OnAIR: Applications of the NASA On-Board Artificial Intelligence Research Platform

Authors

  • Evana Gizzi NASA Goddard Space Flight Center
  • Timothy Chase Jr NASA Goddard Space Flight Center
  • Christian Cassamajor-Paul Massachusetts Institute of Technology
  • Rachael Chertok University of Vermont
  • Lily Clough Aurora Engineering
  • Connor Firth Aurora Engineering
  • Alan Gibson NASA Goddard Space Flight Center
  • Ibrahim Haroon University of Massachusetts Lowell
  • James Marshall NASA Goddard Space Flight Center
  • Patrick Maynard NASA Goddard Space Flight Center
  • Michael Monaghan NASA Goddard Space Flight Center
  • Hayley Owens Tufts University
  • Daniel Rogers NASA Goddard Space Flight Center
  • Mahmooda Sultana NASA Goddard Space Flight Center
  • Jivko Sinapov Tufts University
  • Bethany Theiling NASA Goddard Space Flight Center

DOI:

https://doi.org/10.1609/aaai.v39i28.35156

Abstract

Infusing artificial intelligence algorithms into production aerospace systems can be challenging due to costs, timelines, and a risk-averse industry. We introduce the Onboard Artificial Intelligence Research (OnAIR) platform, an open-source software pipeline and cognitive architecture tool that enables full life cycle AI research for on-board intelligent systems. We begin with a description and user walk-through of the OnAIR tool. Next, we describe four use cases of OnAIR for both research and deployed onboard applications, detailing their use of OnAIR and the benefits it provided to the development and function of each respective scenario. We conclude with remarks on future work, future planned deployments, and goals for the forward progression of OnAIR as a tool to enable a larger AI and aerospace research community.
AAAI-25 / IAAI-25 / EAAI-25 Proceedings Cover

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Published

2025-04-11

How to Cite

Gizzi, E., Chase Jr, T., Cassamajor-Paul, C., Chertok, R., Clough, L., Firth, C., … Theiling, B. (2025). OnAIR: Applications of the NASA On-Board Artificial Intelligence Research Platform. Proceedings of the AAAI Conference on Artificial Intelligence, 39(28), 28893–28899. https://doi.org/10.1609/aaai.v39i28.35156

Issue

Vol. 39 No. 28: IAAI-25, EAAI-25, AAAI-25 Student Abstracts, Undergraduate Consortium and Demonstrations

Section

IAAI Technical Track on Deployed Innovative Tools for Enabling AI Applications