Lunar volcanic pits are collapse features that open a window into the Moon’s subsurface and - with that - into its geologic past. Accessible subsurface space might also provide protection to future permanent lunar infrastructure. However, the exploration of pits presents a significant engineering challenge, largely due to the stark topographic gradients around pits, including funnel slopes approaching the angle of repose of regolith, vertical pit walls, and terrain cluttered with boulders. These characteristics have a direct, negative effect on one key element of lunar exploration missions: continuous Line-of-Sight (LoS) between a mobile asset on the surface and a lander or orbiter, which is particularly important for small robotic missions with small antennas and power budgets. Here, we develop and demonstrate two algorithms that can characterize and quantify LoS around prospective landing sites and pits. First, we implement a viewshed algorithm on Digital Terrain Models to assess LoS as a function of asset and lander geometry for 8 selected lunar mare pits with suitable landing sites and available DTMs to aid regional-scale landing site assessment. We identify several pits with promising LoS characteristics and map out optimal lander locations that provide up to 90.3% LoS coverage, such as a potential landing site at l a t = 14 . 085 ° and l o n = 303 . 222 ° near the Marius Hills pit. Second, we use ray-casting and a geometric model of the Marius Hills pit to characterize how assets with different camera viewing geometries are able to map out the pit wall and an anticipated cave for a range of terrain navigation-constraints, with a focus on slope angle. Our analysis suggests that an asset would be able to view as deep as 15 m into the Marius Hills pit from most observation locations without moving onto funnel slopes steeper than ∼ 5°. A dedicated pit explorer such as the LunarLeaper mission could resolve the anticipated cave at a depth of ∼ 30 m with a camera at ∼ 0.7 m above the ground, moving on a funnel slope of about ∼ 10°. Our analysis and algorithms close a key capability gap and directly inform future landing-site selection and traverse planning efforts.
Margarit et al. (Mon,) studied this question.