Data-Driven Guidance and Control for Asteroid Landing Based on Real-Time Dynamic Mode Decomposition

Abstract

Navigation and control near asteroids are challenging due to high levels of uncertainty, necessitating a high degree of autonomy and safety. To address this challenge, we propose a novel method for leveraging data-driven approaches, which have garnered significant attention in recent years. Traditional data-driven and machine learning techniques have been applied to space exploration problems; however, they often require large amounts of data in advance and are not robust to unknown environments. Hence, they are typically used in auxiliary roles and are not easily adaptable to real-time modeling and control. We propose the use of the theory of dynamic mode decomposition (DMD) and delayed embedding to construct a data-driven real-time guidance and control system for asteroid landings. Our approach employs low-dimensional information obtained from onboard sensors to construct nonlinear dynamics in the vicinity of asteroids. Based on the constructed dynamical model, we propose a guidance control strategy for accurate and reliable spacecraft landing. Our proposed method has the potential to improve guidance and control systems for space exploration by enabling real-time modeling and control with reduced computational cost.