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    Wyświetlanie 1-3 z 3
    Tytuł:
    Verification of ships trajectory planning algorithms using real navigational data
    Autorzy:
    Lazarowska, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/117103.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    Visibility Graph-search Algorithm (VGA)
    Discrete Artificial Potential Field (DAPF)
    real navigational data
    ARPA
    ship's trajectory planning
    ship's trajectory planning algorithms
    Ant Colony Optimization (ACO)
    Trajectory Base Algorithm (TBA)
    Opis:
    The paper presents results of ship's safe trajectory planning algorithms verification. Real navigational data registered from a radar with an Automatic Radar Plotting Aid on board the research and training ship Horyzont II were used as input data to the algorithms. The algorithms verified in the presented research include the Ant Colony Optimization algorithm (ACO), the Trajectory Base Algorithm (TBA), the Visibility Graph-search Algorithm (VGA) ant the Discrete Artificial Potential Field algorithm (DAPF). Details concerning data registration and exemplary results obtained with the use or real navigational data are introduced and summarized in the paper. Presented results prove the applicability of proposed algorithms for solving the ship's safe trajectory planning problem.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2019, 13, 3; 559-564
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A nature inspired collision avoidance algorithm for ships
    Autorzy:
    Lazarowska, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/24201448.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    collision avoidance algorithm
    safe own Ship's Trajectory
    safe navigation
    ant colony optimization
    firefly agorithm
    path planning
    swarm intelligence
    nature inspired computing
    Opis:
    Nature inspired algorithms are regarded as a powerful tool for solving real life problems. They do not guarantee to find the globally optimal solution, but can find a suboptimal, robust solution with an acceptable computational cost. The paper introduces an approach to the development of collision avoidance algorithms for ships based on the firefly algorithm, classified to the swarm intelligence methods. Such algorithms are inspired by the swarming behaviour of animals, such as e.g. birds, fish, ants, bees, fireflies. The description of the developed algorithm is followed by the presentation of simulation results, which show, that it might be regarded as an efficient method of solving the collision avoidance problem. Such algorithm is intended for use in the Decision Support System or in the Collision Avoidance Module of the Autonomous Navigation System for Maritime Autonomous Surface Ships.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2023, 17, 2; 341--346
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multi-criteria ACO-based algorithm for ship’s trajectory planning
    Autorzy:
    Lazarowska, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/116548.pdf
    Data publikacji:
    2017
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    colregs
    ships manoeuvering
    Ant Colony Optimization (ACO)
    ship’s trajectory planning
    route planning
    multi-criteria ACO-based algorithm
    guidance
    navigation and control (GNC)
    ARPA
    Opis:
    The paper presents a new approach for solving a path planning problem for ships in the environment with static and dynamic obstacles. The algorithm utilizes a heuristic method, classified to the group of Swarm Intelligence approaches, called the Ant Colony Optimization. The method is inspired by a collective behaviour of ant colonies. A group of agents - artificial ants searches through the solution space in order to find a safe, optimal trajectory for a ship. The problem is considered as a multi-criteria optimization task. The criteria taken into account during problem solving are: path safety, path length, the International Regulations for Preventing Collisions at Sea (COLREGs) compliance and path smoothness. The paper includes the description of the new multi-criteria ACO-based algorithm along with the presentation and discussion of simulation tests results.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2017, 11, 1; 31-36
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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