A Simulation-Based Observation Planning Tool for the Máni Lunar Orbiter under Pointing Uncertainty
Authors
Manich, Alexander Fredberg ; Risager, Mads Sloth
Term
4. term
Education
Publication year
2026
Submitted on
2026-06-04
Pages
16
Abstract
Máni is a European Space Agency lunar orbiter that will map the Moon’s surface using multi-angular photoclinometric imaging—a method that uses images from many angles to better understand the surface. Unlike typical mapping missions that take one image per area, Máni must collect at least five overlapping images of each target, across a wide range of viewing angles, and repeat the imaging during two overflights separated by about one lunar sidereal period to capture different illumination. Whether a candidate orbit can deliver this depends on the combined effects of the orbit, which sets the viewing geometry, and spacecraft pointing uncertainty, which shifts each image’s footprint away from its intended location. This interaction has not yet been quantified for Máni, and existing tools treat the two factors separately. We built a simulation module that, for any candidate orbit, derives the observation geometry over a region of interest, schedules image acquisitions via an exchangeable scheduler, and uses Monte Carlo simulation (many randomized trials) to estimate the probability that the five-image overlap requirement is met under a configurable pointing error model. We exercise the module across varied orbital configurations, region layouts, error models, and scheduling assumptions. The results indicate that it responds meaningfully to its inputs: it distinguishes orbits with similar overlap but different angular diversity, shows how target spacing and the number of overflights govern region completion, and, by reporting coverage as a distribution rather than a single value, makes worst-case outcomes visible to mission planners.
Máni er en ESA-månesatellit, der vil kortlægge Månens overflade med multivinklet fotoklinometrisk billeddannelse – en metode, der bruger billeder fra mange vinkler for bedre at forstå overfladen. I modsætning til typiske kortlægningsmissioner, som tager ét billede pr. område, skal Máni tage mindst fem overlappende billeder af hvert mål, fra vidt forskellige vinkler, og gentage optagelserne under to overflyvninger adskilt af cirka én lunar siderisk periode for at fange forskellige lysforhold. Om en kandidatbane kan opfylde dette afhænger af samspillet mellem baneparametre, som bestemmer synsgeometrien, og rumfartøjets pegeusikkerhed, som forskyder hvert billedes nedslag på overfladen. Denne kombinerede effekt er endnu ikke kvantificeret for Máni, og eksisterende værktøjer behandler de to forhold hver for sig. Vi har udviklet et simulationsmodul, der for en kandidatbane udleder observationsgeometrien over et område af interesse, planlægger billedoptagelser via en udskiftelig planlægger (scheduler), og bruger Monte Carlo-simulering (mange tilfældige kørsler) til at estimere sandsynligheden for, at kravet om fem overlappende billeder opfyldes under en konfigurerbar pegefejlmodel. Vi afprøver modulet på tværs af forskellige baner, områdelayouts, fejlmodeller og planlægningsantagelser. Resultaterne viser, at modulet reagerer meningsfuldt på sine input: det skelner mellem baner med ens overlap men forskellig vinkeldiversitet, synliggør hvordan målafstand og antal overflyvninger styrer færdiggørelse af regioner, og ved at rapportere dækning som en fordeling frem for ét tal gør det værste udfald synlige for missionsplanlæggere.
[This apstract has been rewritten with the help of AI based on the project's original abstract]