Date of Award
2019
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Graduate Group
Electrical & Systems Engineering
First Advisor
Vijay Kumar
Abstract
Quadrotors and multirotors in general are common in many inspection and surveillance
applications. For these applications, visual-inertial odometry is a common way to localize
the vehicles and observe the environment. However, unlike with wheeled mobile robots,
quadrotor localization algorithms often do not use knowledge of the control inputs and
the full vehicle dynamics as a process model for localization. Rather, they use kinematic
models, with the IMU providing acceleration and angular velocity. One of the reasons for
avoiding the use of dynamics is that, until recently quadrotor aerodynamic effects have not
been considered in the literature and hence the dynamic models for quadrotors have been
less accurate than those for wheeled mobile robots. The main aerodynamic terms that are
significant are first-order effects that are linear in velocity and angular velocity. They are
predominantly caused by aerodynamic interaction with the spinning propellers. This work
investigates the models for such effects, as well as what can be gained if such aerodynamic
effects are incorporated into the dynamic model and the full dynamics are used for state
estimation. We develop novel IMU-based filters, the end results of which are used to estimate
the wind velocity of the quadrotor or, indoors, when the ambient wind is zero, the velocity
of the quadrotor. In addition, these filters estimate the many aerodynamic parameters in
the model online. They may also be used to estimate sensor biases and inertial parameters.
We demonstrate the effectiveness of these filters through experiments. We also present
nonlinear observability analyses that theoretically determine the observability properties of
the systems.
Recommended Citation
Svacha, James Baird, "Imu-Based State Estimation And Control Of Quadrotors Exploiting Aerodynamic Effects" (2019). Publicly Accessible Penn Dissertations. 3604.
https://repository.upenn.edu/edissertations/3604