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A machine learning based energy efficient trajectory planning approach for industrial robots
School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China.
KTH, School of Industrial Engineering and Management (ITM), Production Engineering. School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China.ORCID iD: 0000-0002-9642-6983
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.ORCID iD: 0000-0001-8679-8049
2019 (English)In: Procedia CIRP, Elsevier B.V. , 2019, p. 429-434Conference paper, Published paper (Refereed)
Abstract [en]

Towards an energy efficient trajectory planning of industrial robot (IR), this paper proposes a machine learning based approach. Within the context, the IR’s movements are digitalised in joint space first, which allows using data attributes to represent IR’s trajectories. Moreover, a set of designed trajectories which can address IRs workspace are followed by the IR, and meanwhile, the energy consumption is measured. Then data sets are generated by combining the trajectory data and measured energy consumption data, and they are used to train a machine learning model. On top of that, the trained model provides a fitness function to evolution based or swarm-intelligence based algorithms to obtain a near-optimal or optimal trajectory. Finally, a simplified case study is demonstrated to validate the proposed method. The method provides a direct connection between joint control and energy efficiency objective, by which the solution space can be obviously relaxed, compared to the existing methods.

Place, publisher, year, edition, pages
Elsevier B.V. , 2019. p. 429-434
Keywords [en]
Energy efficiency, Industrial robot, Machine learning, Trajectory planning
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-262479DOI: 10.1016/j.procir.2019.03.074Scopus ID: 2-s2.0-85068434469OAI: oai:DiVA.org:kth-262479DiVA, id: diva2:1361417
Conference
52nd CIRP Conference on Manufacturing Systems, CMS 2019, 12 June 2019 through 14 June 2019
Note

QC 20191016

Available from: 2019-10-16 Created: 2019-10-16 Last updated: 2019-10-16Bibliographically approved

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Ji, WeiWang, Lihui

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf