Velocity Estimation of a Descending Spacecraft in Atmosphereless Environment using Deep Learning 2024-26-0484

Landing of spacecraft on Lunar or Martian surfaces is the last and critical step in inter planetary space missions. The atmosphere on earth is thick enough to slow down the craft but Moon or Mars does not provide a similar atmosphere. Moreover, other factors such as lunar dust, availability of precise onboard navigational aids etc would impact decision making. Soft landing meaning controlling the velocity of the craft from over 6000km/h to zero. If the craft’s velocity is not controlled, it might crash. Various onboard sensors and onboard computing power play a critical role in estimating and hence controlling the velocity, in the absence of GPS-like navigational aids. In this paper, an attempt is made using visual onboard sensor to estimate the velocity of the object. The precise estimation of an object's velocity is a vital component in the trajectory planning of space vehicles, particularly those designed for descent onto lunar or Martian terrains, such as orbiters or landers. In this paper an effort is made to investigate the viability of velocity estimates through the utilization of pictures obtained from LROC cameras, which are part of a publicly available dataset released by Arizona State University. The dataset contains minimal images Additionally, the study investigates the use of Condition – Generative Adversarial Networks to generate synthetic data for the purpose of image-based velocity estimate, in order to support the proposed workflow. The forecast accuracy of the velocity of atmosphere-less objects is empirically examined on several deep learning models, and the final results are reported.