructured environment and to human undependable execution of repetitive tasks. The paper focuses on the utilization of RL to allow a robust industrial assembly process in a HRC workcell. The result of the study is a method for the online generation of robot assembly task sequence that adapts to the unpredictable and inconstant behavior of the human co-workers. The method is presented with the help of a benchmark case study.Antonelli DarioZeng QingfeiAliev KhurshidLiu XuemeiUniversity of Belgrade - Faculty of Mechanical Engineering, Belgradearticlereinforcement learningmachine learningtask-based robot programminghuman-robot collaborationmarkov decision processassemblyEngineering (General). Civil engineering (General)TA1-2040Mechanics of engineering. Applied mechanicsTA349-359ENFME Transactions, Vol 49, Iss 4, Pp 851-858 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	reinforcement learning machine learning task-based robot programming human-robot collaboration markov decision process assembly Engineering (General). Civil engineering (General) TA1-2040 Mechanics of engineering. Applied mechanics TA349-359
spellingShingle	reinforcement learning machine learning task-based robot programming human-robot collaboration markov decision process assembly Engineering (General). Civil engineering (General) TA1-2040 Mechanics of engineering. Applied mechanics TA349-359 Antonelli Dario Zeng Qingfei Aliev Khurshid Liu Xuemei Robust assembly sequence generation in a Human-Robot Collaborative workcell by reinforcement learning
description	Human-Robot Collaborative (HRC) workcells could enhance the inclusive employment of human workers regardless their force or skills. Collaborative robots not only substitute humans in dangerous and heavy tasks, but also make the related processes within the reach of all workers, overcoming lack of skills and physical limitations. To enable the full exploitation of collaborative robots traditional robot programming must be overcome. Reduction of robot programming time and worker cognitive effort during the job become compelling requirements to be satisfied. Reinforcement learning (RL) plays a core role to allow robot to adapt to a changing and unstructured environment and to human undependable execution of repetitive tasks. The paper focuses on the utilization of RL to allow a robust industrial assembly process in a HRC workcell. The result of the study is a method for the online generation of robot assembly task sequence that adapts to the unpredictable and inconstant behavior of the human co-workers. The method is presented with the help of a benchmark case study.
format	article
author	Antonelli Dario Zeng Qingfei Aliev Khurshid Liu Xuemei
author_facet	Antonelli Dario Zeng Qingfei Aliev Khurshid Liu Xuemei
author_sort	Antonelli Dario
title	Robust assembly sequence generation in a Human-Robot Collaborative workcell by reinforcement learning
title_short	Robust assembly sequence generation in a Human-Robot Collaborative workcell by reinforcement learning
title_full	Robust assembly sequence generation in a Human-Robot Collaborative workcell by reinforcement learning
title_fullStr	Robust assembly sequence generation in a Human-Robot Collaborative workcell by reinforcement learning
title_full_unstemmed	Robust assembly sequence generation in a Human-Robot Collaborative workcell by reinforcement learning
title_sort	robust assembly sequence generation in a human-robot collaborative workcell by reinforcement learning
publisher	University of Belgrade - Faculty of Mechanical Engineering, Belgrade
publishDate	2021
url	https://doaj.org/article/727ac0a9feb54581983f467e81f9ce70
work_keys_str_mv	AT antonellidario robustassemblysequencegenerationinahumanrobotcollaborativeworkcellbyreinforcementlearning AT zengqingfei robustassemblysequencegenerationinahumanrobotcollaborativeworkcellbyreinforcementlearning AT alievkhurshid robustassemblysequencegenerationinahumanrobotcollaborativeworkcellbyreinforcementlearning AT liuxuemei robustassemblysequencegenerationinahumanrobotcollaborativeworkcellbyreinforcementlearning
_version_	1718371007661604864

Robust assembly sequence generation in a Human-Robot Collaborative workcell by reinforcement learning

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