Incremental learning of spatial-temporal features in human motion patterns with mixture model for planning motion of a collaborative robot in assembly lines

Akira Kanazawa, Jun Kinugawa, Kazuhiro Kosuge

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Collaborative robots are expected to work in cooperation with humans to improve productivity and maintain the quality of products. In the previous study, we have proposed an incremental learning system for adaptively scheduling a motion of the collaborative robot based on a worker's behavior. Although this system could model the worker's motion pattern precisely and robustly without collecting the worker's data in advance, it required two different models for modeling the worker's spatial and temporal features respectively and was not well considered for generalization. In this paper, we extend the previous incremental learning system by integrating the spatial and temporal models using a mixture model. In addition, we install a new incremental learning algorithm which improves a generalization capability of the mixture model and avoids overfitting in the situation where the prior information is limited. Implementing the proposed algorithm, we evaluate the effectiveness of the proposed system by experiments for several workers and for several assembly processes.

Original languageEnglish
Title of host publication2019 International Conference on Robotics and Automation, ICRA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7858-7864
Number of pages7
ISBN (Electronic)9781538660263
DOIs
Publication statusPublished - 2019 May 1
Event2019 International Conference on Robotics and Automation, ICRA 2019 - Montreal, Canada
Duration: 2019 May 202019 May 24

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2019-May
ISSN (Print)1050-4729

Conference

Conference2019 International Conference on Robotics and Automation, ICRA 2019
CountryCanada
CityMontreal
Period19/5/2019/5/24

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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    Kanazawa, A., Kinugawa, J., & Kosuge, K. (2019). Incremental learning of spatial-temporal features in human motion patterns with mixture model for planning motion of a collaborative robot in assembly lines. In 2019 International Conference on Robotics and Automation, ICRA 2019 (pp. 7858-7864). [8794227] (Proceedings - IEEE International Conference on Robotics and Automation; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2019.8794227