What question did this study set out to answer?

The aim is to develop a capstone demonstration framework that integrates technologies for autonomous mission operations in deep-space habitats.

May 9, 2026Open Access

A Development Framework for a Comprehensive Capstone which Demonstrates Human Interaction with Autonomous Habitat Technology

Key Points

The aim is to develop a capstone demonstration framework that integrates technologies for autonomous mission operations in deep-space habitats.
Developed system architecture encompassing various advanced technologies including machine learning and fault interaction.
Conducted a scenario involving a crew approaching an uncrewed habitat with autonomous systems.
Tested root cause analysis and system response to anomalies via a digital twin.
The deep-space habitat detected anomalous behavior in subsystems, alerting the crew with root cause analysis.
The crew safely managed potentially critical systems remotely before docking, ensuring operational safety.
Post-docking, the crew manually fixed the identified issues, restoring the habitat to nominal operations.

Abstract

Future deep-space missions will require crews to operate more autonomously due to the increased distance from Earth, which results in communications delay and resupply scarcity. Specific flight phases (crewed or uncrewed) and time critical situations, where Earth-based support is impossible, highlight the critical need for the crew and habitat to operate independently from Earth. The Habitats Optimized for Missions of Exploration (HOME) Space Technology Research Institute (STRI) addresses some of the challenges by developing and advancing mission enabling habitat technologies. The HOME STRI encompasses researchers from seven universities that are spread across five research-thrusts, providing novel contributions in the area of vehicle functional design, self-awareness, human-autonomy-teaming, vehicle self-sufficiency, and digital twin technology. This paper presents the development framework of a capstone demonstration which aims to integrate several technologies needed for autonomous fault management mission operations. We present a system architecture that contains various HOME-developed technologies which include; machine learning and artificial intelligence, adaptive modes of autonomy, a decentralized system architecture, a digital twin integration concept, and subsystem fault interaction. The demonstrated mission scenario begins with an uncrewed deep space habitat, operating nominally, with a computational system with incorporated machine learning and artificial intelligence in-training to learn system inter-dependencies while uploading its current knowledge to a digital twin. As a new crew approaches, anomalous behavior in the EPS and the ECLSS subsystems is detected by the deep space habitat. The crew is alerted and provided with root cause analysis. Using this knowledge, the crew correspondingly safe the system remotely prior to docking. Upon arrival to the habitat, the root cause is manually fixed by crew, and the habitat returns to nominal operations. This capstone integrates twelve research projects, highlighting the complex and interconnected nature of detecting faults and how crews interact with habitats that have autonomous elements.

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