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Analysis of Transportation Systems for Colonies on Mars

Author

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  • J. de Curtò

    (Department of Computer Applications in Science & Engineering, BARCELONA Supercomputing Center, 08034 Barcelona, Spain
    Informatik und Mathematik, GOETHE-University Frankfurt am Main, 60323 Frankfurt am Main, Germany
    Estudis d’Informàtica, Multimèdia i Telecomunicació, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
    Escuela Técnica Superior de Ingeniería (ICAI), Universidad Pontificia Comillas, 28015 Madrid, Spain)

  • I. de Zarzà

    (Informatik und Mathematik, GOETHE-University Frankfurt am Main, 60323 Frankfurt am Main, Germany
    Estudis d’Informàtica, Multimèdia i Telecomunicació, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
    Escuela Politécnica Superior, Universidad Francisco de Vitoria, Pozuelo de Alarcón, 28223 Madrid, Spain)

Abstract

The colonization of Mars poses unprecedented challenges in developing sustainable and efficient transportation systems to support inter-settlement connectivity and resource distribution. This study conducts a comprehensive evaluation of two proposed transportation systems for Martian colonies: a ground-based magnetically levitated (maglev) train and a low-orbital spaceplane. Through simulation models, we assess the energy consumption, operational and construction costs, and environmental impacts of each system. Monte Carlo simulations further provide insights into the cost variability and financial risk associated with each option over a decade. Our findings reveal that while the spaceplane system offers lower average costs and reduced financial risk, the maglev train boasts greater scalability and potential for integration with Martian infrastructural development. The maglev system, despite its higher initial cost, emerges as a strategic asset for long-term colony expansion and sustainability, highlighting the need for balanced investment in transportation technologies that align with the goals of Martian colonization. Further extending our exploration, this study introduces advanced analysis of alternative transportation technologies, including hyperloop systems, drones, and rovers, incorporating dynamic environmental modeling of Mars and reinforcement learning for autonomous navigation. In an effort to enhance the realism and complexity of our navigation simulation of Mars, we introduce several significant improvements. These enhancements focus on the inclusion of dynamic atmospheric conditions, the simulation of terrain-specific obstacles such as craters and rocks, and the introduction of a swarm intelligence approach for navigating multiple drones simultaneously. This analysis serves as a foundational framework for future research and strategic planning in Martian transportation infrastructure.

Suggested Citation

  • J. de Curtò & I. de Zarzà, 2024. "Analysis of Transportation Systems for Colonies on Mars," Sustainability, MDPI, vol. 16(7), pages 1-28, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:7:p:3041-:d:1370745
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    2. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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