Autonomous Exploration of Small Bodies Toward Greater Autonomy for Deep Space Missions - 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 (Frontiers in Robotics and AI, 2021)
Most small-body missions have used some level of autonomy, but all operated within narrow windows and under several constraints, with extensive human-in-the-loop. Prior work has demonstrated autonomous cruise capabilities. In this article, we focus on the approach and landing, which continue to be challenging and costly. We present an overview of an estimation framework as a key element of an architecture that could lead to a more autonomous access to small bodies. Such a capability offers an opportunity to mature perception-rich, action-rich autonomy for operating in environments dominated by large uncertainties but using simpler robotic systems: namely, SmallSats. Our approach uses a multi-phase perception/estimation pipeline with interconnected and overlapping measurements and algorithms to characterize and autonomously 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 maneuvering on a small body. The spacecraft design makes accommodations to simplify autonomous surface operations. The pipeline leverages state-of-the-practice centroid and light-curve algorithms to estimate body-spacecraft relative trajectory and rotation, using a priori knowledge of the body's initial orbit and associated uncertainty from radiometric measurements. It then uses shape-from-silhouette to estimate the pole (i.e. rotation axis) and 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 using dense 3D reconstruction. Stability, thermal, occlusion, and surface-properties 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 are unknown a priori, starting from thousands of kilometers down to 20 km. Results indicate the feasibility of recovering body motion and shape through coarse shape matching relying solely on onboard measurements. Current work continues to mature and characterize algorithms for the last phases of the estimation framework to land on the surface.