  As spacecraft journey further from Earth with more complex missions, systems
of greater autonomy and onboard intelligence are called for. Reducing reliance
on human-based mission control becomes increasingly critical if we are to
increase our rate of solar-system-wide exploration. Recent work has explored
AI-based goal-oriented systems to increase the level of autonomy in mission
execution. These systems make use of symbolic reasoning managers to make
inferences from the state of a spacecraft and a handcrafted knowledge base,
enabling autonomous generation of tasks and re-planning. Such systems have
proven to be successful in controlled cases, but they are difficult to
implement as they require human-crafted ontological models to allow the
spacecraft to understand the world. Reinforcement learning has been applied to
train robotic agents to pursue a goal. A new architecture for autonomy is
called for. This work explores the application of Large Language Models (LLMs)
as the high-level control system of a spacecraft. Using a systems engineering
approach, this work presents the design and development of an agentic
spacecraft controller by leveraging an LLM as a reasoning engine, to evaluate
the utility of such an architecture in achieving higher levels of spacecraft
autonomy. A series of deep space mission scenarios simulated within the popular
game engine Kerbal Space Program (KSP) are used as case studies to evaluate the
implementation against the requirements. It is shown the reasoning and planning
abilities of present-day LLMs do not scale well as the complexity of a mission
increases, but this can be alleviated with adequate prompting frameworks and
strategic selection of the agent's level of authority over the host spacecraft.
This research evaluates the potential of LLMs in augmenting autonomous
decision-making systems for future robotic space applications.
