Mark Stephenson, University of Colorado Boulder; Hanspeter Schaub, University of Colorado Boulder
Keywords: inspection, reinforcement learning, multi-agent, autonomy, rpo
Abstract:
The close-proximity inspection of objects in low Earth orbit is important to operations such as rendezvous, debris removal, servicing, and resident space object (RSO) characterization, all which are of increasing interest to commercial and government organizations. Complex relative motion dynamics in low Earth orbit make the problem of path planning for autonomous multi-agent inspection challenging. Agents must be able to fully inspect an object subject to illumination constraints while avoiding collision with the RSO or—in the multi-agent case—each other. In this paper, autonomous satellite inspection with impulsive maneuvers is considered by learning a policy on a multi-agent semi-Markov decision process formulation of the inspection task while ensuring safety via an optimization-based shield for collision avoidance based on analytical equations of relative motion. This work demonstrates closed-loop, autonomous, safe multi-agent inspection of an RSO with shielded deep reinforcement learning over all low Earth orbit (LEO) orbits.
Date of Conference: September 16-19, 2025
Track: Machine Learning for SDA Applications