We present a concept to research the implementation of a self-aware control system for underwater cleaning robots. Multiple robots cooperate as a swarm to clean the hull of large container ships while in transit. We utilise physics-informed neural networks to implement an autonomous steering system and combine it with a deep reinforcement learning-based decision and scheduling system. This approach ensures efficient navigation and cleaning operations, adapting to dynamic environmental conditions in real-time. The proposed control system allows the cleaner robots, to enhance fuel efficiency, reduce CO2 emissions, and minimize the environmental impact of maritime transportation.


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    Titel :

    Self-Aware Control for Autonomous Underwater Vehicles


    Beteiligte:
    Decke, Jens (Autor:in) / Gerland, Florian (Autor:in) / Schomberg, Thomas (Autor:in) / Wunsch, Olaf (Autor:in) / Sick, Bernhard (Autor:in) / Gruhl, Christian (Autor:in)


    Erscheinungsdatum :

    16.09.2024


    Format / Umfang :

    214108 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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