Surface exploration and data collection by planetary rovers are challenging due to unknown complex planet terrains. This paper focuses on developing a Deep Reinforcement Learning (DRL)-based controller for rovers to enable safe operation. The necessary control input for safe and efficient vehicle maneuver is derived using the Control Barrier Function (CBF)-based safety protocols. Deep Deterministic Policy Gradient (DDPG) algorithm is used as a DRL framework to find the optimal exploration policies for the rover. Numerical simulations on different vehicle models show the efficacy of the proposed safety method for planetary rovers.


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    Titel :

    Safe Deep Reinforcement Learning-Based Controller (SDRLC) for Autonomous Navigation of Planetary Rovers


    Beteiligte:


    Erscheinungsdatum :

    22.07.2024


    Format / Umfang :

    1141665 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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