The SuperCam instrument suite provides the Mars 2020 rover, Perseverance, with a number of versatile remote-sensing techniques that can be used at long distance as well as within the robotic-arm workspace. These include laser-induced breakdown spectroscopy (LIBS), remote time-resolved Raman and luminescence spectroscopies, and visible and infrared (VISIR; separately referred to as VIS and IR) reflectance spectroscopy. A remote micro-imager (RMI) provides high-resolution color context imaging, and a microphone can be used as a stand-alone tool for environmental studies or to determine physical properties of rocks and soils from shock waves of laser-produced plasmas. SuperCam is built in three parts: The mast unit (MU), consisting of the laser, telescope, RMI, IR spectrometer, and associated electronics, is described in a companion paper. The on-board calibration targets are described in another companion paper. Here we describe SuperCam’s body unit (BU) and testing of the integrated instrument.The BU, mounted inside the rover body, receives light from the MU via a 5.8 m optical fiber. The light is split into three wavelength bands by a demultiplexer, and is routed via fiber bundles to three optical spectrometers, two of which (UV and violet; 245–340 and 385–465 nm) are crossed Czerny-Turner reflection spectrometers, nearly identical to their counterparts on ChemCam. The third is a high-efficiency transmission spectrometer containing an optical intensifier capable of gating exposures to 100 ns or longer, with variable delay times relative to the laser pulse. This spectrometer covers 535–853 nm ($105\text{--}7070~\text{cm}^{-1}$ 105 – 7070 cm − 1 Raman shift relative to the 532 nm green laser beam) with $12~\text{cm}^{-1}$ 12 cm − 1 full-width at half-maximum peak resolution in the Raman fingerprint region. The BU electronics boards interface with the rover and control the instrument, returning data to the rover. Thermal systems maintain a warm temperature during cruise to Mars to avoid contamination on the optics, and cool the detectors during operations on Mars.Results obtained with the integrated instrument demonstrate its capabilities for LIBS, for which a library of 332 standards was developed. Examples of Raman and VISIR spectroscopy are shown, demonstrating clear mineral identification with both techniques. Luminescence spectra demonstrate the utility of having both spectral and temporal dimensions. Finally, RMI and microphone tests on the rover demonstrate the capabilities of these subsystems as well.

The proper alignment of facets on a dish engine concentrated solar power system is criti-cal to the performance of the system. These systems are generally highly concentrating to produce high temperatures for maximum thermal efficiency so there is little tolerance for poor optical alignment. Improper alignment can lead to poor performance and shortened life through excessively high flux on the receiver surfaces, imbalanced power on multicy-linder engines, and intercept losses at the aperture. Alignment approaches used in the past are time consuming field operations, typically taking 4–6 h per dish with 40–80 fac-ets on the dish. Production systems of faceted dishes will need rapid, accurate alignment implemented in a fraction of an hour. In this paper, we present an extension to our Sandia Optical Fringe Analysis Slope Technique mirror characterization system that will auto-matically acquire data, implement an alignment strategy, and provide real-time mirror angle corrections to actuators or labor beneath the dish. The Alignment Implementation for Manufacturing using Fringe Analysis Slope Technique (AIMFAST) has been imple-mented and tested at the prototype level. In this paper we present the approach used in AIMFAST to rapidly characterize the dish system and provide near-real-time adjustment updates for each facet. The implemented approach can provide adjustment updates ev-ery 5 s, suitable for manual or automated adjustment of facets on a dish assembly line. [DOI: 10.1115/1.4004357

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This paper presents a mathematical basis for establishing achievable performance levels for multisensor electronic vision systems.A random process model of the multisensor scene environment is developed. The concept of feature space and its importance in the context of this model is presented.A set of complexity metrics used to measure the difficulty of an electronic vision task in a given scene environment is developed and presented.These metrics are based on the feature space used for the electronic vision task and the a priori knowledge of scene truth.Several applications of complexity metrics to the analysis of electronic vision systems are proposed.