5 research outputs found
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
Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor
Disturbance estimation for Micro Aerial Vehicles (MAVs) is crucial for
robustness and safety. In this paper, we use novel, bio-inspired airflow
sensors to measure the airflow acting on a MAV, and we fuse this information in
an Unscented Kalman Filter (UKF) to simultaneously estimate the
three-dimensional wind vector, the drag force, and other interaction forces
(e.g. due to collisions, interaction with a human) acting on the robot. To this
end, we present and compare a fully model-based and a deep learning-based
strategy. The model-based approach considers the MAV and airflow sensor
dynamics and its interaction with the wind, while the deep learning-based
strategy uses a Long Short-Term Memory (LSTM) neural network to obtain an
estimate of the relative airflow, which is then fused in the proposed filter.
We validate our methods in hardware experiments, showing that we can accurately
estimate relative airflow of up to 4 m/s, and we can differentiate drag and
interaction force.Comment: The first two authors contributed equall
Using A Quadrotor As Wind Sensor: Time-Varying Parameter Estimation Algorithms
International audienceThe objective of this paper is to develop an algorithm for the estimation of time-varying wind parameters by taking into account a detailed quadrotor model. The design objectives include the time convergence optimization, robustness to measurement noises, and a guaranteed convergence of the estimates to the true values under mild applicability conditions. It is supposed that the estimation algorithm can use IMU (accelerometers, gyroscopes) sensors augmented with an earth reference tracking system and rotor rotational velocity sensors. To this end, three time-varying parameter estimation algorithms are introduced, compared and finally merged to estimate the varying wind velocity in on-board quadrotor systems. Final numerical experiments , using a nonlinear quadrotor simulator, are used to validate the proposed approaches
Modeling and H-Infinity Loop Shaping Control of a Vertical Takeoff and Landing Drone
abstract: VTOL drones were designed and built at the beginning of the 20th century for military applications due to easy take-off and landing operations. Many companies like Lockheed, Convair, NASA and Bell Labs built their own aircrafts but only a few from them came in to the market. Usually, flight automation starts from first principles modeling which helps in the controller design and dynamic analysis of the system.
In this project, a VTOL drone with a shape similar to a Convair XFY-1 is studied and the primary focus is stabilizing and controlling the flight path of the drone in
its hover and horizontal flying modes. The model of the plane is obtained using first principles modeling and controllers are designed to stabilize the yaw, pitch and roll rotational motions.
The plane is modeled for its yaw, pitch and roll rotational motions. Subsequently, the rotational dynamics of the system are linearized about the hover flying mode, hover to horizontal flying mode, horizontal flying mode, horizontal to hover flying mode for ease of implementation of linear control design techniques. The controllers are designed based on an H∞ loop shaping procedure and the results are verified on the actual nonlinear model for the stability of the closed loop system about hover flying, hover to horizontal transition flying, horizontal flying, horizontal to hover transition flying. An experiment is conducted to study the dynamics of the motor by recording the PWM input to the electronic speed controller as input and the rotational speed of the motor as output. A theoretical study is also done to study the thrust generated by the propellers for lift, slipstream velocity analysis, torques acting on the system for various thrust profiles.Dissertation/ThesisMasters Thesis Electrical Engineering 201