Development of a BCI-Driven Drone Control System using ROSneuro, Gazebo, and PX4: from simulation to real-world deployment

The integration of Brain-Computer Interface (BCI) technology with unmanned aerial vehicles (UAVs) offers promising advancements in various fields such as assistive technology, search and rescue, and remote operations. This thesis presents the development of a BCI-driven drone control system leveraging ROSneuro, Gazebo, and PX4. The project aims to enable users to pilot a drone through brain signals, initially in a simulated environment and subsequently in real-world scenarios.The methodology begins with the configuration and calibration of the BCI system using ROSneuro, ensuring accurate interpretation of brain signals for control commands. Gazebo, a versatile robotics simulator, is employed to create a realistic environment for testing and refining the control algorithms. Integration with the PX4 flight control stack facilitates the translation of these algorithms into actionable commands for the UAV.