Remote-Oriented Brain-Controlled Unmanned Aerial Vehicle for IoT

With the rapid development of the Internet of Things (IoT) systems, the application potential of remote-oriented unmanned aerial vehicle (UAV) in the IoT systems is becoming increasingly prominent. Brain-computer interface (BCI)-based remote-oriented UAV systems can not only leverage the natural advantages of the human brain in cognition and response but also contribute to safer and more efficient operations in certain special environments. However, the remote-oriented BCI systems still face challenges in spatial perception and control capabilities. In this study, a compressed-perceptual visual evoked potentials (CPVEPs) paradigm and a human-machine closed-loop (HMCL) controller are proposed for a remote-oriented brain-controlled UAV (BCUAV). A BCVAV system for remote application scenarios is constructed based on the CPVEP paradigm and the HMCL controller. Online experiments demonstrates that all the subjects have completed the navigation task by the proposed remote-oriented BCUAV system. Human-in-the-loop experiments show that the proposed system can significantly improve the system performance and adaptability of BCUAV to different environments, while significantly reducing the user's workload. In the future, the proposed remote-oriented BCUAV system can be applied to various scenarios, such as remote-controlled search and rescue, traffic monitoring, and power line inspection.