Used in research

Flight lab

The Crazyflie is ideal for many areas of research


The Crazyflie is the ideal tool for research in many areas, for instance control algorithms, swarms, path finding, agriculture or failure recovery. The platform is designed to be as flexible and versatile as possible to enable the user to explore the area of interest.


Add new sensors or other hardware to support your needs, the expansion port supports a variety of interfaces and there is plenty of power in the processors for you to use. If you need more lifting capacity, use the BigQuad deck and move your application to a bigger frame, while still being able to develop it in the comfort of your lab.


The Crazyflie can be controlled from a number of programming languages and also integrates with tools and frameworks such as ROS or ZeroMQ. It works well with standard development tools to make your work a breeze.


It is all open to allow you to change what you need to change, you will never be locked in by limited APIs or closed software. Never.

How the Crazyflie 2.X is used by universities

Universities around the world are using the Crazyflie 2.X in different types of research fields. Here we have collected guest blog posts from our blog to show some examples.

Crazyflie used by Carnegie Mellon University

“We use the Crazyflie platform to evaluate our algorithms because the hardware is robust and the user community has helped make firmware available on which we can base our own systems” -Ellen Cappo, researcher at Carnegie Mellon University.

Read about how the researchers at Carnegie Mellon University are using the Crazyflie 2.X to test theory on real world systems. Towards persistent, adaptive multi-robot systems.

Crazyflie used by MIT

“The Crazyflie is easily obtainable, safe, and (we can certify ourselves) very robust. Moreover, since it is open-source and fully programmable, we were able to easily modify the Crazyflie to fit our needs.” -Brandon Araki, researcher at MIT.

In Multi-robot Path Planning for Flying-and-Driving Vehicles you can read about how the researchers at MIT are using the Crazyflie 2.0 to study the coordination of multiple robots.

Publications involving Crazyflie 2.X

  • An Efficient Real-Time NMPC for Quadrotor Position Control under Communication Time-Delay, Barbara Barros Carlos, Tommaso Sartor, Andrea Zanelli, Gianluca Frison, Wolfram Burgard, Moritz Diehl, and Giuseppe Oriolo, Sapienza University of Rome, KU Leuven, University of Freiburg, 2020 PDF Video

  • Latency-aware and -predictable Communication with Open Protocol Stacks for Remote Drone Control, Marlene Böhmer, Andreas Schmidt, Pablo Gil Pereira, Thorsten Herfet, Saarland Informatics Campus, 2020 PDF

  • Distributed Consensus Control of Multiple UAVs in a Constrained Environment, Gang Wang, Weixin Yang, Na Zhao, Yunfeng Ji, Yantao Shen, Hao Xu, and Peng Li, University of Nevada, Reno, 2020 PDF

  • Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactions, Guanya Shi, Wolfgang Honig, Yisong Yue, and Soon-Jo Chung, 2020 PDF

  • Intuitive 3D Control of a Quadrotor in User Proximity with Pointing Gestures, Boris Gromov, Jer´ ome Guzzi, Luca M. Gambardella, Alessandro Giusti, IDSIA, 2020 PDF Video

  • Hand-worn Haptic Interface for Drone Teleoperation, Matteo Macchini, Thomas Havy, Antoine Weber, Fabrizio Schiano, and Dario Floreano, EPFL, 2020 PDF Video

  • ModQuad-DoF: A Novel Yaw Actuation for Modular Quadrotors, Gabrich, Bruno, Guanrui Li, and Mark Yim, University of Pennsylvania, 2020 PDF web

  • Online trajectory generation with distributed model predictive control for multi-robot motion planning, C. E. Luis, M. Vukosavljev, and A. P. Schoellig, Dynamic Systems Lab, University of Toronto, 2020 PDF Video

  • Fast and In Sync: Periodic Swarm Patterns for Quadrotors, Xintong Du, Carlos E. Luis, Marijan Vukosavljev, Angela P. Schoellig, Dynamic Systems Lab, University of Toronto 2019 PDF Video

  • Learning-based Bias Correction for Ultra-wideband Localization of Resource-constrained Mobile Robots, Wenda Zhao, Abhishek Goudar, Jacopo Panerati, Angela P. Schoellig, University of Toronto, 2020. PDF

  • Signal-based self-organization of a chain of UAVs for subterranean exploration, Pierre Laclau, Vladislav Tempez, Franck Ruffier, Enrico Natalizio, Jean-Baptiste Mouret, Universit de Technologie de Compigne, 2020. PDF

  • Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment, K. N. McGuire, C. De Wagter, K. Tuyls, H. J. Kappen and G. C. H. E. de Croon, TU Delft, 2019. PDF, Video

  • A 64-mW DNN-Based Visual Navigation Engine for Autonomous Nano-Drones, Daniele Palossi, Antonio Loquercio, Francesco Conti, Eric Flamand, Davide Scaramuzza, Luca Benini, ETH Zürich, 2019. PDF

  • Trajectory-based Agile Multi UAV Coordination through Time Synchronisation, Omar Shadeed, Halise Turkmen, Emre Koyuncu, AIAA Science and Technology Forum and Exposition, 2020. PDF

  • Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontroller, Bardienus P. Duisterhof, Srivatsan Krishnan, Jonathan J. Cruz, Colby R. Banbury William Fu, Aleksandra Faust, Guido C. H. E. de Croon, Vijay Janapa Reddi, Harvard University, 2019. PDF, Video

  • Compliant Bistable Gripper for Aerial Perching and Grasping, Haijie Zhang, Jiefeng Sun and Jianguo Zhao, Department of Mechanical Engineering, Colorado State University, 2019. PDF

  • Vision based control and landing of Micro aerial vehicles, Christoffer Karlsson, Department of Engineering and Physics, Karlstad University, 2019. PDF

  • Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight, Katie Kang, Suneel Belkhale, Gregory Kahn, Pieter Abbeel, Sergey Levine, Berkeley AI Research (BAIR), University of California, Berkeley, 2019. PDF, Video

  • Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning, Nathan O. Lambert, Daniel S. Drew, Joseph Yaconelli, Roberto Calandra, Sergey Levine and Kristofer S. J. Pister, 2019. PDF

  • Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping, Burgués, J.; Hernández, V.; Lilienthal, A.J.; Marco, S. Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping. Sensors 2019, 19, 478. PDF

  • CrazyS: a software-in-the-loop simulation platform for the Crazyflie 2.0 nano-quadcopter, Giuseppe Silano and Luigi Iannelli, in Robot Operating System (ROS): The Complete Reference (Volume 4), K. Anis, Ed. Springer International Publishing, 2019. PDF, Code, Video

  • Optimal trajectory generation for time-to-contact based aerial robotic perching, Haijie Zhang, Bo Cheng and Jianguo Zhao, Department of Mechanical Engineering, Colorado State University, 2018. PDF

  • Relative positioning system for UAVs in swarming applications, Andres Cecilia Luque, Master’s thesis, Technical University of Denmark, 2018. PDF, Video

  • Improved Quadcopter Disturbance Rejection Using Added Angular Momentum, Nathan Bucki and Mark W. Mueller, in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. PDF, Video

  • CrazyS: a software-in-the-loop platform for the Crazyflie 2.0 nano-quadcopter, Giuseppe Silano, Emanuele Aucone and Luigi Iannelli, in Proc. 2018 26th Mediterranean Conference on Control and Automation (MED), Zadar, Croatia, 2018. PDF, Code, Video

  • Self-Calibrating Ultra-Wideband Network Supporting Multi-Robot Localization, Michael Hamer, Raffaello D’Andrea, ETH, 2017. PDF, Video

  • Development of a multi-agent quadrotor research platform with distributed computational capabilities, Ian S. McInerney, Master’s thesis, Iowa State University, 2017. PDF, Code

  • Downwash-Aware Trajectory Planning for Large Quadrotor Teams, James A. Preiss, Wolfgang Hönig, Nora Ayanian, and Gaurav S. Sukhatme, in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017. PDF, Video

  • Modelling and Control of the Crazyflie Quadrotor for Aggressive and Autonomous Flight by Optical Flow Driven State Estimation, M. Greiff, Master’s thesis, Lund University, 2017. PDF

  • Crazyflie 2.0 Quadrotor as a Platform for Research and Education in Robotics and Control Engineering, Wojciech Giernacki, Mateusz Skwierczyński, Wojciech Witwicki, Paweł Wroński and Piotr Kozierski, Faculty of Electrical Engineering, Institute of Control and Information Engineering Poznan University of Technology, Poland, 2017. PDF, Video 1, Video 2, Video 3, Video 4

  • Crazyswarm: A Large Nano-Quadcopter Swarm, James A. Preiss, Wolfgang Hönig, Gaurav S. Sukhatme and Nora Ayanian, in Proc. IEEE International Conference on Robotics and Automation, 2017. PDF, Video

  • A Hybrid Method for Online Trajectory Planning of Mobile Robots in Cluttered Environments, Leobardo Campos-Macías, David Gómez-Gutiérrez, Rodrigo Aldana-López, Rafael de la Guardia and José I. Parra Vilchis, Multi-Agent Autonomous Systems - Intel Labs, 2017. PDF, or PDF, Video

  • Study and Development of Target Following Capability on Nano-Sized Unmanned Aerial Vehicles, Jaskirat Singh, Master’s Thesis, ETH Zürich, 2017. PDF, Video

  • Smoothing and Mapping of an Unmanned Aerial Vehicle Using Ultra-wideband Sensors, Erik Strömberg’s Master Thesis, KTH, Stockholm, 2017. PDF

  • Development of a swarm control platform for educational and research applications, Justin Noronha, Master’s Thesis, Iowa State University, 2016. PDF

  • Stippling with aerial robots, B. Galea, E. Kia, N. Aird, and P. G. Kry, Computational Aesthetics / Expressive, 2016. PDF, Video 1, Video 2

  • Planning and Control for Quadrotor Flight through Cluttered Environments, B. Landry, Master’s thesis, Massachusetts Institute of Technology, USA, June 2015. PDF, Video, Code

  • Mixed Reality for Robotics, W. Hönig, C. Milanes, L. Scaria, T. Phan, M. Bolas, and N. Ayanian, IROS, 2015. PDF, Video

  • System Identification of the Crazyflie 2.0 Nano Quadrocopter, J. Förster, Bachelor’s thesis, ETH Zürich, 2015. PDF

Publications involving Crazyflie 1.0

  • Lyapunov-based Controller Synthesis and Stability Analysis for the Execution of High-speed Multi-flip Quadrotor Maneuvers, in Proc. American Control Conference, Seattle, USA, May 2017. PDF, Video

  • Generation and Real-Time Implementation of High-Speed Controlled Maneuvers Using an Autonomous 19-Gram Quadrotor, Y. Chen and N. O. Pérez-Arancibia, in Proc. 2016 IEEE Int. Conf. Robot. Autom. (ICRA 2016), Stockholm, Sweden, May 2016. PDF, Video

  • Nonlinear control strategies for quadrotors and CubeSats, Giri Prashanth Subramanian, Master’s thesis, University of Illinois at Urbana-Champaign, USA, July 2015. PDF

  • Perching Failure Detection and Recovery with Onboard Sensing, Hao Jiang, Morgan T. Pope, Matthew A. Estrada, Bobby Edwards, Mark Cuson, Elliot W. Hawkes and Mark R. Cutkosky, IROS, 2015. PDF, Video

  • HoverBall : Augmented Sports with a Flying Ball, Kei Nitta, Keita Higuchi and Jun Rekimoto, 5th International Conference on Augmented Human (AH 2014), 2014. PDF

  • Visual-inertial navigation for a camera-equipped 25g nano-quadrotor, O. Dunkley, J. Engel, J. Sturm, and D. Cremers, in IROS2014 Aerial Open Source Robotics Workshop, 2014. PDF, Video

  • Visual inertial control of a nano-quadrotor, O. M. W. Dunkley, Master’s thesis, Technical University Munich, Germany, Sept. 2014. PDF, Code

  • Development of a wireless video transfer system for remote control of a lightweight UAV, J. Tosteberg and T. Axelsson, Master’s thesis, Linköping University, Sweden, June 2012. PDF

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