MAARS: Machine learning-based Analytics for Automated Rover Systems

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  • Additional Information
    • Publication Information:
      IEEE
    • Publication Date:
      2020
    • Abstract:
      MAARS (Machine leaning-based Analytics for Automated Rover Systems) is an ongoing JPL effort to bring the latest self-driving technologies to Mars, Moon, and beyond. The ongoing AI revolution here on Earth is finally propagating to the red planet as the High Performance Spaceflight Computing (HPSC) and commercial off-the-shelf (COTS) system-on-a-chip (SoC), such as Qualcomm's Snapdragon, become available to rovers. In this three year project, we are developing, implementing, and benchmarking a wide range of autonomy algorithms that would significantly enhance the productivity and safety of planetary rover missions. This paper is to provide the latest snapshot of the project with broad and high-level description of every capability that we are developing, including scientific scene interpretation, vision-based traversability assessment, resource-aware path planning, information-theoretic path planning, on-board strategic path planning, and on-board optimal kinematic settling for accurate collision checking. All of the onboard software capabilities will be integrated into JPL's Athena test rover using ROS (Robot Operating System).
    • Contents Note:
      Conference Acronym: AERO
    • Author Affiliations:
      Jet Propulsion Laboratory, California Institute of Technology
      University of Toronto
      Carnegie Mellon University
      University of Tokyo
      Georgia Tech
      West Virginia University
      NC A&T State University
    • ISBN:
      978-1-7281-2734-7
    • Relation:
      2020 IEEE Aerospace Conference
    • Accession Number:
      10.1109/AERO47225.2020.9172271
    • Rights:
      Copyright 2020, IEEE
    • AMSID:
      9172271
    • Conference Acronym:
      AERO
    • Date of Current Version:
      2020
    • Document Subtype:
      IEEE Conference
    • Notes:
      Conference Location: Big Sky, MT, USA, USA

      Conference Start Date: 7 March 2020

      Conference End Date: 14 March 2020
    • Accession Number:
      edseee.9172271
  • Citations
    • ABNT:
      ONO, M. et al. MAARS: Machine learning-based Analytics for Automated Rover Systems. 2020 IEEE Aerospace Conference, Aerospace Conference, 2020 IEEE, [s. l.], p. 1–17, 2020. DOI 10.1109/AERO47225.2020.9172271. Disponível em: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edseee&AN=edseee.9172271. Acesso em: 24 nov. 2020.
    • AMA:
      Ono M, Rothrock B, Otsu K, et al. MAARS: Machine learning-based Analytics for Automated Rover Systems. 2020 IEEE Aerospace Conference, Aerospace Conference, 2020 IEEE. March 2020:1-17. doi:10.1109/AERO47225.2020.9172271
    • APA:
      Ono, M., Rothrock, B., Otsu, K., Higa, S., Iwashita, Y., Didier, A., Islam, T., Laporte, C., Sun, V., Stack, K., Sawoniewicz, J., Daftry, S., Timmaraju, V., Sahnoune, S., Mattmann, C. A., Lamarre, O., Ghosh, S., Qiu, D., Nomura, S., … Park, H. (2020). MAARS: Machine learning-based Analytics for Automated Rover Systems. 2020 IEEE Aerospace Conference, Aerospace Conference, 2020 IEEE, 1–17. https://doi.org/10.1109/AERO47225.2020.9172271
    • Chicago/Turabian: Author-Date:
      Ono, Masahiro, Brandon Rothrock, Kyohei Otsu, Shoya Higa, Yumi Iwashita, Annie Didier, Tanvir Islam, et al. 2020. “MAARS: Machine Learning-Based Analytics for Automated Rover Systems.” 2020 IEEE Aerospace Conference, Aerospace Conference, 2020 IEEE, March, 1–17. doi:10.1109/AERO47225.2020.9172271.
    • Harvard:
      Ono, M. et al. (2020) ‘MAARS: Machine learning-based Analytics for Automated Rover Systems’, 2020 IEEE Aerospace Conference, Aerospace Conference, 2020 IEEE, pp. 1–17. doi: 10.1109/AERO47225.2020.9172271.
    • Harvard: Australian:
      Ono, M, Rothrock, B, Otsu, K, Higa, S, Iwashita, Y, Didier, A, Islam, T, Laporte, C, Sun, V, Stack, K, Sawoniewicz, J, Daftry, S, Timmaraju, V, Sahnoune, S, Mattmann, CA, Lamarre, O, Ghosh, S, Qiu, D, Nomura, S, Roy, H, Sarabu, H, Hedrick, G, Folsom, L, Suehr, S & Park, H 2020, ‘MAARS: Machine learning-based Analytics for Automated Rover Systems’, 2020 IEEE Aerospace Conference, Aerospace Conference, 2020 IEEE, pp. 1–17, viewed 24 November 2020, .
    • MLA:
      Ono, Masahiro, et al. “MAARS: Machine Learning-Based Analytics for Automated Rover Systems.” 2020 IEEE Aerospace Conference, Aerospace Conference, 2020 IEEE, Mar. 2020, pp. 1–17. EBSCOhost, doi:10.1109/AERO47225.2020.9172271.
    • Chicago/Turabian: Humanities:
      Ono, Masahiro, Brandon Rothrock, Kyohei Otsu, Shoya Higa, Yumi Iwashita, Annie Didier, Tanvir Islam, et al. “MAARS: Machine Learning-Based Analytics for Automated Rover Systems.” 2020 IEEE Aerospace Conference, Aerospace Conference, 2020 IEEE, March 1, 2020, 1–17. doi:10.1109/AERO47225.2020.9172271.
    • Vancouver/ICMJE:
      Ono M, Rothrock B, Otsu K, Higa S, Iwashita Y, Didier A, et al. MAARS: Machine learning-based Analytics for Automated Rover Systems. 2020 IEEE Aerospace Conference, Aerospace Conference, 2020 IEEE [Internet]. 2020 Mar 1 [cited 2020 Nov 24];1–17. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edseee&AN=edseee.9172271