This paper presents the development and evaluation of an optimization-based autonomous trajectory planning algorithm for the asteroid reconnaissance phase of a deep-space exploration mission. The reconnaissance phase is a low-altitude flyby to collect detailed information around a potential landing site. Although such autonomous deep-space exploration missions have garnered considerable interest recently, state-of-the-practice in trajectory design involves a time-intensive ground-based open-loop process that forward propagates multiple trajectories with a range of initial conditions and parameters to account for uncertainties in spacecraft knowledge and actuation. In this work, we introduce a stochastic trajectory optimization-based approach to generate trajectories that satisfy both the mission and spacecraft safety constraints during the reconnaissance phase of the Deep-space Autonomous Robotic Explorer (DARE) mission concept, which seeks to travel to and explore a near-Earth object autonomously, with minimal ground intervention. We first use the Multi-Spacecraft Concept and Autonomy Tool (MuSCAT) simulation framework to rigorously validate the underlying modeling assumptions for our trajectory planner and then propose a method to transform this stochastic optimal control problem into a deterministic one tailored for use with an off-the-shelf nonlinear solver. Finally, we demonstrate the efficacy of our proposed algorithmic approach through extensive numerical experiments and show that it outperforms the state-of-the-practice benchmark used for representative missions.

翻译：本文提出并评估了一种基于优化的自主轨迹规划算法，用于深空探测任务的小行星侦察阶段。侦察阶段旨在通过低空飞越收集潜在着陆点周围的详细信息。尽管此类自主深空探测任务近年来备受关注，但当前轨迹设计的实践方法仍采用耗时的地面开环流程，通过前向传播具有不同初始条件和参数的多个轨迹来应对航天器认知与执行机构的不确定性。本研究引入了一种基于随机轨迹优化的方法，为深空自主机器人探测器（DARE）任务概念的侦察阶段生成满足任务要求和航天器安全约束的轨迹。DARE任务旨在以最小地面干预自主前往并探索近地天体。我们首先使用多航天器概念与自主工具（MuSCAT）仿真框架严格验证轨迹规划器的底层建模假设，随后提出一种将随机最优控制问题转化为确定性问题的改进方法，使其适用于现成的非线性求解器。最后，通过大量数值实验证明了所提算法的有效性，并验证其性能优于当前代表性任务采用的基准方法。