The SHERLOC Calibration Target on the Mars 2020 Perseverance Rover: Design, Operations, Outreach, and Future Human Exploration Functions
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Marc D. Fries↗
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Powering Perseverance
Video describing the RTG for the Mars Perseverance rover and INL's role in preparing for the mission.
99 GENERAL AND MISCELLANEOUS↗
Overview of the Capture, Containment, and Return System (CCRS)
The Mars Sample Return (MSR) campaign is one of the most ambitious and complex planetary exploration missions currently underway. With the participation of NASA, ESA, and a large number of industry partners, MSR aims to bring Martian soil, rock, and atmospheric samples back to Earth, in order to answer key questions about Mars’ biological evolution. To accomplish this goal the campaign relies on four coordinated missions, each fulfilling a fundamental role to bring the samples to Earth. The Mars Perseverance rover, the first of the four missions, landed safely on Mars on February 18, 2021 and has already acquired candidate samples for Earth return. A selection of the samples of Martian soil and atmosphere that Perseverance has captured during its mission will be recovered, launched into Mars orbit, and transported back to Earth. The Sample Fetch Rover and Mars Ascent System, both parts of the Sample Return Lander project, perform the Mars surface missions to retrieve the collected samples and launch them into Mars orbit. NASA’s Capture, Containment, and Return System (CCRS), hosted on ESA’s Earth Return Orbiter (ERO), brings the samples back to Earth from Mars orbit. These retrieval and return missions are currently in the planning and design stages of development. The NASA-provided CCRS is the payload of the ESA ERO and is the focus of this presentation. ERO will enter Mars orbit and provide communication relay to Earth for the other MSR elements. The Sample Return Lander systems will fetch the sample tubes and integrate them into a protective vessel – the Orbiting Sample (OS) system – which is then launched into low Mars orbit. ERO will perform rendezvous maneuvers, allowing its CCRS payload to capture the OS, contain it, and perform the first automated in-space assembly of a spacecraft, the Earth Entry System (EES), while in Mars orbit. ERO will then begin its journey back to Earth, with CCRS and its assembled EES spacecraft. Three days prior to arrival, CCRS will release the EES on an Earth entry trajectory from a distance beyond the orbit of the Moon. The passive EES spacecraft will then enter Earth’s atmosphere, flying on a ballistic trajectory, followed by a terminal descent (without a parachute) and landing at the Utah Test and Training Range (UTTR). This presentation will show the current design of the CCRS system and its concept of operations. ERO and CCRS will perform several firsts in planetary exploration: (a) orbital rendezvous and capture in Mars orbit, (b) in-space sterilization and containment, (c) on-orbit spacecraft assembly at Mars, and (d) fully-passive entry, descent, and landing sequence for sample return.
Carlie H. Zumwalt↗
How to Build a Rover: An Overview of the Mars 2020 Mission’s Vehicle System Testbed
While NASA’s Mars rover Perseverance continues to make groundbreaking achievements on the Red Planet, its twin is hard at work here on Earth. The Operational Perseverance Twin for the Integration of Mechanisms and Instruments Sent to Mars, or OPTIMISM, is the Mars 2020 Vehicle System Testbed (VSTB) rover operated by NASA Jet Propulsion Laboratory (JPL) in Pasadena, California. OPTIMISM’s home is the JPL Mars Yard; an outdoor field with red soil that simulates the terrain encountered by Perseverance. The VSTB is a full-scale engineering model of the flight rover, serving a number of functions to ensure mission operations can continue smoothly and on schedule. The VSTB possesses instrumentation, computers, mechanisms, cameras, and a Mobility subsystem that are nearly identical to its extraterrestrial twin. Its high fidelity allows the rover to be a highly effective tool to fully test system functionality and performance prior to commanding the flight rover. The early stages of building OPTIMISM began a few months prior to Perseverance departing JPL for Cape Canaveral, FL in early 2020. Electrical integration of the flight system avionics, and compatibility checkouts of the electrical ground support equipment ensured that the foundation of the electrical system was operational and in place. Next, the internal harnessing was installed and compatibility checks of the rover instrumentation and mechanisms were performed to confirm the system was prepared for full buildup. Finally, mechanical assembly of the rover chassis with its external components completed the integration of the system before it was moved to the Mars Yard for its initial phase of testing to perform verification & validation (V&V) of the Mobility subsystem requirements. By the time Perseverance landed at Jezero Crater in February 2021, the first phase of VSTB operations was underway. Surface guidance, navigation, and control (SGNC) testing for the Mobility subsystem ensured functionality and performance requirements were met for various capabilities such as visual odometry (VO), mapping, and automatic navigation (AutoNav). Subsequent integration of the robotic arm (RA) onto the VSTB enabled the V&V campaign for surface sampling operations (SSO) to commence. As the mission’s engineering operations (EO) have gotten underway, the VSTB has been utilized for an array of purposes including troubleshooting software anomalies, and performing dry-runs for first time activities (FTAs) prior to sending the commands to Perseverance. OPTIMISM will continue to serve mission critical functions as long as Perseverance is roving the Red Planet.
Rojas, Jose Trujillo↗
Identifying Shocked Feldspar on Mars Using Perseverance Spectroscopic Instruments: Implications for Geochronology Studies on Returned Samples
The Perseverance rover (Mars 2020) mission, the first step in NASA’s Mars Sample Return (MSR) program, will select samples for caching based on their potential to improve understanding Mars’ astrobiological, geological, geochemical, and climatic evolution. Geochronologic analyses will be among the key measurements planned for returned samples. Assessing a sample’s shock history will be critical because shock metamorphism could influence apparent sample age. Shock effects in one Mars-relevant mineral class, plagioclase feldspar, have been well- documented using various spectroscopy techniques (thermal infrared reflectance, emission, and transmission spectroscopy, Raman, and luminescence). A subset of these data will be obtained with the SuperCam and SHERLOC (Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals) instruments onboard Perseverance to inform caching decisions for MSR. Here, we review shock indicators in plagioclase feldspar as revealed in Raman, luminescence, and IR spectroscopy lab data, with an emphasis on Raman spectroscopy. We consider how this information may inform caching decisions for selecting optimal samples for geochronology measurements. We then identify challenges and make recommendations for both in situ measurements performed with SuperCam and SHERLOC and for supporting lab studies to enhance the success of geochronologic analyses after return to Earth.
mars↗
Automating Surface Attitude Positioning and Pointing Operations for Mars 2020
The Surface Attitude Positioning and Pointing (SAPP) subsystem of the Mars Perseverance rover keeps track of the rover’s position and attitude on the surface of Mars. The SAPP Downlink Engineering Operations team members receive data from the rover on a daily basis. They must interpret the data to make sure the rover is staying safe and to support uplink planning. The SAPP team keeps track of the error growth in the rover’s attitude estimate due to noise in the Rover Inertial Measurement Unit’s (RIMU) gyroscopes used to propagate that attitude estimate whenever the rover is moving. Whenever this error grows to a particular threshold, SAPP is responsible for updating the onboard attitude knowledge using the RIMU’s accelerometers to estimate rover roll and pitch and sun imaging to estimate rover yaw, thereby reducing this attitude estimation error. Accurate attitude estimation is required so that the rover can successfully point its High Gain Antenna (HGA) to receive information from Earth and as a backup to the Mars orbiters used for sending data from the rover to Earth, point instruments on its Remote Sensing Mast (RSM), and support safe movement and placement of instruments by the rover’s ARM relative to the Martian surface. The Mars 2020 Engineering Operations team has been working to increase the operational efficiency of the mission and eventually move to a five-hour timeline for daily operations. In pursuit of this goal, the SAPP Engineering Operations team has automated their downlink process by developing a centralized Jupyter notebook to analyze the data received daily from the rover. The SAPP downlink Jupyter notebook automatically collects the data relevant to the SAPP subsystem and visualizes this information in plots and tables that can be easily read by downlink operators to aid them in assessing the status of the subsystem. Various Application Programming Interfaces (APIs) have been incorporated into the downlink daily notebook to automate the collection and posting of data, such as gathering and posting data products to the cloud. The SAPP team has also developed a SAPP downlink software library that includes functions to aid the notebook in processing data. In addition to assessing the SAPP subsystem on a daily basis, operators need to assess the long-term trending behavior of the subsystem over time. An automated trending process has been developed to collect information from the daily notebooks in order to plot and analyze that data in a centralized place. These daily and trending processes have expedited the SAPP downlink assessment and laid the groundwork to completely automate the SAPP downlink process so that SAPP operators are unnecessary unless something unexpected occurs. This paper will provide an overview of the functions that the SAPP subsystem carries out on a daily basis, and will then dive into the automations that have been developed for daily and trending downlink assessment. An assessment of the downlink efficiency will be provided, along with a summary of lessons learned and work to go. Finally, the authors will discuss how these types of automated spacecraft health assessments could be more broadly used within mission operations.
Zarifian, Anais↗
The SuperCam Instrument Suite on the NASA Mars 2020 Rover: Body Unit and Combined System Tests
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 ( $105\text{--}7070~\text{cm}^{-1}$ Raman shift relative to the 532 nm green laser beam) with $12~\text{cm}^{-1}$ 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.
47 OTHER INSTRUMENTATION↗
Variance Decomposition of MEDLI2 Reconstructed Heating Using Neural Networks
The Mars Entry, Descent, and Landing Instrumentation (MEDLI2) sensor suite collected data during entry of the Mars 2020 Perseverance rover into Mars’ atmosphere. This suite included a network of MEDLI2 Instrumented Sensor Plugs (MISPs). Each MISP was comprised of a cylinder made of Thermal Protection System (TPS) material with 1-3 embedded thermocouples (TCs), and it was flush mounted into the heatshield or backshell. Data from these in-depth TCs were used to reconstruct the aeroheating environment of the vehicle throughout entry. Surface heating was posed as an inverse problem, with the goal of estimating the surface heating by minimizing an objective function of the difference between MISP temperature measurements during flight and the temperature predictions derived from the Fully Implicit Ablation and Thermal response (FIAT) program. Given an aerothermal environment, FIAT calculates the material response and provides in-depth temperatures throughout the TPS material. To achieve the reverse, an internal tool called FIAT_Opt runs through multiple different environments until the output temperature at the TC depth closely matches the flight data. 95% confidence intervals on the reconstructed surface heating were obtained using Monte Carlo analysis, in which uncertainties in the thermocouple depth and the TPS material properties (e.g., density, thermal conductivity, heat capacity, emissivity) based on flight-lot material testing were included. A variance decomposition method using Sobol indices was employed to assess the sensitivity of the reconstructed peak heating to the TC placement and material property uncertainties. Variance decomposition was found to require tens of thousands of FIAT_Opt runs in order for the Sobol indices to converge. With a single FIAT_Opt run taking on the order of 40 minutes, the required number of computations would take months to complete, even if using multiple CPUs. To mitigate this problem, three machine learning models (ridge regression with cross-validation, random forest regression, and a deep neural network) were trained and tested using the 2000 Monte Carlo runs that were already completed. A subset of 1600 runs were used to train the model (i.e., training set), while the remaining 400 runs were used as the test set. The predictions from the deep neural network (DNN) on the test set showed nearly perfect agreement to the actual values computed with FIAT_Opt (R2 > 0.99). Using the DNN as a surrogate model, the variance decomposition using 50,000 runs was completed within minutes. The resulting Sobol indices showed that the reconstructed peak surface heating was most sensitive to the uncertainties in the thermal conductivity (ST = 0.37) and heat capacity (ST = 0.26). This method can be leveraged to provide requirements for material property measurements needed to improve the accuracy of surface heating prediction and ultimately lead to the reduction of design margins in the future. This presentation will include background on the MEDLI2 suite; the method used for inverse heating estimation; the way that material property uncertainties were accounted for using Monte Carlo analysis; a brief background on variance decomposition; the motivation for using machine learning in this context; how a neural network was trained on the data to enable variance decomposition in a fraction of the time; and the variance decomposition results for one of the MISPs.
Hannah Alpert↗
Variance Decomposition of MEDLI2 Reconstructed Heating Using Neural Networks
The Mars Entry, Descent, and Landing Instrumentation (MEDLI2) sensor suite collected data during entry of the Mars 2020 Perseverance rover into Mars’ atmosphere. An inverse estimation of the backshell and heatshield surface aeroheating was performed, using the data from the MEDLI2 Instrumented Sensor Plugs, a network of thermocouples embedded within the thermal protection system across the aeroshell. Monte Carlo analysis was conducted to assess the sensitivity of the surface heat rate, temperature, and heat load to uncertainties in thermocouple depth and material properties. In this paper, a variance decomposition method using Sobol indices was employed to understand the relative contributions of each uncertainty parameter. Performing this analysis using results from the inverse analysis tool FIAT_Opt was found to require incredibly high computation time, and thus machine learning models were trained and evaluated as a surrogate model for FIAT_Opt. This paper demonstrates that machine learning models can be an efficient, accurate alternative to state-of-the-art inverse analysis tools like FIAT_Opt, especially for computationally-expensive processes. Using these models, the sensitivity analysis showed that uncertainties in heat capacity and thermal conductivity were the main drivers for the overall uncertainty in peak reconstructed heating and heat load.
H S Alpert↗
Test Facilities for SHERLOC Laser Development
The Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) instrument is a deep UV laser based spectrometer that is part of NASA’s Mars Perseverance rover. The laser is a pulsed 248.6 nm NeCu hollow cathode gas discharge laser. The design, development, and testing of lasers and laser power supplies (LPS) were performed by scientists and engineers at the Jet Propulsion Laboratory (JPL) and Photon Systems Inc. (PSI). While these lasers had been used previously in extreme terrestrial environments, before they had to be qualified for operation and functionality over the expected range of environmental situations (temperature cycling, vibration, mechanical shock, low pressure corona emission testing) over the course of mission life time. The SHERLOC laser/LPS testing facilities consisted of custom-tailored environmental test chambers with metrology/control electronics. A custom LabVIEW software package was developed to autonomously operate all test facilities using a multi-threaded, object-oriented programming architecture, tasked with interfacing with many instruments simultaneously for operation and data acquisition.
Houck, Andrew↗
Actuator and Motor Control End-to-End V&V on the Mars 2020 Rover
The Mars 2020 Perseverance rover is the most advanced robotic exploration system ever sent to another planet. To support the complex scientific and mobility needs of the mission, the rover utilizes 33 actuators, three multi-degree-of-freedom force-torque sensors, fifteen single or dual-speed resolvers, two solenoid valves, and twelve contact switches. The control for these actuators and sensors is achieved by several levels of flight software, coordinated between two computers with varying bandwidth control loops. Furthermore, the actuators and sensors were integrated into multiple larger robotic mechanisms that were delivered by different organizations at various points in the Integration and Test (I&T) timeline. All of this created a very complex Verification and Validation (V&V) scenario involving multiple subsystems and teams, several hardware and software testbeds with varying levels of fidelity, and significant systems engineering to ensure the overall I&T schedule could be maintained while ensuring system hardware safety.This paper details the integrated V&V effort across multiple teams and venues to provide full coverage of all necessary functionality, performance, and fault protection. First, it provides an overview of how the V&V campaign was subdivided among teams and venues and provides descriptions of the various hardware configurations used to support the testing. The Mars 2020 implementation of the plan incorporates many of the lessons learned from Mars Science Laboratory’s test campaign, and these value-added modifications are discussed here. Also included in this section is the system-level environmental testing approach used for mechanisms. Second, the paper describes the phased approach used by the teams to support new hardware and software deliveries to testbed and Systems I&T. In this approach the test campaign was built upon higher-level mechanism needs for performance, functionality, and safety at specific times in the campaign. Finally, the paper discusses lessons learned from the V&V campaign that should be applied to future large-scale motion control testing efforts.
Borne, Davis↗
Linux on Mars: How the Perseverance Rover and Ingenuity Helicopter leveraged Linux to accomplish their mission
No abstract provided
Canham, Timothy K↗
An Overview of SHERLOC Raman and Fluorescence Spectroscopy Results Obtained During Perseverance’s Green Zone Campaign at Jezero Crater, Mars
The Perseverance rover landed in Jezero crater, Mars, in February 2021. The field site was chosen because orbiter data provided evidence that the crater hosted an ancient (>2.7 Ga) fluvio-lacustrine environment. The Octavia E. Butler landing site is located ~1.9 km east of the erosional remnant of the Jezero river delta. Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) is an arm-mounted instrument that combines fluorescence and Raman spectroscopy with microscopic imaging to analyze geological materials in order to better understand the history of the environments recorded in the rocks within Jezero crater and to search for potential biosignatures. SHERLOC has two microscopic cameras, the Autofocus and Context Imager (ACI) and the Wide-Angle Topographic Sensor for Operations and eNgineering, (WATSON). These subsystems obtain high spatial resolution (10.1–100 µm/pixel) images to identify grain-scale structure and texture. SHERLOC spectroscopy enables high-sensitivity detection, characterization, and spatially-resolved correlation of trace organic materials. SHERLOC’s 248.6 nm deep UV laser generates a 100 µm-diameter spot. Photons generated by Raman scattering and fluorescence emission are collected and the spectra are downlinked to Earth for analysis. Knowledge of where the laser is pointed allows for mineral and compositional maps to be generated and overlain in ACI and WATSON images. Early spectroscopy observations focused on natural rock surfaces at targets named Nataani (sol 83), Bi_la_sana (sol 98) and Foux (sol 141). These surfaces included patches of aeolian dust that had settled upon the rocks. Perseverance’s abrasion tool is expected to become available in August 2021; it will grind to depths of 14 mm to remove dust and penetrate coatings and weathering rinds. This presentation will summarize major results from analysis of the rocks examined by SHERLOC during Perseverance’s first science and sample coring campaign (Green Zone Campaign).
Luther W Beegle↗
Astrobiological Potential of Rocks Acquired By the Perseverance Rover in Jezero Crater, Mars
The scientific community has prioritized the return of Martian rock samples with known geologic context for more than a decade (Beatty et al., 2019). Thus, the main objectives of the Perseverance rover mission on Mars include identifying past habitable environments, collecting rocks that are likely to preserve biosignaturesand using the rover’s instruments to look for potential biosignatures in these rocks (Farley et al., 2020). Here, we present an overview of the bedrock samples collected bythe Perseverance rover in Jezero crater to date and discuss the potential of the sampled materials to address astrobiological questions upon sample return to Earth.
Mars 2020↗
Perseverance Rover Collision Model for a range of Autonomous Behaviors
The NASA Mars 2020 Perseverance rover landed in Jezero crater on Mars on 18 February 2021. It is a science mission to collect and cache sample cores for possible return to Earth in the future. Robot collision modeling is traditionally used in robotics for hardware safety for manipulation and sampling. The Mars 2020 Rover Collision Model (RCM) optimizes and extends collision checking in innovative ways to provide a range of onboard autonomous capability on a computationally constrained system. It provides an example of the benefit of systems and operations cognizant software design and development of autonomous systems.
Klein, Douglas↗
An acoustic investigation of the near-surface turbulence on Mars
The Perseverance rover is carrying out an original acoustic experiment on Mars: the SuperCam microphone records the spherical acoustic waves generated by laser sparks at distances from 2 m to more than 8 m. These N-shaped acoustic waves scatter from the multiple local heterogeneities of the turbulent atmosphere. Therefore, large and random fluctuations of sound travel time and intensity develop as the waves cross the medium. The variances of the travel times and the scintillation index (normalized variance of the sound intensity) are studied within the mathematical formalism of the propagation of spherical acoustic waves through thermal turbulence to infer statistical properties of the Mars atmospheric temperature fluctuation field. The comparison with the theory is made by simplifying assumptions that do not include wind fluctuations and diffraction effects. Two Earth years (about one Martian year) of observations acquired during the maximum convective period (10:00–14:00 Mars local time) show a good agreement between the dataset and the formalism: the travel time variance diverges from the linear Chernov solution exactly where the density of occurrence of the first caustic reaches its maximum. Moreover, on average, waves travel faster than the mean speed of sound due to a fast path effect, which is also observed on Earth. To account for the distribution of turbulent eddies, several power spectra are tested and the best match to observation is obtained with a generalized von Karman spectrum with a shallower slope than the Kolmogorov cascade, φ(k)∝(1 + k 2 L 2 ) -4/3 . It is associated with an outer scale of turbulence, L, of 11 cm at 2 m above the surface and a standard deviation of 6 K over 9 s for the temperature. These near-surface atmospheric properties are consistent with a weak to moderate wave scattering regime around noon with little saturation. Overall, this study presents an innovative and promising methodology to probe the near-surface atmospheric turbulence on Mars.
79 ASTRONOMY AND ASTROPHYSICS↗
The Mars 2020 Watson Imaging Subsystem of the Sherloc Investigation and Anticpated Early Results
In February 2021, the Mars 2020 Perseverance rover is anticipated to touch down in Jezero crater, Mars. Perseverance is unique in that it will conduct in situ science as well as cache samples for eventual return to Earth for analysis in terrestrial laboratories. It will explore the geologic setting within Jezero over a range of scales in order to address fundamental questions about the evolution of Mars and assess whether there is evidence of past or present Martian life. The Wide Angle Topographic Sensor for Operations and eNgineering (WATSON), one of two imaging subsystems within the Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) instrument[1,2], acquires images that support scientific study of Jezero crater and sample acquisition, as well as rover and instrument operations(Fig. 1).WATSON serves a number of roles; it 1) provides color context imaging of SHERLOC and Planetary Instrument for X-Ray Litho chemistry (PIXL)analysis locations, placing the spatial distribution of organics and mineralogy detected by SHERLOC and the elemental maps generated by PIXL within the context of rock texture and structure; 2)acquires stand-alone observations of rock structures and textures from the outcrop to the grain scale; and 3)images rover components and other instruments to monitor their health and condition. We plan to present images acquired within the first ~30 sols of operations.
M E Minitti↗
Electromagnetic Compatibility Test and Analysis Campaign of NASA's Mars 2020 Rover Final Submission
NASA's Mars 2020 Perseverance Rover—with a launch window opening July 2020, and landing expected February 2021—has mission objectives to look for evidence of habitability, seek biosignatures of past life, collect and cache samples for possible future return to Earth, and prepare for future human missions to Mars. The Rover platform is similar to the previous Mars Science Laboratory (MSL) “Curiosity” rover that landed in 2012 but contains a new suite of scientific instruments and upgrades to existing functionality: seven new and/or upgraded scientific payloads, an upgraded arm and sampling system, and a Helicopter demonstration. These changes—along with new efficiency goals to operate more Rover subsystems concurrently and thus collect more science—presented new electromagnetic compatibility (EMC) challenges. In this paper, we will describe the campaign to ensure Mars 2020 mission success from an electromagnetic environment perspective: 1) confirming existing MSL heritage subsystems and EMC requirements were compatible with the new Mars 2020 mission objectives, 2) engaging with engineers and scientists early in the project to identify and evaluate risks before hardware assembly and performing ambitious risk reduction tests, 3) undertaking a comprehensive subsystem qualification test program based on tailored MIL-STD-461F requirements, occasionally leading to redesign, 4) synthesizing the data collected to perform detailed analyses toward the goal of making risk-informed decisions at a system level. The spacecraft successfully completed all three planned system level tests, demonstrating self-compatibility with minimal impact to operations from electromagn
Gonzales, Edward C↗