This paper presents a robust path-planning framework for safe spacecraft autonomy under uncertainty and develops a computationally tractable formulation based on convex programming. We utilize chance-constrained control to formulate the problem. It provides a mathematical framework to solve for a sequence of control policies that minimizes a probabilistic cost under probabilistic constraints with a user-defined confidence level (e.g., safety with 99.9% confidence). The framework enables the planner to directly control state distributions under operational uncertainties while ensuring the vehicle safety. This paper rigorously formulates the safe autonomy problem, gathers and extends techniques in literature to accommodate key cost/constraint functions that often arise in spacecraft path planning, and develops a tractable solution method. The presented framework is demonstrated via two representative numerical examples: safe autonomous rendezvous and orbit maintenance in cislunar space, both under uncertainties due to navigation error from Kalman filter, execution error via Gates model, and imperfect force models.

翻译：本文提出了一种面向不确定性条件下航天器自主安全的鲁棒路径规划框架，并基于凸规划发展了计算可处理的求解方法。我们采用机会约束控制对问题进行建模，该数学框架可求解在用户指定置信水平（例如99.9%置信度下的安全性）的概率约束条件下，最小化概率代价的控制策略序列。该框架使规划器能够直接控制运行不确定性下的状态分布，同时确保航天器安全性。本文对安全自主规划问题进行了严格数学表述，系统梳理并拓展了文献中用于处理航天器路径规划中常见代价/约束函数的相关技术，进而发展出一种可求解的方法。通过两个典型数值算例验证了所提框架的有效性：地月空间的安全自主交会对接与轨道维持任务，两者均面临由卡尔曼滤波导航误差、Gates模型执行误差及非理想力场模型所导致的不确定性。