A hybrid BMI for control of robotic swarms

Preliminary results

George K. Karavas, Daniel T. Larsson, Panagiotis Artemiadis

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

1 Citation (Scopus)

Abstract

Human Swarm Interaction (HSI) is a new field which relates to the effective control of robotic swarms by human operators. The iterature has shown that the control of swarms can become quite complicated. On the other hand, Brain Machine Interfaces (BMI) can offer intuitive control in a plethora of applications where other interfaces alone (e.g. joysticks) are inadequate or impractical, e.g. for people with motor disabilities. There are multiple types of BMI, but most of them rely on the analysis of ElectroEncephaloGraphic (EEG) signals. The authors have previously shown that swarm behaviors elicit specific brain activity on human subjects that observe them. Motivated by this result, in this work, we present preliminary results of a hybrid BMI that combines information from the brain and an external device. An algorithm for extracting information from the frequency domain of EEG signals that allows integration with the manual task of using a joystick is presented. The hybrid interface shows high accuracy and robustness when used as a brain-robot interface. Moreover, it allows for continuous control variables extracted from the EEG signals. Finally, its efficacy is proven across multiple subjects, while its performance is also demonstrated in the real-time control of a swarm of quadrotors.

Original languageEnglish (US)
Title of host publicationIROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5065-5075
Number of pages11
Volume2017-September
ISBN (Electronic)9781538626825
DOIs
StatePublished - Dec 13 2017
Event2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 - Vancouver, Canada
Duration: Sep 24 2017Sep 28 2017

Other

Other2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017
CountryCanada
CityVancouver
Period9/24/179/28/17

Fingerprint

Brain
Robotics
Real time control
Robots

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Cite this

Karavas, G. K., Larsson, D. T., & Artemiadis, P. (2017). A hybrid BMI for control of robotic swarms: Preliminary results. In IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems (Vol. 2017-September, pp. 5065-5075). [8206390] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS.2017.8206390

A hybrid BMI for control of robotic swarms : Preliminary results. / Karavas, George K.; Larsson, Daniel T.; Artemiadis, Panagiotis.

IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems. Vol. 2017-September Institute of Electrical and Electronics Engineers Inc., 2017. p. 5065-5075 8206390.

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

Karavas, GK, Larsson, DT & Artemiadis, P 2017, A hybrid BMI for control of robotic swarms: Preliminary results. in IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems. vol. 2017-September, 8206390, Institute of Electrical and Electronics Engineers Inc., pp. 5065-5075, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017, Vancouver, Canada, 9/24/17. https://doi.org/10.1109/IROS.2017.8206390
Karavas GK, Larsson DT, Artemiadis P. A hybrid BMI for control of robotic swarms: Preliminary results. In IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems. Vol. 2017-September. Institute of Electrical and Electronics Engineers Inc. 2017. p. 5065-5075. 8206390 https://doi.org/10.1109/IROS.2017.8206390
Karavas, George K. ; Larsson, Daniel T. ; Artemiadis, Panagiotis. / A hybrid BMI for control of robotic swarms : Preliminary results. IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems. Vol. 2017-September Institute of Electrical and Electronics Engineers Inc., 2017. pp. 5065-5075
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