Title: An Information Theoretic Approach to Sample Acquisition and Perception in Planetary Robotics,
Abstract: An important and emerging component of planetary exploration is sample
retrieval and return to Earth. Obtaining and analyzing rock samples can provide
unprecedented insight into the geology, geo-history and prospects for finding
past life and water. Current methods of exploration rely on mission scientists
to identify objects of interests and this presents major operational
challenges. Finding objects of interests will require systematic and efficient
methods to quickly and correctly evaluate the importance of hundreds if not
thousands of samples so that the most interesting are saved for further
analysis by the mission scientists. In this paper, we propose an automated
information theoretic approach to identify shapes of interests using a library
of predefined interesting shapes. These predefined shapes maybe human input or
samples that are then extrapolated by the shape matching system using the
Superformula to judge the importance of newly obtained objects. Shape samples
are matched to a library of shapes using the eigenfaces approach enabling
categorization and prioritization of the sample. The approach shows robustness
to simulated sensor noise of up to 20%. The effect of shape parameters and
rotational angle on shape matching accuracy has been analyzed. The approach
shows significant promise and efforts are underway in testing the algorithm
with real rock samples.