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    Item type:Publication, Access status: without files ,
    Concept for a decentralized tactical conflict management in a U-space ecosystem
    (IEEE, 2024)
    Rönn, Luca von
    ;
    Häckel, Timo
    ;
    Juchmann, Nemo
    ;
    Grebner, Tobias
    ;
    Kloock-Schreiber, Daniel
    To ensure the success of future urban air mobility, it is important to address public concerns regarding safety, and noise. Current regulations stipulate that UAS traffic in Europe occurs in U-space airspaces, where the UAS operator is responsible for flight safety. However, before these legal principles can be technically implemented, the underlying processes must first be defined. Therefore, this paper proposes a decentralized tactical conflict management concept in a U-space ecosystem. The concept is developed based on legal and technical requirements for process automation, sensor technology, and communication. It consists of three modules: conflict detection, application of resolution strategies, and initiating a failure strategy. The focus is on the presentation of key stakeholders and their responsibilities as well as the interactions between them. A final practical example shows the basic applicability of the concept.
    Publicationtype: Conference Paper
      29
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    Item type:Publication, Access status: without files ,
    Towards Non-invasive Fish Monitoring in Hard-to-Access Habitats Using Autonomous Underwater Vehicles and Machine Learning∗
    (2021)
    Zach, Juri
    ;
    Busse, Christian
    ;
    Funk, Steffen
    ;
    Mollmann, Christian
    ;
    Renner, Bernd-Christian
    This paper presents a concept for non-invasive, high spatio-temporal resolution fish monitoring. Autonomous underwater vehicles combined with artificial intelligence enable automatic habitat mapping and fish detection from sonar and camera data. The monitoring approach will help to fill important knowledge gaps on target fish spatio-temporal distribution in hard-to-access areas, give valuable insight into target fish behavior, and help to identify the species' essential habitats, which is relevant for the design of marine protected areas in fish management and conservation. Many of the required hardware, such as underwater vehicles, sensors, and acoustic modems, have become very cost-effective over recent years, making this approach feasible. Unclear, however, remains the question of how to detect fish using images obtained from low-cost camera and sonar devices. Therefore, we have reviewed and discussed suitable machine learning techniques for this task.
    Publicationtype: Conference Paper
      95