Position, Velocity and Orientation Estimation of Minesto’s Crossflow Underwater Kite
Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
Lyster, Linn
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Crossflow underwater kites have shown promising potential to generate green energy,
with Minesto’s Deep Green on the front line. The power output is optimized when
the kite is following a figure-eight trajectory, where the motion is controlled by a
rudder on the kite. This work focuses on providing the control system with improved
input about position, velocity and orientation of the kite, with the use of inertial
sensors and knowledge of depth, in order to steer the rudder optimally. The sensor
signals were processed and filtered in order to handle problems of sensor and process
noise.
An algorithm was designed that combined the different sensors to predict the
pose and motion of the kite. This was done by first approximate the noise of the
sensors, which were used as input, into an extended Kalman filter for orientation
estimation, together with inertial measurements from a gyroscope and an accelerometer.
After an initial guess on position, based mainly on depth of kite, a steady-state
Kalman filter was applied in order to improve the position estimate and also obtain
velocity.
The result show that the sensor fusion performed has potential in predicting the
movement of the kite. However, the limited access to data prevents us from drawing
too big conclusions. Even if there are some challenges regarding bias drift and
robustness of the algorithm, it can be shown that the proposed algorithm produces
realistic output when it comes to physical constraints due to the tether length but
also in terms of periodicity of the orientation.
Beskrivning
Ämne/nyckelord
pose estimation , cross-flow underwater kite , sensor fusion , Kalman Filter , extended Kalman Filter