Our project is about designing a machine-learning-based routing model to optimize communication links within a heterogeneous satellite network. It is designed to accomodate variables such as delay, signal quality, and available compute on the satellite nodes. To validate this model, we constructed a Hardware-in-the-Loop testbed using robotic vehicles to emulate real-world conditions the model might face.