Current Status of the LUMIO Autonomous Optical Navigation Experiment
Authors:   Politecnico Di Milano, Milan, Italy, Panicucci Paolo , Piccolo Felice , Rizza Antonio , Borgia Salvatore , Franzese Vittorio , Topputo Francesco 
Published: January 2024

Abstract

The Earth-Moon system is constantly bombarded by meteoroids of different sizes. They are fragments of asteroids, comets, and major celestial bodies which provide information about the formation and evolution of the Solar System. Meteor showers have been studied for at least 50 years

to construct Solar System meteoroid models which can be exploited to understand the spatial distribution of objects near the Earth-Moon system, to predict the degradation of spaceborne equipment, and to forecast large impact on Earth [1]. These models have been constructed by performing Earth-based observations of meteor showers and lunar flashes due to meteoroid impacts. As Earth-based observations are limited by geometrical, illumination, and meteorological constraints, a Moon-orbiting observatory could increase the quality and quantities of meteoroid impact detection to improve current meteoroid models. To answer these questions, the Lunar Meteoroid Impacts Observer (LUMIO) mission has been designed and is currently in development under ESA funding [2]. LUMIO is a CubeSat mission to a halo orbit at Earth-Moon L2 that shall observe, quantify, and characterize meteoroid impacts on the Lunar farside by detecting their flashes, complementing Earth-based observations on the Lunar nearside, to provide global information on the Lunar Meteoroid Environment and contribute to Lunar Situational Awareness [3, 4].

The LUMIO mission foresees two modes during its operative phase [5]: the Science Cycle and the Navigation & Engineering Cycle. The former lasts approximately 14 days and occurs when the Moon far side has the optimal illumination to perform flash detection (i.e., half of the Moon is not illuminated). The latter is defined as the complementary of the Science Cycle. During the Navigation & Engineering Cycle, the CubeSats cannot carry out scientific observations and performs reaction wheel desaturation, communication with the Earth, station-keeping, and technological demonstration.

The technological demonstration performed by LUMIO is the Optical Navigation Experiment (ONX) [6]. The ONX aims at proving the feasibility of CubeSats to autonomously navigate in the cislunar environment without communication with the ground by exploiting images of the Moon. Indeed, the spacecraft-Moon range is computed by determining the apparent size of the Moon in the image [7, 8]. This piece of information is then provided to the navigation filter to determine the spacecraft state [6].

The computation of the spacecraft-Moon range is determined by the image processing (IP) algorithm which determines the Moon limb location in the image at subpixel precision [9] and estimates the spacecraft-Moon range with a non-iterative method [10]. To make the algorithm robust to possible outliers in the limb location points, the RANSAC algorithm is used [11]. This IP pipeline provides position measurements to the navigation filter by exploiting attitude information from the ADCS subsystem. The navigation filter is an Extended Kalman Filter (EKF) in ECI J2000 taking into account the Moon and Earth gravitational attraction and the Sun third-body perturbation. This work presents the current status of the ONX by focusing on the image processing (IP) algorithm, the navigation filter, and the expected results during the experiment.

Acknowledgment

This work has been conducted under ESA Contract No. 4000139301/22/NL/AS within the General Support Technology Programme (GSTP) through the support of the national delegations of Italy (ASI) and Norway (NOSA). The authors would like to acknowledge the members of the LUMIO team for their support and the ESA experts for reviewing the Phases 0 and A design.

References

[1] Z. Ceplecha, J. Borovička, W. G. Elford, D. O. ReVelle, R. L. Hawkes, V. Porubčan, and M. Šimek. Meteor phenomena and bodies. Space Science Reviews, 84(3):327–471, 1998. doi: 10.1023/A:1005069928850.

[2] A. Cervone, F. Topputo, S. Speretta, A. Menicucci, E. Turan, P. Di Lizia, M. Massari, V. Franzese, C. Giordano, G. Merisio, D. Labate, G. Pilato, E. Costa, E. Bertels, A. Thorvaldsen, A. Kukharenka, J. Vennekens, and R. Walker. LUMIO: A CubeSat for observing and characterizing micro-meteoroid impacts on the Lunar far side. Acta Astronautica, 195:309–317, 2022. doi: 10.1016/j.actaastro.2022.03.032.

[3] F. Topputo, G. Merisio, V. Franzese, C. Giordano, M. Massari, G. Pilato, D. Labate, A. Cervone, S. Speretta, A. Menicucci, E. Turan, E. Bertels, J. Vennekens, R. Walker, and D. Koschny. Meteoroids detection with the LUMIO lunar CubeSat. Icarus, 389:115213, 2023. doi: 10.1016/j.icarus.2022.115213.

[4] G. Merisio and F. Topputo. Present-day model of lunar meteoroids and their impact flashes for LUMIO mission. Icarus, 389:115180, 2023. doi: 10.1016/j.icarus.2022.115180.

[5] A. M. Cipriano, D. A. Dei Tos, and F. Topputo. Orbit design for LUMIO: The lunar meteoroid impacts observer. Frontiers in Astronomy and Space Sciences, 5:29, 2018. doi: 10.3389/fs-pas.2018.00029.

[6] V. Franzese, P. Di Lizia, and F. Topputo. Autonomous optical navigation for the lunar meteoroid impacts observer. Journal of Guidance, Control, and Dynamics, 42(7):1579–1586, 2019. doi: 10.2514/1.G003999.

[7] D. Mortari, C. N. D’Souza, and R. Zanetti. Image processing of illuminated ellipsoid. Journal of Spacecraft and Rockets, 53(3):448–456, 2016. doi: 10.2514/1.A33342.

[8] J. A. Christian. Optical navigation using planet’s centroid and apparent diameter in image. Journal of guidance, control, and dynamics, 38(2):192–204, 2015. doi: 10.2514/1.G000872.

[9] J. A. Christian. Accurate planetary limb localization for image-based spacecraft navigation. Journal of Spacecraft and Rockets, 54(3):708–730, 2017. doi: 10.2514/1.A33692.

[10] John A Christian and Shane B Robinson. Noniterative Horizon-Based optical navigation by Cholesky factorization. Journal of Guidance, Control, and Dynamics, 39(12):2757–2765, 2016. doi: 10.2514/1.G000539.

[11] R. Hartley and A. Zisserman. Multiple view geometry in computer vision. Cambridge university press, 2003.