AI Space Cortex: An Experimental System for Future Era Space Exploration

TL;DR

This paper presents REASIMO, an AI-assisted autonomous system designed for future ocean world space missions, capable of anomaly detection, recovery, and executing pre-trained behaviors to overcome communication delays and harsh conditions.

Contribution

It introduces a novel AI-driven autonomy framework for ocean world exploration, enabling onboard decision-making without reliance on Earth-based control.

Findings

Successful autonomous sampling tests on a lander-manipulator testbed.

Demonstrated anomaly detection and recovery capabilities.

Validated robustness of AI-assisted control in simulated surface conditions.

Abstract

Our Robust, Explainable Autonomy for Scientific Icy Moon Operations (REASIMO) effort contributes to NASA's Concepts for Ocean worlds Life Detection Technology (COLDTech) program, which explores science platform technologies for ocean worlds such as Europa and Enceladus. Ocean world missions pose significant operational challenges. These include long communication lags, limited power, and lifetime limitations caused by radiation damage and hostile conditions. Given these operational limitations, onboard autonomy will be vital for future Ocean world missions. Besides the management of nominal lander operations, onboard autonomy must react appropriately in the event of anomalies. Traditional spacecraft rely on a transition into 'safe-mode' in which non-essential components and subsystems are powered off to preserve safety and maintain communication with Earth. For a severely time-limited Ocean world mission, resolutions to these anomalies that can be executed without Earth-in-the-loop communication and associated delays are paramount for completion of the mission objectives and science goals. To address these challenges, the REASIMO effort aims to demonstrate a robust level of AI-assisted autonomy for such missions, including the ability to detect and recover from anomalies, and to perform missions based on pre-trained behaviors rather than hard-coded, predetermined logic like all prior space missions. We developed an AI-assisted, personality-driven, intelligent framework for control of an Ocean world mission by combining a mix of advanced technologies. To demonstrate the capabilities of the framework, we perform tests of autonomous sampling operations on a lander-manipulator testbed at the NASA Jet Propulsion Laboratory, approximating possible surface conditions such a mission might encounter.