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

Xiaowei Li, Kuan Xu, Fen Liu, Ruofei Bai, Shenghai Yuan, Lihua Xie

TL;DR

AirSwarm addresses the barrier of expensive, infrastructure-dependent UAV swarms by leveraging COTS drones with drift-free visual localization and a hierarchical control stack. It integrates a three-layer architecture (Mapping, Communication, Control) and a ROS-based universal framework to enable scalable, education-friendly multi-UAV experiments on resource-constrained hardware. The system demonstrates cm-level localization accuracy and sub-27 ms control latency, with a 99% experimental success rate, outperforming costly baselines in real-world settings. By providing open-source tooling and field-validated methods, AirSwarm democratizes swarm robotics research and education while preserving practical performance.

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

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

TL;DR

AirSwarm addresses the barrier of expensive, infrastructure-dependent UAV swarms by leveraging COTS drones with drift-free visual localization and a hierarchical control stack. It integrates a three-layer architecture (Mapping, Communication, Control) and a ROS-based universal framework to enable scalable, education-friendly multi-UAV experiments on resource-constrained hardware. The system demonstrates cm-level localization accuracy and sub-27 ms control latency, with a 99% experimental success rate, outperforming costly baselines in real-world settings. By providing open-source tooling and field-validated methods, AirSwarm democratizes swarm robotics research and education while preserving practical performance.

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

Paper Structure

This paper contains 15 sections, 6 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. AirSwarm System
  4. System Overview
  5. ROS-Based Universal COTS Framework
  6. Drift-Free Visual Localization
  7. Unified Multi-UAV Architecture
  8. Position-Based Control System
  9. Experiments and Results
  10. Experimental Platform
  11. Communication Latency Analysis
  12. Navigation Performance Evaluation
  13. Case Study: Swarm-Based UAV Tracking and Encirclement
  14. Limitation and Future Works
  15. Conclusion

Figures (5)

  • Figure 1: Comparison of Swarm systems by cost and complexity, highlighting the proposed approach.
  • Figure 2: AirSwarm System Architecture. The diagram shows the complete workflow from environmental sensing to drone control, including: multi-session mapping, hardware communication architecture, and the integrated control interface.
  • Figure 3: Comparison of SLAM-estimated and Reference Trajectories in Multi-UAV Formation Flight
  • Figure 4: This visualization represents multi-agent aerial tracking and encirlement coordination using the proposed solution.
  • Figure 5: In the presence of communication noise, MAP-based SLAM like CCM-SLAM is more prone to errors due to its dependence on prior states, whereas AirSwarm is based on MLE and demonstrates greater resilience with better noise-handling capabilities. It is the key reason why the proposed solution is better for low-cost COTS swarm research.