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 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Computer Vision and Pattern Recognition
- Computer Science Applications