Master of Science Yufei Wang will on February 18 at 12:15 publically defend his thesis for the PhD degree in Nautical Operations.
Title of the PhD thesis:
«Local-Scale Advanced Ship Predictor towards Enhanced Maritime Situation Awareness»
Popular scientific abstract:
Interest in autonomous and remotely-controlled ships is growing as AI proves its capabilities across various industries. However, the introduction of these new types of vessels increases the risk of collisions and near misses during close encounters with other ships. To ensure safer ship navigation, innovative technologies are needed to support decision-making during ship operations.
This thesis focuses on the local-scale Advanced Ship Predictors (ASP), which is designed to predict a ship maneuvering behaviors over short periods. The local-scale ASP operates in two main stages. First, the ship's navigation state is estimated using the combination of kinematic motion models and Kalman filter algorithms. These estimates are then used to calculate the ship's pivot point, which helps predict its position and direction. The pivot point is a key concept in navigation, familiar to trained navigators, and plays a crucial role in understanding and predicting ship behavior.
The local-scale ASP was tested using both simulated maneuvers from the UiT bridge simulator and data gathered from sea trials conducted with the UiT research vessel, Ymir. The performance of the local-scale ASP has been gradually improved through modifications after each evaluation. In simulated tests, the local-scale ASP predicted the ship's position with an accuracy of 10 to 15 meters over 90 seconds, and direction changes within a 0 to 5-degree error range. During sea trials, the system made 10-second predictions, achieving a position error of around 8.7 meters and a direction error of about 13 degrees after a rudder adjustment.
These predictions can provide navigators with early warnings of potential collisions or near misses, providing more time to make crucial decisions. The local-scale ASP also has the potential to assist digital navigators that are anticipated to be developed by AI.
The thesis is published and available in Munin.
Supervisors:
Evaluation Committee:
Leader of the public defence:
Vice Dean of Education Professor Per Kristen Jakobsen, Faculty of Science and Technology.
Opposition ex auditorio:
If you have any questions for the candidate during the public defence, please send an e-mail to per.jakobsen@uit.no leader of the public defense. They will announce your questions during the defense.
Trial lecture:
The trial lecture is held on Tuesday February 18 at 10:15 in the same auditorium.
The title of the trial lecture is:
«Outlier detection and rejection through different estimation techniques and sensor fusion in autonomous navigation systems»
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