Browsing NTNU Open by Author "Martinsen, Andreas Bell"

Now showing items 1-14 of 14

    • Autonomous docking using direct optimal control 

      Martinsen, Andreas Bell; Lekkas, Anastasios M.; Gros, Sebastien (Journal article; Peer reviewed, 2019)
      We propose a method for performing autonomous docking of marine vessels using numerical optimal control. The task is framed as a dynamic positioning problem, with the addition of spatial constraints that ensure collision ...

    • Combining system identification with reinforcement learning-based MPC 

      Martinsen, Andreas Bell; Lekkas, Anastasios; Gros, Sebastien (Peer reviewed; Journal article, 2020)
      In this paper we propose and compare methods for combining system identification (SYSID) and reinforcement learning (RL) in the context of data-driven model predictive control (MPC). Assuming a known model structure of the ...

    • Curved Path Following with Deep Reinforcement Learning: Results from Three Vessel Models 

      Martinsen, Andreas Bell; Lekkas, Anastasios M. (Chapter, 2018)
      This paper proposes a methodology for solving the curved path following problem for underactuated vehicles under unknown ocean current influence using deep reinforcement learning. Three dynamic models of high complexity ...

    • End-to-end training for path following and control of marine vehicles 

      Martinsen, Andreas Bell (Master thesis, 2018)
      The problem of following, or tracking a predefined path, has been a long standing problem in the control engineering community. In most cases, previous works utilized existing or newly-presented models to represent the ...

    • milliAmpere: An Autonomous Ferry Prototype 

      Brekke, Edmund Førland; Eide, Egil; Eriksen, Bjørn-Olav Holtung; Wilthil, Erik Falmår; Breivik, Morten; Skjellaug, Even; Helgesen, Øystein Kaarstad; Lekkas, Anastasios M.; Martinsen, Andreas Bell; Thyri, Emil Hjelseth; Torben, Tobias Valentin Rye; Veitch, Erik Aleksander; Alsos, Ole Andreas; Johansen, Tor Arne (Peer reviewed; Journal article, 2022)
      In this paper, we summarize the experiences with the autonomous passenger ferry development prototype milliAmpere, which has been used as a test platform in several research projects at the Norwegian University of Science ...

    • Optimal Model-Based Trajectory Planning With Static Polygonal Constraints 

      Martinsen, Andreas Bell; Lekkas, Anastasios; Gros, Sebastien (Peer reviewed; Journal article, 2021)
      The main contribution of this article is a novel method for planning globally optimal trajectories for dynamical systems subject to polygonal constraints. The proposed method is a hybrid trajectory planning approach, which ...

    • Optimization-Based Automatic Docking and Berthing of ASVs Using Exteroceptive Sensors: Theory and Experiments 

      Martinsen, Andreas Bell; Bitar, Glenn Ivan; Lekkas, Anastasios M.; Gros, Sebastien (Peer reviewed; Journal article, 2020)
      Docking of autonomous surface vehicles (ASVs) involves intricate maneuvering at low speeds under the influence of unknown environmental forces, and is often a challenging operation even for experienced helmsmen. In this ...

    • Optimization-based Planning and Control for Autonomous Surface Vehicles 

      Martinsen, Andreas Bell (Doctoral theses at NTNU;2021:164, Doctoral thesis, 2021)
      With autonomy offering a number of benefits in robotics applications, such as increased safety, better consistency and reliability, reduced environmental impact and higher efficiency, it is not surprising that the topic ...

    • Reinforcement learning-based NMPC for tracking control of ASVs: Theory and experiments 

      Martinsen, Andreas Bell; Lekkas, Anastasios; Gros, Sebastien (Peer reviewed; Journal article, 2022)
      We present a reinforcement learning-based (RL) model predictive control (MPC) method for trajectory tracking of surface vessels. The proposed method uses an MPC controller in order to perform both trajectory tracking and ...

    • Reinforcement Learning-Based Tracking Control of USVs in Varying Operational Conditions 

      Martinsen, Andreas Bell; Lekkas, Anastasios M.; Gros, Sebastien (Peer reviewed; Journal article, 2020)
      We present a reinforcement learning-based (RL) control scheme for trajectory tracking of fully-actuated surface vessels. The proposed method learns online both a model-based feedforward controller, as well an optimizing ...

    • Straight-Path Following for Underactuated Marine Vessels using Deep Reinforcement Learning 

      Martinsen, Andreas Bell; Lekkas, Anastasios M. (Chapter, 2018)
      We propose a new framework, based on reinforcement learning, for solving the straight-path following problem for underactuated marine vessels under the influence of unknown ocean current. A dynamic model from the Marine ...

    • Trajectory Planning and Control for Automatic Docking of ASVs with Full-Scale Experiments 

      Bitar, Glenn Ivan; Martinsen, Andreas Bell; Lekkas, Anastasios; Breivik, Morten (Peer reviewed; Journal article, 2020)
      We propose a method for performing automatic docking of a small autonomous surface vehicle (ASV) by interconnecting an optimization-based trajectory planner with a dynamic positioning (DP) controller for trajectory tracking. ...

    • Two Space-Time Obstacle Representations Based on Ellipsoids and Polytopes 

      Martinsen, Andreas Bell; Lekkas, Anastasios (Journal article; Peer reviewed, 2021)
      When operating autonomous surface vessels in uncertain environments with dynamic obstacles, planning safe trajectories and evaluating collision risk is key to navigating safely. In order to perform these tasks, it is ...

    • Two-Stage Optimized Trajectory Planning for ASVs Under Polygonal Obstacle Constraints: Theory and Experiments 

      Bitar, Glenn Ivan; Martinsen, Andreas Bell; Lekkas, Anastasios; Breivik, Morten (Peer reviewed; Journal article, 2020)
      We propose a method for energy-optimized trajectory planning for autonomous surface vehicles (ASVs), which can handle arbitrary polygonal maps as obstacle constraints. The method comprises two stages: The first is a hybrid ...