Crazyswarm: A large nano-quadcopter swarm

We define a system architecture for a large swarm of miniature quadcopters flying in dense formation indoors. The large number of small vehicles motivates novel design choices for state estimation and communication. For state estimation, we develop a method to reliably track many small rigid bodies with identical motion-capture marker arrangements. Our communication infrastructure uses compressed one-way data flow and supports a large number of vehicles per radio. We achieve reliable flight with accurate tracking (< 2 cm mean position error) by implementing the majority of computation onboard, including sensor fusion, control, and some trajectory planning. We provide various examples and empirically determine latency and tracking performance for swarms with up to 49 vehicles.

[1]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Vijay Kumar,et al.  Minimum snap trajectory generation and control for quadrotors , 2011, 2011 IEEE International Conference on Robotics and Automation.

[3]  Julian Förster,et al.  System Identification of the Crazyflie 2.0 Nano Quadrocopter , 2015 .

[4]  Roland Siegwart,et al.  Real-time metric state estimation for modular vision-inertial systems , 2011, 2011 IEEE International Conference on Robotics and Automation.

[5]  Markus Hehn,et al.  A Computationally Efficient Motion Primitive for Quadrocopter Trajectory Generation , 2015, IEEE Transactions on Robotics.

[6]  Michael R. Clement,et al.  Live-fly, large-scale field experimentation for large numbers of fixed-wing UAVs , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[7]  N. Trawny,et al.  Indirect Kalman Filter for 3 D Attitude Estimation , 2005 .

[8]  Nicholas Roy,et al.  Towards A Swarm of Agile Micro Quadrotors , 2013 .

[9]  Tamás Vicsek,et al.  Outdoor flocking and formation flight with autonomous aerial robots , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Angelo Cangelosi,et al.  Reynolds flocking in reality with fixed-wing robots: Communication range vs. maximum turning rate , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Vijay Kumar,et al.  The GRASP Multiple Micro-UAV Testbed , 2010, IEEE Robotics & Automation Magazine.

[12]  Gaurav S. Sukhatme,et al.  Observability-Aware Trajectory Optimization for Self-Calibration With Application to UAVs , 2016, IEEE Robotics and Automation Letters.

[13]  Sergei Lupashin,et al.  A platform for aerial robotics research and demonstration: The Flying Machine Arena , 2014 .

[14]  Raffaello D'Andrea,et al.  A robot self-localization system using one-way ultra-wideband communication , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[15]  Flavio Fontana,et al.  Autonomous, Vision‐based Flight and Live Dense 3D Mapping with a Quadrotor Micro Aerial Vehicle , 2016, J. Field Robotics.

[16]  Gaurav S. Sukhatme,et al.  Risk-aware trajectory generation with application to safe quadrotor landing , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.