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STAR: Swarm Technology for Aerial Robotics Research

Jimmy Chiun, Yan Rui Tan, Yuhong Cao, John Tan, Guillaume Sartoretti

TL;DR

This work introduces a swarm architecture based on the Crazyflie, a low-cost, open-source, palm-sized aerial platform, well suited for experimental swarm algorithms, and proposes a landmark-based localization module leveraging fiducial markers that enhances the adaptability and versatility of the framework.

Abstract

In recent years, the field of aerial robotics has witnessed significant progress, finding applications in diverse domains, including post-disaster search and rescue operations. Despite these strides, the prohibitive acquisition costs associated with deploying physical multi-UAV systems have posed challenges, impeding their widespread utilization in research endeavors. To overcome these challenges, we present STAR (Swarm Technology for Aerial Robotics Research), a framework developed explicitly to improve the accessibility of aerial swarm research experiments. Our framework introduces a swarm architecture based on the Crazyflie, a low-cost, open-source, palm-sized aerial platform, well suited for experimental swarm algorithms. To augment cost-effectiveness and mitigate the limitations of employing low-cost robots in experiments, we propose a landmark-based localization module leveraging fiducial markers. This module, also serving as a target detection module, enhances the adaptability and versatility of the framework. Additionally, collision and obstacle avoidance are implemented through velocity obstacles. The presented work strives to bridge the gap between theoretical advances and tangible implementations, thus fostering progress in the field.

STAR: Swarm Technology for Aerial Robotics Research

TL;DR

This work introduces a swarm architecture based on the Crazyflie, a low-cost, open-source, palm-sized aerial platform, well suited for experimental swarm algorithms, and proposes a landmark-based localization module leveraging fiducial markers that enhances the adaptability and versatility of the framework.

Abstract

In recent years, the field of aerial robotics has witnessed significant progress, finding applications in diverse domains, including post-disaster search and rescue operations. Despite these strides, the prohibitive acquisition costs associated with deploying physical multi-UAV systems have posed challenges, impeding their widespread utilization in research endeavors. To overcome these challenges, we present STAR (Swarm Technology for Aerial Robotics Research), a framework developed explicitly to improve the accessibility of aerial swarm research experiments. Our framework introduces a swarm architecture based on the Crazyflie, a low-cost, open-source, palm-sized aerial platform, well suited for experimental swarm algorithms. To augment cost-effectiveness and mitigate the limitations of employing low-cost robots in experiments, we propose a landmark-based localization module leveraging fiducial markers. This module, also serving as a target detection module, enhances the adaptability and versatility of the framework. Additionally, collision and obstacle avoidance are implemented through velocity obstacles. The presented work strives to bridge the gap between theoretical advances and tangible implementations, thus fostering progress in the field.

Paper Structure

This paper contains 14 sections, 2 equations, 7 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related works
  3. Framework Overview
  4. Vehicle
  5. Communications
  6. Task Manager
  7. Collision Avoidance
  8. Landmark-based localisation
  9. Simulation tool
  10. Experiment
  11. Experiment setup
  12. Position estimate performance
  13. System latency
  14. Conclusion

Figures (7)

  • Figure 1: Snapshot of aerial swarm using STAR
  • Figure 2: STAR Framework Overview
  • Figure 3: Illustration of collision avoidance. 2D polygonal surfaces (pink edges) can be derived from 3D obstacle maps, with virtual agents at key points along these edges. The ORCA algorithm can then be used for static obstacle avoidance.
  • Figure 4: Landmark-based SLAM factor graph
  • Figure 5: Illustration of simulation environment: Each UAV's orientation is depicted as a coordinate axis (with red representing the x-axis, green for y, and blue for z). The camera model is illustrated by white trapeziums.
  • ...and 2 more figures