DataSheet1_Autonomous Exploration of Small Bodies Toward Greater Autonomy for Deep Space Missions.pdf

Autonomy is becoming increasingly important for the robotic exploration of unpredictable environments. One such example is the approach, proximity operation, and surface exploration of small bodies. In this article, we present an overview of an estimation framework to approach and land on small bodies as a key functional capability for an autonomous small-body explorer. We use a multi-phase perception/estimation pipeline with interconnected and overlapping measurements and algorithms to characterize and reach the body, from millions of kilometers down to its surface. We consider a notional spacecraft design that operates across all phases from approach to landing and to maneuvering on the surface of the microgravity body. This SmallSat design makes accommodations to simplify autonomous surface operations. The estimation pipeline combines state-of-the-art techniques with new approaches to estimating the target’s unknown properties across all phases. Centroid and light-curve algorithms estimate the body–spacecraft relative trajectory and rotation, respectively, using a priori knowledge of the initial relative orbit. A new shape-from-silhouette algorithm estimates the pole (i.e., rotation axis) and the initial visual hull that seeds subsequent feature tracking as the body gets more resolved in the narrow field-of-view imager. Feature tracking refines the pole orientation and shape of the body for estimating initial gravity to enable safe close approach. A coarse-shape reconstruction algorithm is used to identify initial landable regions whose hazardous nature would subsequently be assessed by dense 3D reconstruction. Slope stability, thermal, occlusion, and terra-mechanical hazards would be assessed on densely reconstructed regions and continually refined prior to landing. We simulated a mission scenario for approaching a hypothetical small body whose motion and shape were unknown a priori, starting from thousands of kilometers down to 20 km. Results indicate the feasibility of recovering the relative body motion and shape solely relying on onboard measurements and estimates with their associated uncertainties and without human input. Current work continues to mature and characterize the algorithms for the last phases of the estimation framework to land on the surface.

自主能力在未知环境的机器人探测任务中愈发关键。其中一类典型场景为小天体 (small bodies) 的接近、近距操作与表面探测。本文针对自主小天体探测器的核心功能需求，综述了一套用于小天体接近与着陆的估计框架。我们采用互联且测量、算法相互重叠的多阶段感知-估计流水线，实现从数百万公里外直至小天体表面的小天体特性表征与抵近探测。本文考虑一种可覆盖从接近、着陆直至微重力天体表面机动全阶段的概念性航天器设计，该小卫星 (SmallSat) 设计通过适应性优化简化了自主表面作业流程。该估计流水线将前沿技术与全新方法相结合，用于全阶段估计目标的未知特性。质心与光变曲线算法分别利用初始相对轨道的先验知识，估计小天体与航天器的相对轨迹及自转状态。一种全新的轮廓法形状重建 (shape-from-silhouette) 算法可估计极轴（即自转轴）与初始视觉壳，当窄视场成像仪对小天体的分辨率逐步提升时，该视觉壳可为后续特征跟踪提供初始依据。特征跟踪可优化极轴指向与小天体形状，用于估计初始引力场，以实现安全抵近。粗形状重建算法可识别初始可着陆区域，后续将通过密集三维重建评估这些区域的危险性。针对密集重建区域，将对坡度稳定性、热环境、遮挡情况以及地表力学特性等风险隐患进行评估，并在着陆前持续优化这些评估结果。我们模拟了一套任务场景：从数千公里外逐步抵近一颗先验运动与形状均未知的假想小天体，直至距离其20公里处。实验结果表明，仅依靠星上测量与带相关不确定性的估计结果、无需人工干预即可恢复小天体的相对运动与形状，该方案具备可行性。目前研究仍在持续优化该估计框架着陆阶段的相关算法，并对其性能进行表征。