RESUMO
NASA's Perseverance rover uses robotic autonomy to achieve its mission goals on Mars. Its self-driving autonomous navigation system (AutoNav) has been used to evaluate 88% of the 17.7-kilometer distance traveled during its first Mars year of operation. Previously, the maximum total autonomous distance evaluated was 2.4 kilometers by the Opportunity rover during its 14-year lifetime. AutoNav has set multiple planetary rover records, including the greatest distance driven without human review (699.9 meters) and the greatest single-day drive distance (347.7 meters). The Autonomous Exploration for Gathering Increased Science (AEGIS) system analyzes wide-angle imagery onboard to autonomously select targets for observations by the SuperCam instrument, a multimode sensor suite capable of millimeter-scale geochemical and mineralogical analysis. AEGIS enables observations of scientifically interesting targets during or immediately after long drives without the need for ground communication. OnBoard Planner (OBP) is a scheduling capability planned for operational use in September 2023 that has the potential to reduce energy usage by up to 20% and complete drive and arm-contact science campaigns in 25% fewer days on Mars. This paper presents an overview of the AutoNav, AEGIS, and OBP capabilities used on Perseverance.
RESUMO
UNLABELLED: A new generation of planetary rover instruments, such as PIXL (Planetary Instrument for X-ray Lithochemistry) and SHERLOC (Scanning Habitable Environments with Raman Luminescence for Organics and Chemicals) selected for the Mars 2020 mission rover payload, aim to map mineralogical and elemental composition in situ at microscopic scales. These instruments will produce large spectral cubes with thousands of channels acquired over thousands of spatial locations, a large potential science yield limited mainly by the time required to acquire a measurement after placement. A secondary bottleneck also faces mission planners after downlink; analysts must interpret the complex data products quickly to inform tactical planning for the next command cycle. This study demonstrates operational approaches to overcome these bottlenecks by specialized early-stage science data processing. Onboard, simple real-time systems can perform a basic compositional assessment, recognizing specific features of interest and optimizing sensor integration time to characterize anomalies. On the ground, statistically motivated visualization can make raw uncalibrated data products more interpretable for tactical decision making. Techniques such as manifold dimensionality reduction can help operators comprehend large databases at a glance, identifying trends and anomalies in data. These onboard and ground-side analyses can complement a quantitative interpretation. We evaluate system performance for the case study of PIXL, an X-ray fluorescence spectrometer. Experiments on three representative samples demonstrate improved methods for onboard and ground-side automation and illustrate new astrobiological science capabilities unavailable in previous planetary instruments. KEY WORDS: Dimensionality reduction-Planetary science-Visualization.