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An Autonomous Reinforcement Learning Framework for Fault Recovery and Mission Replanning on CubeSats

Overview

• CubeSat missions face a persistent challenge: failures happen. Often.
• From 2000 to 2015, over two-thirds of CubeSat missions that made it to orbit experienced some kind of failure.
• The reasons = varied—limited development budgets, ambitious designs, and a lack of robust testing procedures.
• Interestingly, most failures don't happen right out of the gate. They occur in-flight, when the CubeSat works fine one moment, and fails the next.
• This raises an important question: How do we build fault recovery systems that are autonomous, adaptable, and reusable across missions?

This framework is our answer. It's designed to take the burden off CubeSat developers by automating fault recovery and mission replanning. At its core, it uses reinforcement learning (RL)—a way for the CubeSat to "learn" how to adapt on its own when things go wrong.

The Problem We Set Out to Solve

The status quo for fault recovery systems isn’t great. Existing approaches either rely on human intervention (like manually rescheduling the mission plan after detecting a fault) or use redundant hardware to patch over issues. Both have their limits. Human intervention is slow and often impractical during orbit. Redundancy helps only if small-scale faults occur, and even then, it’s costly and resource-intensive. These solutions also focus too narrowly—fixing the fault itself without considering how it impacts the broader mission.

Our goal was to create a system that didn’t just react to faults but planned for them. We wanted something that could:

• Automatically reschedule tasks based on available resources post-fault.
• Optimize task selection to prioritize the most critical mission objectives.
• Run on low-power CubeSat hardware, which meant it needed to be lightweight and efficient.

Come read more in this documentation page:

Autonomous Learning for Fault Recovery and Mission Replanning