Abstract In-situ Resource Utilisation (ISRU) is considered necessary to enable Off-Earth settlement. It is also regularly compared with terrestrial mining operations. Optimisation of the resource extraction sequence and cut-off grades for product and waste streams are critical for both terrestrial mining operations and ISRU. However, the traditional methods used in the terrestrial mining industry are not directly compatible with ISRU. This paper outlines the differences between terrestrial mining and ISRU and develops a new method for ISRU planning based on Reinforcement Learning (RL). An RL agent is trained and evaluated for extraction sequencing, sometimes showing the ability to outperform a human expert. The generalised RL agent can also be used to run multiple scenarios to determine optimal cut-off grades and conduct risk analysis on varying geological and equipment reliability inputs. Future ISRU projects and assessments will benefit from this method by reducing the human effort required to achieve production optimality.

    Highlights Application of Reinforcement Learning to lunar resource extraction sequencing. A new method of mine planning and evaluation for resources the Moon. A new method of determining In-Situ Resource Utilisation cut-off grades.


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    Titel :

    Planning lunar In-Situ Resource Utilisation with a reinforcement learning agent


    Beteiligte:
    Pelech, T. (Autor:in) / Yao, L. (Autor:in) / Saydam, S. (Autor:in)

    Erschienen in:

    Acta Astronautica ; 201 ; 401-419


    Erscheinungsdatum :

    2022-09-16


    Format / Umfang :

    19 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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