  As technological advancements continue to expand the capabilities of multi
unmanned-aerial-vehicle systems (mUAV), human operators face challenges in
scalability and efficiency due to the complex cognitive load and operations
associated with motion adjustments and team coordination. Such cognitive
demands limit the feasible size of mUAV teams and necessitate extensive
operator training, impeding broader adoption. This paper developed a Hand
Gesture Based Interactive Control (HGIC), a novel interface system that utilize
computer vision techniques to intuitively translate hand gestures into modular
commands for robot teaming. Through learning control models, these commands
enable efficient and scalable mUAV motion control and adjustments. HGIC
eliminates the need for specialized hardware and offers two key benefits: 1)
Minimal training requirements through natural gestures; and 2) Enhanced
scalability and efficiency via adaptable commands. By reducing the cognitive
burden on operators, HGIC opens the door for more effective large-scale mUAV
applications in complex, dynamic, and uncertain scenarios. HGIC will be
open-sourced after the paper being published online for the research community,
aiming to drive forward innovations in human-mUAV interactions.
