Path planning algorithms that can account for the complexity of the airspace are imperative for operations of unmanned air traffic in Advanced Air Mobility applications such as parcel delivery, air taxis, and medical transport. This paper proposes an algorithm based on Rapidly-exploring Random Trees Star (RRT*) to plan paths for unmanned aircraft navigating in environments with solid and permeable obstacles. Thresholding in the collision-checker and distance-dependent penalties in the objective function of RRT* are introduced in the algorithm. The path planner is applied to the case where estimated air traffic density data is used to define the permeability of obstacles. The capabilities of the algorithm and the applicability to the air traffic density case study are demonstrated through simulations of flight scenarios around an international airport.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Unmanned Aircraft Path Planning Using Air Traffic Density


    Beteiligte:
    Li, Steven (Autor:in) / Chang, Josh (Autor:in) / Chavda, Nishkarsh (Autor:in) / Borshchova, Iryna (Autor:in) / Laliberte, Jeremy (Autor:in) / Rodrigues, Luis (Autor:in)


    Erscheinungsdatum :

    18.06.2024


    Format / Umfang :

    1018530 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    PATH PLANNING METHOD OF UNMANNED AERIAL VEHICLE FOR TRAFFIC SERVICE AND PATH PLANNING APPARATUS

    LEE HYUNG JUNE / CHO JEI HEE | Europäisches Patentamt | 2023

    Freier Zugriff

    Adaptive Path Planning for Unmanned Aircraft Using In-flight Wind Velocity Estimation

    Benders, Sebastian / Wenz, Andreas / Johansen, Tor Arne | IEEE | 2018


    Multi-query Path Planning for an Unmanned Fixed-Wing Aircraft

    Niendorf, Moritz / Schmitt, Fabian / Adolf, Florian | AIAA | 2013



    Unmanned Aircraft Systems Traffic Management

    Baum, Michael / Safari, an O’Reilly Media Company. | TIBKAT | 2021