The deployment of Advanced Air Mobility requires the continued development of technologies to ensure operational safety. One of the key aspects to consider here is the availability of robust solutions to avoid tactical conflicts between drones and other flying elements, such as other drones or birds. Bird detection is a relatively underexplored area, but due to the large number of birds, their shared airspace with drones, and the fact that they are non-cooperative elements within an air traffic management system, it is of interest to study how their detection can be improved and how collisions with them can be avoided. This work demonstrates how a LiDAR sensor mounted on a drone can detect birds of various sizes. A LiDAR simulator, previously developed by the Aerolab research group, is employed in this study. Six different collision trajectories and three different bird sizes (pigeon, falcon, and seagull) are tested. The results show that the LiDAR can detect any of these birds at about 30 m; bird detection improves when the bird gets closer and has a larger size. The detection accuracy is higher than 1 m in most of the cases under study. The errors grow with increasing drone-bird relative speed.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Assessment of LiDAR-Based Sensing Technologies in Bird–Drone Collision Scenarios


    Beteiligte:


    Erscheinungsdatum :

    2024




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    LIDAR Micro Drone with Proximity Sensing

    Maheswari, E / V, Balaji / Ezhilarasi, G et al. | IEEE | 2023


    Drone Propeller Guard with Collision Sensing Function

    Europäisches Patentamt | 2025

    Freier Zugriff

    Drone-assisted Remote Gas Sensing in Volcanic Scenarios

    Guastella, Dario Calogero / Sutera, Giuseppe / Giudice, Gaetano et al. | IEEE | 2024


    DRONE COLLISION AVOIDANCE

    LEV TSVI / HOCH YAACOV | Europäisches Patentamt | 2020

    Freier Zugriff

    Drone collision avoidance

    LEV TSVI / HOCH YAACOV | Europäisches Patentamt | 2021

    Freier Zugriff