Optimality Principles in Spacecraft Neural Guidance and Control
Dario Izzo Emmanuel Blazquez Robin Ferede Sebastien Origer Christophe De Wagter Guido C. H. E. de Croon
Spacecraft and drones aimed at exploring our solar system are designed to
operate in conditions where the smart use of onboard resources is vital to the
success or failure of the mission. Sensorimotor actions are thus often derived
from high-level, quantifiable, optimality principles assigned to each task,
utilizing consolidated tools in optimal control theory. The planned actions are
derived on the ground and transferred onboard where controllers have the task
of tracking the uploaded guidance profile. Here we argue that end-to-end neural
guidance and control architectures (here called G&CNets) allow transferring
onboard the burden of acting upon these optimality principles. In this way, the
sensor information is transformed in real time into optimal plans thus
increasing the mission autonomy and robustness. We discuss the main results
obtained in training such neural architectures in simulation for interplanetary
transfers, landings and close proximity operations, highlighting the successful
learning of optimality principles by the neural model. We then suggest drone
racing as an ideal gym environment to test these architectures on real robotic
platforms, thus increasing confidence in their utilization on future space
exploration missions. Drone racing shares with spacecraft missions both limited
onboard computational capabilities and similar control structures induced from
the optimality principle sought, but it also entails different levels of
uncertainties and unmodelled effects. Furthermore, the success of G&CNets on
extremely resource-restricted drones illustrates their potential to bring
real-time optimal control within reach of a wider variety of robotic systems,
both in space and on Earth.