Risk-Bounded; Goal Driven AI for Human Planetary Exploration

When planning an EVA; flight operations identifies goals and decides traverse paths and timelines given the constraints and risks of a mission. Our proposal will mature risk-bounded; goal-driven AI methods for generating mixed traverse and activity planning for crewed Lunar and Martian exploration.

Our risk-bounded; goal-driven approach builds off academic research on autonomous robotic exploration. For instance; autonomous underwater vehicles have used similar approaches onboard to develop plans; avoid obstacles; and complete science tasks. Recognizing that surface operations on the Moon and Cislunar and Mars necessitates consideration of many sources of risk; e.g.; life support; crater proximity; shadows; slope; comm. coverage; etc.; there is a need to model risk and quantify its impact. We will enable the use of risk as a resource in mission planning and decision support systems. To the best of our knowledge; no one has modeled combined sources of risk for surface EVA planning.

This is early/mid-stage (TRL 3) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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