Swarm Robotics
A Modular Infrastructure for Agile Nano-Quadcopter Swarm Autonomy
This project introduces STAR (Swarm Technology for Aerial Robotics), a modular, open-source infrastructure designed to bridge the gap between simulation and high-fidelity physical deployments. STAR integrates decentralized task allocation with robust vision-based landmark localization to manage fleets of nano-quadrotors (e.g., Crazyflies) in cluttered environments. The framework provides a high-throughput ROS 2-based communication layer and a hardware-in-the-loop (HIL) sim-to-real pipeline, enabling researchers to validate complex multi-agent algorithms, reactive obstacle avoidance, and swarm behaviors on physical robotic collectives.
Swarm RoboticsSim-to-Real DeploymentAgile AutonomyROS 2 Architecture
Contributions
What this work contributed, drawn from the papers below.
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01
Lowering the cost barrier to swarm research
The acquisition cost of physical multi-UAV systems has kept aerial swarm experiments out of reach for many labs. STAR is built around the Crazyflie, a low-cost, open-source, palm-sized platform suited to experimental swarm algorithms.
STAR, ICCAS 2024 -
02
Landmark localization that doubles as target detection
A localization module based on fiducial markers offsets the limitations of low-cost hardware, and the same module serves as a target detection module, which widens what the framework can be used for.
STAR, ICCAS 2024 -
03
Reactive collision and obstacle avoidance
Collision and obstacle avoidance are implemented through velocity obstacles.
STAR, ICCAS 2024 -
04
Closing the gap between theory and hardware
The framework is aimed squarely at making published swarm algorithms runnable as tangible implementations on real aircraft.
STAR, ICCAS 2024 -
05
3D indoor mapping beyond the Manhattan-world assumption
Indoor mapping methods typically assume axis-aligned structure. This algorithm maps three-dimensional indoor environments in non-Manhattan World settings, where those methods fail.
3D Map Generation, CIS-RAM 2024 -
06
Corner detection by breadth-first search and convolution
The algorithm filters sensor noise, identifies structural corners using breadth-first search and convolution, and constructs non-intersecting polygons to represent the environment.
3D Map Generation, CIS-RAM 2024 -
07
Reliable maps from small, cheap drones
Experiments validate improvements in automatic wall detection, noise resilience, and handling of more complex shapes, producing usable indoor maps where previous methods do not.
3D Map Generation, CIS-RAM 2024