As space becomes more congested, on orbit inspection is an increasingly
relevant activity whether to observe a defunct satellite for planning repairs
or to de-orbit it. However, the task of on orbit inspection itself is
challenging, typically requiring the careful coordination of multiple observer
satellites. This is complicated by a highly nonlinear environment where the
target may be unknown or moving unpredictably without time for continuous
command and control from the ground. There is a need for autonomous, robust,
decentralized solutions to the inspection task. To achieve this, we consider a
hierarchical, learned approach for the decentralized planning of multi-agent
inspection of a tumbling target. Our solution consists of two components: a
viewpoint or high-level planner trained using deep reinforcement learning and a
navigation planner handling point-to-point navigation between pre-specified
viewpoints. We present a novel problem formulation and methodology that is
suitable not only to reinforcement learning-derived robust policies, but
extendable to unknown target geometries and higher fidelity information
theoretic objectives received directly from sensor inputs. Operating under
limited information, our trained multi-agent high-level policies successfully
contextualize information within the global hierarchical environment and are
correspondingly able to inspect over 90% of non-convex tumbling targets, even
in the absence of additional agent attitude control.

本研究提出了一种用于自主、鲁棒、去中心化的旋转目标多智能体检查的层次学习方法。采用深度强化学习训练高水平计划器和导航计划器处理点对点导航，对于未知目标几何形状和来自传感器输入的更高保真度的信息论目标，此方法可拓展至鲁棒策略，并在受限信息下成功地检查超过 90% 的非凸旋转目标。