A Practical Framework for Incremental Testing and Technology Maturation of Spacecraft Autonomy

Abstract

This paper addresses the "trust gap" in deploying AI-enabled spacecraft autonomy through an integrated verification framework combining offline ground testing with online flight monitoring. The offline phase progresses systematically through simulation studies, processor-in-the-loop (PIL), hardware-in-the-loop (HIL), and robotic emulation testing, culminating in flight validation. The online phase employs AI watchdog systems for performance monitoring and safety guardrails for real-time constraint enforcement. Key contributions include: (1) systematic organization of verification techniques emphasizing closed-loop behavioral testing at each phase, (2) integration of AI watchdogs with runtime assurance, and (3) demonstration of the framework through a reinforcement learning-based attitude control case study. This approach integrates mature technologies to provide a viable pathway for safe deployment of AI-enabled autonomy in high-consequence space operations while addressing simulation-to-reality transfer challenges.