A Hierarchical Finite-State Machine-Based Task Allocation Framework for Human-Robot Collaborative Assembly Tasks

Ilias El Makrini; Mohsen Omidi; Fabio Fusaro; Edoardo Lamon; Arash Ajoudani; Bram Vanderborght

doi:10.1109/IROS47612.2022.9981618

A Hierarchical Finite-State Machine-Based Task Allocation Framework for Human-Robot Collaborative Assembly Tasks

Ilias El Makrini, Mohsen Omidi, Fabio Fusaro, Edoardo Lamon, Arash Ajoudani, Bram Vanderborght

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

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Abstract

Work-related musculoskeletal disorders (MSD) are one of the major cause of injuries and absenteeism at work. These lead to important cost in the manufacturing industry. Human-robot collaboration can help decreasing this issue by appropriately distributing the tasks and decreasing the workload of the factory worker. This paper proposes a novel generic task allocation approach based on hierarchical finite-state machines for human-robot assembly tasks. The developed framework decomposes first the main task into sub-tasks modelled as state machines. Based on capabilities considerations, workload, and performance estimations, the task allocator assigns the sub-task to human or robot agent. The algorithm was validated on the assembly of a crusher unit of a smoothie machine using the collaborative Franka Emika Panda robot and showed promising results in terms of productivity thanks to task parallelization, with improvement of more than 30% of the total assembly time with respect to a collaborative scenario, where the agents perform the tasks sequentially.

Original language	English
Title of host publication	2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)
Publisher	IEEE
Pages	10238-10244
Number of pages	7
Volume	2022 - December
ISBN (Electronic)	9781665479271
DOIs	https://doi.org/10.1109/IROS47612.2022.9981618
Publication status	Published - 26 Dec 2022
Event	2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Kyoto International Conference Center, Kyoto, Japan Duration: 23 Oct 2022 → 27 Oct 2022 https://iros2022.org/

Publication series

Name	IEEE International Conference on Intelligent Robots and Systems
Volume	2022-October
ISSN (Print)	2153-0858
ISSN (Electronic)	2153-0866

Conference

Conference	2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Country/Territory	Japan
City	Kyoto
Period	23/10/22 → 27/10/22
Internet address	https://iros2022.org/

Bibliographical note

Funding Information:
This work was supported by European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 871237 (SOPHIA) and Flanders Make through the programme ”Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen” of the Flemish Government. 1 Robotics and Multibody Mechanics Research Group, Vrije Uni-versiteit Brussel, Belgium, www.brubotics.eu. Corresponding author: Ilias.El.Makrini@vub.be 2 Flexible Assembly, Flanders Make, Belgium 3 HRI2 Lab, Istituto Italiano di Tecnologia, Via Morego 30, 16163, Genova, Italy 4 Interuniversity Microelectronics Centre (IMEC), Belgium 5 Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy

Publisher Copyright:
© 2022 IEEE.

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10.1109/IROS47612.2022.9981618

IROS2022_El_MakriniSubmitted manuscript, 2.91 MB

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El Makrini, I., Omidi, M., Fusaro, F., Lamon, E., Ajoudani, A., & Vanderborght, B. (2022). A Hierarchical Finite-State Machine-Based Task Allocation Framework for Human-Robot Collaborative Assembly Tasks. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022) (Vol. 2022 - December, pp. 10238-10244). [9981618] (IEEE International Conference on Intelligent Robots and Systems; Vol. 2022-October). IEEE. https://doi.org/10.1109/IROS47612.2022.9981618

El Makrini, Ilias ; Omidi, Mohsen ; Fusaro, Fabio ; Lamon, Edoardo ; Ajoudani, Arash ; Vanderborght, Bram. / A Hierarchical Finite-State Machine-Based Task Allocation Framework for Human-Robot Collaborative Assembly Tasks. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022). Vol. 2022 - December IEEE, 2022. pp. 10238-10244 (IEEE International Conference on Intelligent Robots and Systems).

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title = "A Hierarchical Finite-State Machine-Based Task Allocation Framework for Human-Robot Collaborative Assembly Tasks",

abstract = "Work-related musculoskeletal disorders (MSD) are one of the major cause of injuries and absenteeism at work. These lead to important cost in the manufacturing industry. Human-robot collaboration can help decreasing this issue by appropriately distributing the tasks and decreasing the workload of the factory worker. This paper proposes a novel generic task allocation approach based on hierarchical finite-state machines for human-robot assembly tasks. The developed framework decomposes first the main task into sub-tasks modelled as state machines. Based on capabilities considerations, workload, and performance estimations, the task allocator assigns the sub-task to human or robot agent. The algorithm was validated on the assembly of a crusher unit of a smoothie machine using the collaborative Franka Emika Panda robot and showed promising results in terms of productivity thanks to task parallelization, with improvement of more than 30% of the total assembly time with respect to a collaborative scenario, where the agents perform the tasks sequentially.",

author = "{El Makrini}, Ilias and Mohsen Omidi and Fabio Fusaro and Edoardo Lamon and Arash Ajoudani and Bram Vanderborght",

note = "Funding Information: This work was supported by European Union{\textquoteright}s Horizon 2020 research and innovation program under Grant Agreement No. 871237 (SOPHIA) and Flanders Make through the programme ”Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen” of the Flemish Government. 1 Robotics and Multibody Mechanics Research Group, Vrije Uni-versiteit Brussel, Belgium, www.brubotics.eu. Corresponding author: Ilias.El.Makrini@vub.be 2 Flexible Assembly, Flanders Make, Belgium 3 HRI2 Lab, Istituto Italiano di Tecnologia, Via Morego 30, 16163, Genova, Italy 4 Interuniversity Microelectronics Centre (IMEC), Belgium 5 Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ; Conference date: 23-10-2022 Through 27-10-2022",

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El Makrini, I , Omidi, M, Fusaro, F, Lamon, E, Ajoudani, A & Vanderborght, B 2022, A Hierarchical Finite-State Machine-Based Task Allocation Framework for Human-Robot Collaborative Assembly Tasks. in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022). vol. 2022 - December, 9981618, IEEE International Conference on Intelligent Robots and Systems, vol. 2022-October, IEEE, pp. 10238-10244, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 23/10/22. https://doi.org/10.1109/IROS47612.2022.9981618

A Hierarchical Finite-State Machine-Based Task Allocation Framework for Human-Robot Collaborative Assembly Tasks. / El Makrini, Ilias ; Omidi, Mohsen; Fusaro, Fabio; Lamon, Edoardo; Ajoudani, Arash; Vanderborght, Bram.

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022). Vol. 2022 - December IEEE, 2022. p. 10238-10244 9981618 (IEEE International Conference on Intelligent Robots and Systems; Vol. 2022-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

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AU - Lamon, Edoardo

AU - Ajoudani, Arash

AU - Vanderborght, Bram

N1 - Funding Information: This work was supported by European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 871237 (SOPHIA) and Flanders Make through the programme ”Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen” of the Flemish Government. 1 Robotics and Multibody Mechanics Research Group, Vrije Uni-versiteit Brussel, Belgium, www.brubotics.eu. Corresponding author: Ilias.El.Makrini@vub.be 2 Flexible Assembly, Flanders Make, Belgium 3 HRI2 Lab, Istituto Italiano di Tecnologia, Via Morego 30, 16163, Genova, Italy 4 Interuniversity Microelectronics Centre (IMEC), Belgium 5 Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy Publisher Copyright: © 2022 IEEE.

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N2 - Work-related musculoskeletal disorders (MSD) are one of the major cause of injuries and absenteeism at work. These lead to important cost in the manufacturing industry. Human-robot collaboration can help decreasing this issue by appropriately distributing the tasks and decreasing the workload of the factory worker. This paper proposes a novel generic task allocation approach based on hierarchical finite-state machines for human-robot assembly tasks. The developed framework decomposes first the main task into sub-tasks modelled as state machines. Based on capabilities considerations, workload, and performance estimations, the task allocator assigns the sub-task to human or robot agent. The algorithm was validated on the assembly of a crusher unit of a smoothie machine using the collaborative Franka Emika Panda robot and showed promising results in terms of productivity thanks to task parallelization, with improvement of more than 30% of the total assembly time with respect to a collaborative scenario, where the agents perform the tasks sequentially.

AB - Work-related musculoskeletal disorders (MSD) are one of the major cause of injuries and absenteeism at work. These lead to important cost in the manufacturing industry. Human-robot collaboration can help decreasing this issue by appropriately distributing the tasks and decreasing the workload of the factory worker. This paper proposes a novel generic task allocation approach based on hierarchical finite-state machines for human-robot assembly tasks. The developed framework decomposes first the main task into sub-tasks modelled as state machines. Based on capabilities considerations, workload, and performance estimations, the task allocator assigns the sub-task to human or robot agent. The algorithm was validated on the assembly of a crusher unit of a smoothie machine using the collaborative Franka Emika Panda robot and showed promising results in terms of productivity thanks to task parallelization, with improvement of more than 30% of the total assembly time with respect to a collaborative scenario, where the agents perform the tasks sequentially.

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M3 - Conference paper

VL - 2022 - December

T3 - IEEE International Conference on Intelligent Robots and Systems

SP - 10238

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El Makrini I , Omidi M, Fusaro F, Lamon E, Ajoudani A, Vanderborght B. A Hierarchical Finite-State Machine-Based Task Allocation Framework for Human-Robot Collaborative Assembly Tasks. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022). Vol. 2022 - December. IEEE. 2022. p. 10238-10244. 9981618. (IEEE International Conference on Intelligent Robots and Systems). https://doi.org/10.1109/IROS47612.2022.9981618

A Hierarchical Finite-State Machine-Based Task Allocation Framework for Human-Robot Collaborative Assembly Tasks

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