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 language | English (US) |
|---|---|
| Title of host publication | IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 5065-5075 |
| Number of pages | 11 |
| Volume | 2017-September |
| ISBN (Electronic) | 9781538626825 |
| DOIs | |
| State | Published - Dec 13 2017 |
| Event | 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 - Vancouver, Canada Duration: Sep 24 2017 → Sep 28 2017 |
Other
| Other | 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 |
|---|---|
| Country | Canada |
| City | Vancouver |
| Period | 9/24/17 → 9/28/17 |
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ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Computer Vision and Pattern Recognition
- Computer Science Applications
Cite this
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 proceeding › Conference contribution
}
TY - GEN
T1 - A hybrid BMI for control of robotic swarms
T2 - Preliminary results
AU - Karavas, George K.
AU - Larsson, Daniel T.
AU - Artemiadis, Panagiotis
PY - 2017/12/13
Y1 - 2017/12/13
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85041951439&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85041951439&partnerID=8YFLogxK
U2 - 10.1109/IROS.2017.8206390
DO - 10.1109/IROS.2017.8206390
M3 - Conference contribution
AN - SCOPUS:85041951439
VL - 2017-September
SP - 5065
EP - 5075
BT - IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
ER -