The Application of Computer-Aided Discovery to Spacecraft Site Selection

Abstract:

The selection of landing and exploration sites for interplanetary robotic or human missions is a complex task. Historically it has been labor-intensive, with large groups of scientists manually interpreting a planetary surface across a variety of datasets to identify potential sites based on science and engineering constraints. This search process can be lengthy, and excellent sites may get overlooked when the aggregate value of site selection criteria is non-obvious or non-intuitive. As planetary data collection leads to Big Data repositories and a growing set of selection criteria, scientists will face a combinatorial search space explosion that requires scalable, automated assistance.

We are currently exploring more general computer-aided discovery techniques in the context of planetary surface deformation phenomena that can lend themselves to application in the landing site search problem. In particular, we are developing a general software framework that addresses key difficulties: characterizing a given phenomenon or site based on data gathered from multiple instruments (e.g. radar interferometry, gravity, thermal maps, or GPS time series), and examining a variety of possible workflows whose individual configurations are optimized to isolate different features. The framework allows algorithmic pipelines and hypothesized models to be perturbed or permuted automatically within well-defined bounds established by the scientist. For example, even simple choices for outlier and noise handling or data interpolation can drastically affect the detectability of certain features. These techniques aim to automate repetitive tasks that scientists routinely perform in exploratory analysis, and make them more efficient and scalable by executing them in parallel in the cloud. We also explore ways in which machine learning can be combined with human feedback to prune the search space and converge to desirable results.

Acknowledgements: We acknowledge support from NASA AIST NNX15AG84G (PI V. Pankratius)