With the rapid evolution of modern military warfare,the remote brain-controlled unmanned aerial vehicle ( UAV ) systems are playing an increasingly important role in battlefield information gathering,target surveillance,and tactical deployment. This research proposes a compressed sensing control paradigm and a human-machine closed-loop control algorithm for remote brain-controlled UAV. Based on this control paradigm and algorithm,a remote brain-controlled UAV system for military applications is constructed. Online experiments conducted in this study demonstrate that eight participants successfully completed the navigation tasks using the brain-controlled UAV system based on the compressed sensing control paradigm and human-machine closed-loop control algorithm. The average task completion rate of the proposed brain-controlled UAV system is 0.95,and its average task completion time is 100.46 s,which significantly outperformes the brain-controlled UAV system based on human-machine open-loop control algorithms. In the future,the proposed brain-controlled UAV system can be used for battlefield reconnaissance in military scenarios,significantly enhancing the remote-control capabilities of military personnel and expanding their battlefield awareness.
维普
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