This chapter proposes a drone control method based on brain-computer interface (BCI) to determine the flight path in a predator-prey situation to seek enemy drones and track them. We have conducted two experiments both with a common BCI-based control: the first one consists of virtual simulation platform with advanced environment allowing multiple drone control scenarios. The second one consists of an experimental testbed using real drones moving in an indoor environment. In both experimental settings, we use Electroencephalography recording system with eight dry electrodes, where the able-bodied subjects are instructed to control the drones in real-time using P300 paradigm and EEG Unicorn Hybrid Black system. For the drone simulation experiment, the participants are instructed to sit in front of a computer screen and control the predator-drone so as to follow its prey by navigating through an indoor multiroom environment. In the experimental testbed, the subjects are asked to control two different drones, where one drone plays the role of a prey that is continuously trying to escape its predator, and the second one as a predator chasing its target. In this setup, the predator-drone is BCI-controlled, while the prey-drone is controlled via a joystick or preprogrammed code. The results of the conducted experiments show that BCI subjects were able to accurately generate near-optimal trajectories by reacting quickly to the target's movements.
- Brain-computer interface
- Drone predator-prey interaction
- P300-based bci
ASJC Scopus subject areas
- Computer Science(all)