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.

翻译：摘要：随着技术进步的持续扩展，多无人机系统（mUAV）的能力不断提升，人类操作员在可扩展性和效率方面面临挑战，这源于与运动调整和团队协调相关的复杂认知负荷及操作。此类认知需求限制了mUAV团队的实际规模，并需要大量操作员培训，阻碍了其更广泛的采用。本文开发了一种基于手势的交互式控制系统（HGIC），这是一种新颖的界面系统，利用计算机视觉技术将手势直观地转化为机器人编队的模块化指令。通过学习控制模型，这些指令能够实现高效且可扩展的mUAV运动控制与调整。HGIC无需专用硬件，并提供两大优势：1）通过自然手势实现最低限度的培训需求；2）通过可适应指令增强可扩展性与效率。通过减轻操作员的认知负担，HGIC为在复杂、动态且不确定的场景中开展更有效的大规模mUAV应用打开了大门。在论文在线发表后，HGIC将向研究社区开源，旨在推动人机交互领域的创新。