Browsing NTNU Open by Author "Flåten, Andreas Lindahl"

Now showing items 1-5 of 5

    • Gaussian Field Current Estimation from Drift Sea Ice Tracking with the Labeled Multi-Bernoulli Filter 

      Olofsson, Jonatan; Flåten, Andreas Lindahl; Veibäck, Clas; Lauknes, Tom Rune (Chapter, 2017)
      In polar region operations, drift sea ice positioning and tracking is useful for both scientific and safety reasons. Modeling ice movements has proven difficult, not least due to the lack of information of currents and ...

    • Radar-based Maritime Collision Avoidance using Dynamic Window 

      Eriksen, Bjørn-Olav Holtung; Wilthil, Erik Falmår; Flåten, Andreas Lindahl; Brekke, Edmund Førland; Breivik, Morten (Journal article; Peer reviewed, 2018)
      Collision avoidance systems are a key ingredient in developing autonomous surface vehicles (ASVs). Such systems require real-time information about the environment, which can be obtained from transponder-based systems or ...

    • The branching-course model predictive control algorithm for maritime collision avoidance 

      Eriksen, Bjørn-Olav Holtung; Breivik, Morten; Wilthil, Erik Falmår; Flåten, Andreas Lindahl; Brekke, Edmund Førland (Journal article; Peer reviewed, 2019)
      This article presents a new algorithm for short‐term maritime collision avoidance (COLAV) named the branching‐course model predictive control (BC‐MPC) algorithm. The algorithm is designed to be robust with respect to noise ...

    • The Neighbor Course Distribution Method with Gaussian Mixture Models for AIS-based Vessel Trajectory Prediction 

      Dalsnes, Bjørnar; Hexeberg, Simen; Flåten, Andreas Lindahl; Eriksen, Bjørn-Olav Holtung; Brekke, Edmund Førland (Chapter, 2018)
      When operating an autonomous surface vessel (ASV) in a marine environment it is vital that the vessel is equipped with a collision avoidance (COLAV) system. This system must be able to predict the trajectories of other ...

    • Visual Detection of Maritime Vessels 

      Tangstad, Espen Johansen (Master thesis, 2017)
      This thesis investigates how a tailored Convolutional Neural Network (CNN) can aid autonomous surface vehicles (ASVs) in detecting and classifying maritime traffic for collision avoidance. Several state of the art CNN ...