Unmanned aerial vehicles (UAVs) have recently gained increasing attention. Self-positioning and integrated navigation are main aspects during a flight mission and rely on predefined trajectories. Current guidance and control systems provide real-time control laws to track desired trajectories given by the Embedded Flight Management System (E-FMS). Since some UAVs are highly non-linear systems with under-actuation properties from the control point of view, discontinuities in control inputs (coming from trajectories generated by E-FMS) can produce undesired vibrations causing aging and damages in the structure. In this paper, we propose a Recursive Smooth Trajectory generation algorithm (RST) that allows for finding a smooth $\mathcal{C}^{\propto}$ polynomial path, and thus a close form trajectory satisfying any arbitrary dynamic limitations translated into kinematic constraints (e.g. position, velocity, acceleration, etc). Each kinematic constraint is recursively fulfilled, leading to a fast online implementation for the E-FMS. The storage requirements and execution time are therefore discussed in this paper. The effectiveness and suitability of the RST algorithm over a minimum-snap piecewise polynomial approach for highly nonlinear UAV flight is also analyzed.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A New Recursive Framework for Trajectory Generation of UAVs


    Beteiligte:


    Erscheinungsdatum :

    01.03.2020


    Format / Umfang :

    3939589 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Ground-sensitive trajectory generation for UAVs

    ROY NICK / TAKAYAMA LEILA / FLECK MATHIAS SAMUEL et al. | Europäisches Patentamt | 2016

    Freier Zugriff

    Collision-free trajectory generation for UAVs using Markov decision process

    Yu, Xiang / Zhou, Xiaobin / Zhang, Youmin | IEEE | 2017



    Deep Reinforcement Learning for Trajectory Generation and Optimisation of UAVs

    Akhtar, Mishma / Maqsood, Adnan / Verbeke, Mathias | IEEE | 2023