用安全强化学习规划航天器在轨检查观测点序列
Chance-Constrained Inspection Viewpoint Sequencing for On-Orbit Servicing via Safe Reinforcement Learning
欧洲团队发的新论文，把在轨检查的视角规划做成带机会约束的强化学习，违规率比传统固定裕度方法砍掉一半以上。
论文将在轨服务中的近距离观测点规划建模为约束马尔可夫决策过程（CMDP），用 Proximal Policy Optimization 配合拉格朗日松弛求解。系统由高层规划器在目标周围的观测球面上排列相机视点，底层模型用 Clohessy--Wiltshire（CW）方程评估每次转移的单脉冲可达性与 Δv 上限。针对导航和机动误差导致的安全裕度随机化问题，引入机会约束，并用 PID 控制拉格朗日乘子在训练中自适应调整惩罚。在中地球轨道一颗 Galileo 级导航卫星上的测试显示，在相近覆盖率下，违规率不到固定裕度确定性规划的一半，且无需逐案重新调参。
Chance-Constrained Inspection Viewpoint Sequencing for On-Orbit Servicing via Safe Reinforcement Learning
Autonomous inspection of a target spacecraft is typically the initial step in on-orbit servicing. Planning close-proximity observation viewpoints involves a complex combinatorial problem constrained by coverage, fuel, and safety considerations. This paper models the viewpoint selection as a Constrained Markov Decision Process (CMDP) and applies Proximal Policy Optimization with Lagrangian relaxation to solve it. The system combines a high-level planner, which sequences camera viewpoints on an observation sphere around the target, with a low-level model that assesses each transfer using the Clohessy--Wiltshire (CW) equations for relative motion. The deterministic constraints on single-impulse reachability and $Δv$ cap are enforced directly on the action set. Safety margins are affected by navigation and manoeuvre errors, making the actual clearance stochastic. To address this, a chance constraint on the realised trajectory is implemented, with a Lagrangian multiplier governed by PID control to adapt the penalty during training. The approach is tested on a Galileo-class navigation satellite in medium Earth orbit, modelled as a point cloud where visibility from each viewpoint considers field-of-view and line-of-sight occlusion. Across various risk budgets, the trained policy maintains the keep-out violation rate within the specified limit, achieving coverage close to its maximum and staying within the mission $Δv$ budget. Compared to the usual practice of applying a fixed margin to the keep-out radius and planning deterministically, the chance-constrained policy attains less than half the violation rate at comparable coverage. This makes the collision risk an explicit design parameter, eliminating the need for re-tuning per case. The learned policy selects a viewpoint without the repeated Monte Carlo risk estimates required by a greedy, chance-constrained planner at every decision point.
