Autonomous Systems
Building and
Testing
the Future of Autonomy
Autonomous systems are at the core of modern robotics research. They represent the ability of machines to perceive, decide, and act independently in dynamic environments. The Crazyflie® platform offers a unique foundation for developing and validating these systems, combining full control access with a robust and flexible hardware–software architecture.
Researchers worldwide use Crazyflie to study autonomous flight, multi-agent decision-making, and adaptive control in real-world conditions. Its compact form, open firmware, and advanced localization options make it an ideal testbed for testing algorithms that move seamlessly from simulation to reality.
From Concept to
Controlled Autonomy
With Crazyflie, researchers can implement and evaluate:
- Adaptive control algorithms that respond to disturbances, noise, and model uncertainty.
- Reinforcement learning approaches for motion control, energy optimization, and decision-making.
- Onboard autonomy using embedded AI and edge computing frameworks.
- Cooperative autonomy where multiple drones coordinate tasks through distributed communication.
- System identification and modeling for dynamic system analysis and validation.
- Fault detection and resilient autonomy to evaluate system behavior under failures,and unexpected environmental conditions.
Each experiment can be fully instrumented, logged, and reproduced. The open architecture allows integration with ROS, Python, MATLAB, and other research tools, ensuring smooth transitions between simulation environments and hardware testing.
Research in Action
Research in autonomous systems continues to push the boundaries of what robots can perceive, decide, and accomplish independently. These examples reflect diverse approaches to autonomy, spanning control systems, onboard intelligence, multi-agent coordination, and adaptive behavior.
TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers
Carnegie Mellon University
DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control
University of Washington
Learning to Fly in Seconds
New York University
Low-Level Control of a Quadrotor With Deep Model-Based Reinforcement Learning
University of California–Berkeley
Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms Using Learned Interactions
California Institute of Technoogy
A bioinspired revolving-wing drone with passive attitude stability and efficient hovering flight
City University of Hong Kong
Reference Setup
Every project has unique requirements, but many share common building blocks. The configurations below provide practical starting points for autonomy research, combining hardware, software, and localization infrastructure commonly used in studies of autonomous flight, adaptive control, and onboard decision-making.
Take your autonomous flying experience to the next level with the Infinite Flight Bundle. This complete package provides everything you need to keep your Crazyflie 2.1 Brushless in the air 24/7—with seamless landings and automated charging for continuous operation.
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With the STEM ranging bundle you get everything you need for scripting and learning how to operate a robot in 3 dimensions and react to objects around it.
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With the STEM ranging bundle you get everything you need for scripting and learning how to operate a robot in 3 dimensions and react to objects around it.
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The Crazyflie gives researchers the freedom to experiment with autonomy in all its forms: perception-driven control, cooperative flight, or learning-based adaptation.
Whether your work focuses on embedded AI, distributed systems, or flight control, the platform provides a dependable bridge between theoretical models and real-world results.