A Scalable and Unified Multi-Control Framework for KUKA LBR iiwa Collaborative Robots

Antonio Serrano-Munoz; Inigo Elguea-Aguinaco; Dimitrios Chrysostomou; Simon Bogh; Nestor Arana-Arexolaleiba

doi:10.1109/SII55687.2023.10039308

A Scalable and Unified Multi-Control Framework for KUKA LBR iiwa Collaborative Robots

Antonio Serrano-Munoz, Inigo Elguea-Aguinaco, Dimitrios Chrysostomou, Simon Bogh, Nestor Arana-Arexolaleiba

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

1 Citation (Scopus)

Abstract

The trend towards industrialization and digitalization has led more and more companies to deploy robots in their manufacturing facilities. In the field of collaborative robotics, the KUKA LBR iiwa is one of the benchmark robots. To communicate these robots with different components and generate an interoperability infrastructure, the software libraries provided by Robot Operating System are now widely widespread. However, the latency that such communication between devices often generates, diminishes the potential of machine learning control techniques, such as reinforcement learning, when the robot must react swiftly in an unstructured environment. This paper presents a scalable and unified control system that supports both Robot Operating System and direct control and outperforms current control frameworks in terms of exploiting the functionalities of the KUKA LBR iiwa. The framework's documentation can be found at https://libiiwa.readthedocs.io and its source code is available on GitHub at https://github.com/Toni-SM/libiiwa.

Original language	English
Title of host publication	2023 IEEE/SICE International Symposium on System Integration, SII 2023
Publisher	IEEE
Publication date	15 Feb 2023
ISBN (Electronic)	9798350398687
DOIs	https://doi.org/10.1109/SII55687.2023.10039308
Publication status	Published - 15 Feb 2023
Event	2023 IEEE/SICE International Symposium on System Integration, SII 2023 - Atlanta, United States Duration: 17 Jan 2023 → 20 Jan 2023

Conference

Conference	2023 IEEE/SICE International Symposium on System Integration, SII 2023
Country/Territory	United States
City	Atlanta
Period	17/01/2023 → 20/01/2023

Series	2023 IEEE/SICE International Symposium on System Integration, SII 2023

Bibliographical note

Funding Information:
This study was partially financed by European Union’s SMART EUREKA programme under grant agreement S0218-chARmER, Innovation Fund Denmark (Grant no. 9118-00001B), and by H2020-WIDESPREAD project no. 857061 “Networking for Research and Development of Human Interactive and Sensitive Robotics Taking Advantage of Additive Manufacturing – R2P2” 1Antonio Serrano-Muñoz is with the Faculty of Engineering, Mondragon Unibertsitatea, Arrasate, Spain aserrano@mondragon.edu 2´ñigo Elguea-Aguinaco is with the Faculty of Engineering, Mon-dragon Unibertsitatea, Arrasate, Spain ielguea@aldakin.com, inigo.elguea@alumni.mondragon.edu 3Dimitris Chrysostomou is with the Faculty of Engineering and Science, Aalborg University, Aalborg, Denmark dimi@mp.aau.dk 4Simon Bøgh is with the Faculty of Engineering and Science, Aalborg University, Aalborg, Denmark sb@mp.aau.dk 5Nestor Arana-Arexolaleiba is with the Faculty of Engineering, Mon-dragon Unibertsitatea, Arrasate, Spain narana@mondragon.edu

Publisher Copyright:
© 2023 IEEE.

Keywords

Collaborative Robot
kuka iiwa
Reinforcement Learning (RL)
Software libraries
Service robots
Operating systems
Software

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/SII55687.2023.10039308

AUB Link

Search for the material in Aalborg University Library's search engine

Cite this

Serrano-Munoz, A., Elguea-Aguinaco, I., Chrysostomou, D., Bogh, S., & Arana-Arexolaleiba, N. (2023). A Scalable and Unified Multi-Control Framework for KUKA LBR iiwa Collaborative Robots. In 2023 IEEE/SICE International Symposium on System Integration, SII 2023 IEEE. 2023 IEEE/SICE International Symposium on System Integration, SII 2023 https://doi.org/10.1109/SII55687.2023.10039308

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title = "A Scalable and Unified Multi-Control Framework for KUKA LBR iiwa Collaborative Robots",

abstract = "The trend towards industrialization and digitalization has led more and more companies to deploy robots in their manufacturing facilities. In the field of collaborative robotics, the KUKA LBR iiwa is one of the benchmark robots. To communicate these robots with different components and generate an interoperability infrastructure, the software libraries provided by Robot Operating System are now widely widespread. However, the latency that such communication between devices often generates, diminishes the potential of machine learning control techniques, such as reinforcement learning, when the robot must react swiftly in an unstructured environment. This paper presents a scalable and unified control system that supports both Robot Operating System and direct control and outperforms current control frameworks in terms of exploiting the functionalities of the KUKA LBR iiwa. The framework's documentation can be found at https://libiiwa.readthedocs.io and its source code is available on GitHub at https://github.com/Toni-SM/libiiwa.",

keywords = "Collaborative Robot, kuka iiwa, Reinforcement Learning (RL), Software libraries, Service robots, Operating systems, Software",

author = "Antonio Serrano-Munoz and Inigo Elguea-Aguinaco and Dimitrios Chrysostomou and Simon Bogh and Nestor Arana-Arexolaleiba",

note = "Funding Information: This study was partially financed by European Union{\textquoteright}s SMART EUREKA programme under grant agreement S0218-chARmER, Innovation Fund Denmark (Grant no. 9118-00001B), and by H2020-WIDESPREAD project no. 857061 “Networking for Research and Development of Human Interactive and Sensitive Robotics Taking Advantage of Additive Manufacturing – R2P2” 1Antonio Serrano-Mu{\~n}oz is with the Faculty of Engineering, Mondragon Unibertsitatea, Arrasate, Spain aserrano@mondragon.edu 2´{\~n}igo Elguea-Aguinaco is with the Faculty of Engineering, Mon-dragon Unibertsitatea, Arrasate, Spain ielguea@aldakin.com, inigo.elguea@alumni.mondragon.edu 3Dimitris Chrysostomou is with the Faculty of Engineering and Science, Aalborg University, Aalborg, Denmark dimi@mp.aau.dk 4Simon B{\o}gh is with the Faculty of Engineering and Science, Aalborg University, Aalborg, Denmark sb@mp.aau.dk 5Nestor Arana-Arexolaleiba is with the Faculty of Engineering, Mon-dragon Unibertsitatea, Arrasate, Spain narana@mondragon.edu Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE/SICE International Symposium on System Integration, SII 2023 ; Conference date: 17-01-2023 Through 20-01-2023",

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Serrano-Munoz, A, Elguea-Aguinaco, I, Chrysostomou, D , Bogh, S & Arana-Arexolaleiba, N 2023, A Scalable and Unified Multi-Control Framework for KUKA LBR iiwa Collaborative Robots. in 2023 IEEE/SICE International Symposium on System Integration, SII 2023. IEEE, 2023 IEEE/SICE International Symposium on System Integration, SII 2023, 2023 IEEE/SICE International Symposium on System Integration, SII 2023, Atlanta, United States, 17/01/2023. https://doi.org/10.1109/SII55687.2023.10039308

A Scalable and Unified Multi-Control Framework for KUKA LBR iiwa Collaborative Robots. / Serrano-Munoz, Antonio; Elguea-Aguinaco, Inigo; Chrysostomou, Dimitrios et al.
2023 IEEE/SICE International Symposium on System Integration, SII 2023. IEEE, 2023. (2023 IEEE/SICE International Symposium on System Integration, SII 2023).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

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AU - Chrysostomou, Dimitrios

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N1 - Funding Information: This study was partially financed by European Union’s SMART EUREKA programme under grant agreement S0218-chARmER, Innovation Fund Denmark (Grant no. 9118-00001B), and by H2020-WIDESPREAD project no. 857061 “Networking for Research and Development of Human Interactive and Sensitive Robotics Taking Advantage of Additive Manufacturing – R2P2” 1Antonio Serrano-Muñoz is with the Faculty of Engineering, Mondragon Unibertsitatea, Arrasate, Spain aserrano@mondragon.edu 2´ñigo Elguea-Aguinaco is with the Faculty of Engineering, Mon-dragon Unibertsitatea, Arrasate, Spain ielguea@aldakin.com, inigo.elguea@alumni.mondragon.edu 3Dimitris Chrysostomou is with the Faculty of Engineering and Science, Aalborg University, Aalborg, Denmark dimi@mp.aau.dk 4Simon Bøgh is with the Faculty of Engineering and Science, Aalborg University, Aalborg, Denmark sb@mp.aau.dk 5Nestor Arana-Arexolaleiba is with the Faculty of Engineering, Mon-dragon Unibertsitatea, Arrasate, Spain narana@mondragon.edu Publisher Copyright: © 2023 IEEE.

PY - 2023/2/15

Y1 - 2023/2/15

N2 - The trend towards industrialization and digitalization has led more and more companies to deploy robots in their manufacturing facilities. In the field of collaborative robotics, the KUKA LBR iiwa is one of the benchmark robots. To communicate these robots with different components and generate an interoperability infrastructure, the software libraries provided by Robot Operating System are now widely widespread. However, the latency that such communication between devices often generates, diminishes the potential of machine learning control techniques, such as reinforcement learning, when the robot must react swiftly in an unstructured environment. This paper presents a scalable and unified control system that supports both Robot Operating System and direct control and outperforms current control frameworks in terms of exploiting the functionalities of the KUKA LBR iiwa. The framework's documentation can be found at https://libiiwa.readthedocs.io and its source code is available on GitHub at https://github.com/Toni-SM/libiiwa.

AB - The trend towards industrialization and digitalization has led more and more companies to deploy robots in their manufacturing facilities. In the field of collaborative robotics, the KUKA LBR iiwa is one of the benchmark robots. To communicate these robots with different components and generate an interoperability infrastructure, the software libraries provided by Robot Operating System are now widely widespread. However, the latency that such communication between devices often generates, diminishes the potential of machine learning control techniques, such as reinforcement learning, when the robot must react swiftly in an unstructured environment. This paper presents a scalable and unified control system that supports both Robot Operating System and direct control and outperforms current control frameworks in terms of exploiting the functionalities of the KUKA LBR iiwa. The framework's documentation can be found at https://libiiwa.readthedocs.io and its source code is available on GitHub at https://github.com/Toni-SM/libiiwa.

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ER -

A Scalable and Unified Multi-Control Framework for KUKA LBR iiwa Collaborative Robots

Abstract

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

AUB Link

Other files and links

Fingerprint

chARmER: Assistive Robotic Disassembly System for Recycling

R2P2: Networking for Research and Development of Human Interactive and Sensitive Robotics taking advantage of Additive Manufacturing

Cite this

A Scalable and Unified Multi-Control Framework for KUKA LBR iiwa Collaborative Robots

Abstract

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

AUB Link

Other files and links

Fingerprint

Projects

chARmER: Assistive Robotic Disassembly System for Recycling

R2P2: Networking for Research and Development of Human Interactive and Sensitive Robotics taking advantage of Additive Manufacturing

Cite this