AirSwarm：利用商用现货无人机实现高性价比的多无人机研究
AirSwarm: Enabling Cost-Effective Multi-UAV Research with COTS drones
AI总结：
AirSwarm利用低成本商用无人机、分层控制架构、无基础设施视觉SLAM和ROS框架，降低多无人机集群研究的技术与资金门槛，实现厘米级定位与可靠协调。
摘要：
传统的无人机（UAV）集群任务严重依赖昂贵的定制无人机，这些无人机配备机载感知或外部定位系统，限制了其在研究和教育中的广泛应用。为解决这一问题，我们提出了AirSwarm。AirSwarm利用低成本商用现货无人机（如Tello或Anafi）实现多无人机协调的普及化，使经济实惠的集群空中机器人研究和教育成为可能。关键创新包括：用于可靠多UAV协调的分层控制架构、无需外部动作捕捉即可实现精确定位的基础设施无关视觉SLAM系统，以及基于ROS的软件框架以简化集群开发。实验展示了厘米级跟踪精度、低延迟控制、通信故障抵抗能力、编队飞行和轨迹跟踪。通过降低财务和技术门槛，AirSwarm使多机器人教育和研究更加易于获取。完整说明和开源代码将予以公开。
英文摘要：
Traditional unmanned aerial vehicle (UAV) swarm missions rely heavily on expensive custom-made drones with onboard perception or external positioning systems, limiting their widespread adoption in research and education. To address this issue, we propose AirSwarm. AirSwarm democratizes multi-drone coordination using low-cost commercially available drones such as Tello or Anafi, enabling affordable swarm aerial robotics research and education. Key innovations include a hierarchical control architecture for reliable multi-UAV coordination, an infrastructure-free visual SLAM system for precise localization without external motion capture, and a ROS-based software framework for simplified swarm development. Experiments demonstrate cm-level tracking accuracy, low-latency control, communication failure resistance, formation flight, and trajectory tracking. By reducing financial and technical barriers, AirSwarm makes multi-robot education and research more accessible. The complete instructions and open source code will be available at
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
