Adaptive sampling with PIXL on the Mars Perseverance rover

TL;DR

This paper details the implementation of an adaptive sampling algorithm for PIXL on the Mars Perseverance rover, enabling real-time, autonomous decision-making to optimize scientific measurements of Martian rocks.

Contribution

It introduces the first autonomous, real-time adaptive sampling algorithm for planetary surface analysis, enhancing data quality and scientific return.

Findings

Adaptive sampling increases data quality at points of interest.

Machine learning rules effectively identify compositional targets.

Performance quantified using Mars surface measurements.

Abstract

Planetary rovers can use onboard data analysis to adapt their measurement plan on the fly, improving the science value of data collected between commands from Earth. This paper describes the implementation of an adaptive sampling algorithm used by PIXL, the X-ray fluorescence spectrometer of the Mars 2020 Perseverance rover. PIXL is deployed using the rover arm to measure X-ray spectra of rocks with a scan density of several thousand points over an area of typically 5 x 7 mm. The adaptive sampling algorithm is programmed to recognize points of interest and to increase the signal-to-noise ratio at those locations by performing longer integrations. Two approaches are used to formulate the sampling rules based on past quantification data: 1) Expressions that isolate particular regions within a ternary compositional diagram, and 2) Machine learning rules that threshold for a high weight percent of particular compounds. The design of the rulesets are outlined and the performance of the algorithm is quantified using measurements from the surface of Mars. To our knowledge, PIXL's adaptive sampling represents the first autonomous decision-making based on real-time compositional analysis by a spacecraft on the surface of another planet.