Fundamental Science and Engineering Questions in Planetary Cave Exploration

Abstract

Nearly half a century ago, two papers postulated the likelihood of lunar lava tube caves using mathematical models. Today, armed with an array of orbiting and fly-by satellites and survey instrumentation, we have now acquired cave data across our solar system-including the identification of potential cave entrances on the Moon, Mars, and at least nine other planetary bodies. These discoveries gave rise to the study of planetary caves. To help advance this field, we leveraged the expertise of an interdisciplinary group to identify a strategy to explore caves beyond Earth. Focusing primarily on astrobiology, the cave environment, geology, robotics, instrumentation, and human exploration, our goal was to produce a framework to guide this subdiscipline through at least the next decade. To do this, we first assembled a list of 198 science and engineering questions. Then, through a series of social surveys, 114 scientists and engineers winnowed down the list to the top 53 highest priority questions. This exercise resulted in identifying emerging and crucial research areas that require robust development to ultimately support a robotic mission to a planetary cave-principally the Moon and/or Mars. With the necessary financial investment and institutional support, the research and technological development required to achieve these necessary advancements over the next decade are attainable. Subsequently, we will be positioned to robotically examine lunar caves and search for evidence of life within Martian caves; in turn, this will set the stage for human exploration and potential habitation of both the lunar and Martian subsurface.

Keywords: horizon scan; human exploration; robotic exploration.

Figure 1

Summary by subject area groups, workflow, statistics of panelists (Surveys 1 and 3) and the broader community (Survey 2), and breakdown of the 53 fundamental questions in planetary cave science and engineering by subject area group.

Figure 2

Evaluation of potential bias of survey participants by professional specialty for the 31 survey participants in Survey 1. (a) Whisker plots provide quartile breakdown; solid line within the box represents the median and dots are outliers. (b) NMDS illustrates the lack of clustering across the various professional specialties. Analysis ran with 500 random starts. (c) Summary of responses of participants (i.e., the authors) by professional specialty (individual blue lines of varying hues) identifying questions as “major importance” (y‐axis) by question group (x‐axis). Each line in this graph represents an individual survey participant. Each point along the line is the average of how many times the individual chose “major importance” for a given question group. For example, the datapoint for an individual in the robotics group who considered all broad concepts questions to be of “major importance” would appear as 1.0 (or 100%) on the graph.

Figure 3

Evaluation of potential bias of survey participants by professional specialty for the 82 survey participants in Survey 2. (a) Whisker plots provide quartile breakdown; solid line within the box represents the median and dots are outliers. (b) NMDS illustrates the lack of clustering across the various professional specialties. Analysis ran with 500 random starts. (c) Summary of responses of participants by professional specialty (individual blue lines of varying hues) identifying questions as “major importance” (y‐axis) by question group (x‐axis). “Other” represents other profession. Each line in this graph represents an individual survey participant. Each point along the line is the average of how many times the individual chose “major importance” for a given question group. For example, the datapoint for an individual in the robotics group who considered all broad concepts questions to be of “major importance” would appear as 1.0 (or 100%) on the graph.