Schrödinger basin is a well-preserved peak-ring basin located on the lunar farside, along the rim of the much larger South Pole – Aitken (SPA) basin. The relatively young age (Lower Imbrian series, or 3.8 Ga) of this basin makes it an ideal site to study the geology of peak-ring basins in general, and the geological history of SPA specifically. Impact materials still recognizable include a well-defined crater rim, wall terraces, quasi-circular peak ring, and interior and exterior melt units. A small pyroclastic deposit fills a portion of the basin floor, along with several mare patches. This study uses Clementine multispectral ultraviolet–visible (UV–VIS) data, and a limited set of higher spectral resolution Chandrayaan-1 Moon Mineralogy Mapper (M3) data, as well as radar, camera, and topography data from the Lunar Reconnaissance Orbiter to better understand Schrödinger’s geology. Sampled spectral profiles and linear unmixing models applied to the Clementine data indicate there is a heterogeneous distribution of both anorthositic and basaltic materials in the crater floor. M3 data further validates this observation, and the high spectral resolution shows that most of the mafic content is dominated by pyroxene. These results challenge the traditional assumption that Schrödinger was formed in mostly highland terrain. Our assessment brings forth a new understanding regarding the placement of Schrödinger within SPA and the role SPA impact materials played in shaping the composition of Schrödinger.

A Mission Control Architecture is presented for a Robotic Lunar Sample Return Mission which builds upon the experience of the landed missions of the NASA Mars Exploration Program. This architecture consists of four separate processes working in parallel at Mission Control and achieving buy-in for plans sequentially instead of simultaneously from all members of the team. These four processes were: Science Processing, Science Interpretation, Planning and Mission Evaluation. Science Processing was responsible for creating products from data downlinked from the field and is organized by instrument. Science Interpretation was responsible for determining whether or not science goals are being met and what measurements need to be taken to satisfy these goals. The Planning process, responsible for scheduling and sequencing observations, and the Evaluation process that fostered inter-process communications, reporting and documentation assisted these processes. This organization is advantageous for its flexibility as shown by the ability of the structure to produce plans for the rover every two hours, for the rapidity with which Mission Control team members may be trained and for the relatively small size of each individual team. This architecture was tested in an analogue mission to the Sudbury impact structure from June 6-17, 2011. A rover was used which was capable of developing a network of locations that could be revisited using a teach and repeat method. This allowed the science team to process several different outcrops in parallel, downselecting at each stage to ensure that the samples selected for caching were the most representative of the site. Over the course of 10 days, 18 rock samples were collected from 5 different outcrops, 182 individual field activities -such as roving or acquiring an image mosaic or other data product -were completed within 43 command cycles, and the rover travelled over 2,200 m. Data transfer from communications passes were filled to 74%. Sample triage was simulated to allow down-selection to 1kg of material for return to Earth.

Remote robotic data provides different information than that obtained from immersion in the field. This significantly affects the geological situational awareness experienced by members of a mission control science team. In order to optimize science return from planetary robotic missions, these limitations must be understood and their effects mitigated to fully leverage the field experience of scientists at mission control. Results from a 13-day analogue deployment at the Mistastin Lake impact structure in Labrador, Canada suggest that scale, relief, geological detail, and time are intertwined issues that impact the mission control science team"s effectiveness in interpreting the geology of an area. These issues are evaluated and several mitigation options are suggested. Scale was found to be difficult to interpret without the reference of known objects, even when numerical scale data were available. For this reason, embedding intuitive scale-indicating features into image data is recommended. Since relief is not conveyed in 2D images, both 3D data and observations from multiple angles are required. Furthermore, the 3D data must be observed in animation or as anaglyphs, since without such assistance much of the relief information in 3D data is not communicated. Geological detail may also be missed due to the time required to collect, analyze, and request data. We also suggest that these issues can be addressed, in part, by an improved understanding of the operational time costs and benefits of scientific data collection. Robotic activities operate on inherently slow timescales. This fact needs to be embraced and accommodated. Instead of focusing too quickly on the details of a target of interest, thereby potentially minimizing science return, time should be allocated at first to more broad data collection at that target, including preliminary surveys, multiple observations from various vantage points, and progressively smaller scale of focus. This operational model more closely follows techniques employed by field geologists and is fundamental to the geologic interpretation of an area. Even so, an operational time cost/benefit analyses should be carefully considered in each situation, to determine when such comprehensive data collection would maximize the science return. Finally, it should be recognized that analogue deployments cannot faithfully model the time scales of robotic planetary missions. Analogue missions are limited by the difficulty and expense of fieldwork. Thus, analogue deployments should focus on smaller aspects of robotic missions and test components in a modular way (e.g., dropping communications constraints, limiting mission scope, focusing on a specific problem, spreading the mission over several field seasons, etc.).

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