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.

翻译：随着太空日益拥挤，在轨检测已成为一项越来越重要的活动，无论是为了观测失效卫星以规划维修，还是使其脱离轨道。然而，在轨检测任务本身具有挑战性，通常需要多颗观测卫星的精细协调。高度非线性环境进一步增加了复杂性，其中目标可能未知或不可预测地运动，且没有时间进行连续的地面指挥与控制。因此需要自主、鲁棒、分布式的检测任务解决方案。为此，我们提出了一种分层学习式方法，用于实现针对翻滚目标的多智能体分布式检测规划。我们的解决方案包含两个组件：使用深度强化学习训练的视点或高层规划器，以及处理预设视点间点对点导航的导航规划器。我们提出了一种新颖的问题表述和方法论，不仅适用于基于强化学习的鲁棒策略，还可扩展至未知目标几何形状以及直接从传感器输入接收的高保真信息论目标。在有限信息条件下，我们训练的多智能体高层策略成功地在全局分层环境中上下文化信息，并相应能够检测超过90%的非凸翻滚目标，即使在缺少额外智能体姿态控制的情况下也是如此。