AirSwarm: Enabling Cost-Effective Multi-UAV Research with COTS drones

2025-03-10

AI Summary

High entry barriers in multi-UAV swarm research and education—stemming from reliance on expensive custom platforms and external motion-capture systems—hinder accessibility and scalability. Method: We propose a lightweight, open-source swarm system based on low-cost commercial drones (e.g., Tello, Anafi), featuring a hierarchical distributed control architecture; infrastructure-agnostic visual SLAM for centimeter-level autonomous localization without motion capture; a ROS-based software framework integrating Wi-Fi-adaptive communication and embedded real-time scheduling. Contribution/Results: The system enables stable formation flight, sub-second control latency, robust operation under intermittent communication, and centimeter-level trajectory tracking accuracy. It significantly reduces both hardware acquisition and software development costs while providing an extensible, easy-to-deploy platform for swarm algorithm validation and hands-on educational use.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Distributed Problem Solving

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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

Problem

• Reduces cost of UAV swarm research using COTS drones
• Enables precise localization without external positioning systems
• Simplifies swarm development with a ROS-based software framework

Innovation

• Hierarchical control for multi-UAV coordination
• Infrastructure-free visual SLAM for precise localization
• ROS-based software framework for swarm development