  The earth's orbit is becoming increasingly crowded with debris that poses
significant safety risks to the operation of existing and new spacecraft and
satellites. The active tether-net system, which consists of a flexible net with
maneuverable corner nodes launched from a small autonomous spacecraft, is a
promising solution for capturing and disposing of such space debris. The
requirement of autonomous operation and the need to generalize over scenarios
with debris scenarios in different rotational rates makes the capture process
significantly challenging. The space debris could rotate about multiple axes,
which, along with sensing/estimation and actuation uncertainties, calls for a
robust, generalizable approach to guiding the net launch and flight - one that
can guarantee robust capture. This paper proposes a decentralized actuation
system combined with reinforcement learning for planning and controlling this
tether-net system. In this new system, four microsatellites with cold gas type
thrusters act as the corner nodes of the net and can thus help control or
correct the flight of the net after launch. The microsatellites pull the net to
complete the task of approaching and capturing the space debris. The proposed
method uses a RL framework that integrates a proximal policy optimization to
find the optimal solution based on the dynamics simulation of the net and the
microsatellites performed in Vortex Studio. The RL framework finds the optimal
trajectory that is both fuel-efficient and ensures a desired level of capture
quality.
