Vision-based automatic landing of a quadrotor UAV on a floating platform: A new approach using incremental backstepping

Vision-based automatic landing of a quadrotor UAV on a floating platform: A new approach using incremental backstepping

Mendes, A.S.

Chu, Q.P. (mentor)
Van Kampen, E. (mentor)
Mulder, J.A. (mentor)
Remes, B.D.W. (mentor)

Aerospace Engineering

Control and Simulation

Dynamics and Control of Aerospace Vehicles

2012-02-24

The development of systems that allow unmanned aerial vehicles, known as UAVs, to perform tasks autonomously is a current trend in aerospace research. The specific aim of this thesis is to study and achieve vision-based automatic landing of a quadrotor UAV on a floating platform, a known target that possesses oscillatory behavior. The research contributions to be taken from this study can be divided into two perspectives, as described below. From a theoretical point of view, a design solution is proposed which includes GPS navigation to enable the quadrotor to find the target, and vision-based control to approach and land upon it. From this design, several control-related issues must then be solved, mainly the development of a controller for the autoland mission. To accomplish this control task, an incremental backstepping control law is developed. Additionally, linear and standard backstepping controllers are designed for comparison. The derived control laws require knowledge of the states to close the feedback loops; therefore, state estimation algorithms are designed for complete state reconstruction. The approach selected is modular, thus separating position/velocity estimation from attitude determination. The former is performed using an extended Kalman filter, and the latter using a complementary filter. Furthermore, an augmented Kalman filter formulation is developed for estimation of the platform’s vertical motion. The combination of control and state estimation algorithms is tested in a simulated environment using a simulation tool developed in this study for Monte-Carlo analysis. This tool allows for evaluation of the design not only for the nominal case, but also for random combinations of external conditions. Results show that successful performance is obtained for the nonlinear controllers since the desired criteria is met and the risk of crashing is demonstrated to be residual. Additional tests show that incremental backstepping is, in general, more robust than standard backstepping in the case of model mismatch, even in the presence of state estimation errors. From a practical perspective, the findings are twofold. First, this thesis presents a procedure to experimentally determine the moments of inertia of the quadrotor by using a two-axis motion simulator and a six-component force/torque sensor. The inertia properties are also determined analytically using two modeling approaches: point mass analysis and assumption of simple geometric shapes. The results show that point mass analysis can lead to erroneous inertia estimation deviation of 20-30% from the real value), thus resulting in a significant model mismatch. The experimental and simple shapes assumption methods render similar results, which strongly indicates not only that the experimental method proposed is valid, but also that the assumption of simple geometric shapes can be used as a reliable and cost-effective method to determine moments of inertia of small UAVs. Second, in this thesis the system is tested in real time using an actual quadrotor. Flight tests are performed for hovering above a target with known characteristics, and to achieve this end, a vision system is developed to obtain relative position measurements from images captured by an on-board camera. A Kalman filter is implemented for real-time integration of vision with IMU data, and a linear controller with reference command filters is used. Tuning procedures are then carried out until satisfactory performance is achieved.

Quadrotor UAV
Moments of inertia experiment
Computer vision
Augmented Kalman filtering
Incremental backstepping
Monte-Carlo simulations
Real-time implementation

http://resolver.tudelft.nl/uuid:f8848255-1831-482a-be21-2da3ca3e50bb

Student theses

master thesis

(c) 2012 Mendes, A.S.