The Phoenix Drone: An Open-Source Dual-Rotor Tail-Sitter Platform for Research and Education

In this paper, we introduce the Phoenix drone: the first completely open-source tail-sitter micro aerial vehicle (MAV) platform. The vehicle has a highly versatile, dual-rotor design and is engineered to be low-cost and easily extensible/modifiable. Our open-source release includes all of the design documents, software resources, and simulation tools needed to build and fly a high-performance tail-sitter for research and educational purposes. The drone has been developed for precision flight with a high degree of control authority. Our design methodology included extensive testing and characterization of the aerodynamic properties of the vehicle. The platform incorporates many off-the-shelf components and 3D-printed parts, in order to keep the cost down. Nonetheless, the paper includes results from flight trials which demonstrate that the vehicle is capable of very stable hovering and accurate trajectory tracking. Our hope is that the open-source Phoenix reference design will be useful to both researchers and educators. In particular, the details in this paper and the available open-source materials should enable learners to gain an understanding of aerodynamics, flight control, state estimation, software design, and simulation, while experimenting with a unique aerial robot.Comment: In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'19), Montreal, Canada, May 20-24, 201

Quadrotor Control in the Presence of Unknown Mass Properties

Quadrotor UAVs are popular due to their mechanical simplicity, as well as their capability to hover and vertically take-off and land. As applications diversify, quadrotors are increasingly required to operate under unknown mass properties, for example as a multirole sensor platform or for package delivery operations. The work presented here consists of the derivation of a generalized quadrotor dynamic model without the typical simplifying assumptions on the first and second moments of mass. The maximum payload capacity of a quadrotor in hover, and the observability of the unknown mass properties are discussed. A brief introduction of L1 adaptive control is provided, and three different L1 adaptive controllers were designed for the Parrot AR.Drone quadrotor. Their tracking and disturbance rejection performance was compared to the baseline nonlinear controller in experiments. Finally, the results of the combination of L1 adaptive control with iterative learning control are presented, showing high performance trajectory tracking under uncertainty.M.A.S