Nesnas Issa A D, Hockman Benjamin J, Bandopadhyay Saptarshi, Morrell Benjamin J, Lubey Daniel P, Villa Jacopo, Bayard David S, Osmundson Alan, Jarvis Benjamin, Bersani Michele, Bhaskaran Shyam
Mobility and Robotics Systems Section, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States.
Mission Design and Navigation Section, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States.
Front Robot AI. 2021 Nov 1;8:650885. doi: 10.3389/frobt.2021.650885. eCollection 2021.
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 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 , 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.
自主性对于在不可预测环境中的机器人探索变得越来越重要。一个这样的例子是对小天体的接近、近距离操作和表面探测。在本文中,我们概述了一种估计框架,该框架将接近小天体并在其表面着陆作为自主小天体探测器的一项关键功能能力。我们使用一个多阶段感知/估计流程,该流程具有相互关联和重叠的测量与算法,以描述并抵达小天体,范围从数百万公里直至其表面。我们考虑一种概念性航天器设计,它在从接近小天体到着陆以及在微重力天体表面进行机动的所有阶段都能运行。这种小卫星设计做出了一些调整以简化自主表面操作。该估计流程将先进技术与新方法相结合,以在所有阶段估计目标的未知属性。质心算法和光曲线算法分别利用初始相对轨道的信息来估计小天体与航天器的相对轨迹和旋转。一种新的从轮廓提取形状的算法估计极点(即旋转轴)和初始视觉外壳,随着小天体在窄视场成像仪中变得更加清晰,该初始视觉外壳会为后续的特征跟踪提供种子。特征跟踪会细化小天体的极点方向和形状,以估计初始重力,从而实现安全的近距离接近。一种粗略形状重建算法用于识别初始可着陆区域,其危险性质随后将通过密集三维重建来评估。在着陆前,将对密集重建区域进行斜坡稳定性、热、遮挡和土力学危害的评估,并不断进行细化。我们模拟了一个接近假设小天体的任务场景,该小天体的运动和形状未知,起始距离为数千公里,直至20公里。结果表明,仅依靠机载测量和估计及其相关不确定性,且无需人工输入,就能恢复小天体的相对运动和形状。当前的工作继续使估计框架最后阶段的算法成熟并进行特性描述,以便在表面着陆。