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NASA NTRSDate not supplied

Highly Differentiated Basaltic Lavas Examined by PIXL in Jezero Crater

Textural, bulk chemical, and mineralogical data collected by PIXL(Planetary Instrument for X-ray Lithochemistry) indicate that the first rock unit (Cf-fr, Crater Floor-Fractured rough)examined by the M2020 Perseverance rover in Jezero crater is from a basaltic lava flow. This unit was originally mapped as volcanic flow[1]but has been reinterpreted as a clastic or volcaniclastic sediment[2].We here present evidence that it is a basalt flow, with implications for its petrogenesis as a highly differentiated basalt.

M E Schmidt↗
NASA NTRSDate not supplied

Topographic Trends of the Geologic Units in Jezero Crater: Lake Levels, Potential Shorelines, and the Crater Floor Units

The Mars2020 Perseverance rover continues to explore the crater floor of Jezero crater working to understand the origins of these different crater floor geological units. Here, we model and analyze the modern and paleo-topography of various facets of Jezero crater to better constrain the evolution and origins of the various units and features observed. In general, we find that that a) the evidences for lake levels are largely unconstrained and are primarily supported by the outflow channel, western delta, and Kodiak, b) so far no convincing evidence has been found for continuous lake terraces (i.e., shorelines), and c) the contact surface of the lobate Crater Floor Fractured Rough (Cf-fr)unit dips to the southeast.

S F Sholes↗
NASA NTRSDate not supplied

Composition and Density Stratification Observed by SuperCam in the First 300 Sols in Jezero Crater

The Perseverance rover has traveled > 2.5 km since leaving its Octavia Butler landing site in Jezero crater ~300 sols ago. The SuperCam remote sensing instrument suite has made > 1000 observations of bedrock along the traverse to provide a comprehensive picture of Jezero crater floor’s chemistry and mineralogy. SuperCam combines high resolution imaging, visible and near-infrared (VISIR) reflectance spectroscopy (0.4-0.85, 1.3-2.6 µm), remote time-resolved green-laser Raman and fluorescence spectroscopy, laser-induced breakdown spectroscopy (LIBS), and acoustic sensing into a single co-boresighted package [1, 2]. Derivation of the major element abundances as oxide wt% for this part of the mission is presented in [3], while calibration of the VIS and IR spectrometers are given in [4-7].

R.C. Wiens↗
NASA NTRSDate not supplied

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↗
NASA NTRS2020

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↗
NASA NTRS2020

Preparing for Mars 2020 and Future Space Missions: Technology Advancements Driving Contamination Control Requirements and Cleanroom Protocols

Future space exploration missions will force contamination control requirements to become more strict to support increasingly sensitive instrumentation and search for life missions. Driving issues for these extremely clean requirements include increased instrument sensitivity, return sample science, and protecting ambitious mission science objectives. Preparing to meet these requirements mandates that contamination control provide new guidelines and more involved support for the cleanrooms during flight hardware assembly, including establishing better methods for setting cleanroom personnel limits to reduce particle fall out in cleanrooms. Limited literature exists for universal methods of determining cleanroom personnel limits, and what does exist includes mostly theory and assumptions on determining the limit. In this work, published method will be assessed against particle fall out data collected from the ISO 5 cleanrooms of the Mars 2020 Perseverance Rover assembly. Additional evaluations will assess contamination control required cleanroom protocols and the overall success of meeting strict cleanliness requirements of the Adaptive Caching Assembly (ACA) and sample tubes to safeguard future scientific endeavors.

Chen, Nicole↗
NASA NTRS2020

Onboard Automated Scheduling for the Mars 2020 Rover

The Mars 2020 Mission, scheduled to land on Mars February 18, 2021, has developed an onboard scheduling system [1]. The rationale for the onboard scheduler is to enable the Perseverance rover to adjust its activities in response to activities taking longer or shorter than planned, or using more or less resources than expected, as effectively using these resources could significantly improve rover productivity [2]. If deployed, the onboard scheduler would be an unprecedented use of Artificial Intelligence/Autonomy onboard software in a key role for a major mission.

Biehl, J.↗
NASA NTRS2020

Results From the First Four Years of Aegis Autonomous Target-ing for Chemcam on Mars Science Laboratory and New Capability Planned for Supercam on Mars 2020 Rover

Autonomous Exploration for Gathering Increased Science (AEGIS) was uploaded on the NASA Mars Science Laboratory (MSL) Curiosity rover in 2015 for autonomous target selection. This paper presents results from the first four years of its regular opera-tion on Mars for autonomously selecting targets for the ChemCam remote geochemical spectrometer with a focus on the most recent findings. Results show that AEGIS has targeted the most desired material greater than 93% of the time vs 24% without onboard intelligent targeting. There has also been a notable increase in the rate of ChemCam observations. AE-GIS is also part of surface flight software for the NASA Mars 2020 Perseverance rover. This paper describes new AEGIS capabilities that will be availa-ble for autonomously targeting the SuperCam in-strument after the planned 18 February 2021 landing in Jezero crater on Mars.

Castano, R↗
NASA NTRS2020

Using a Model of Scheduler Runtime to Improve the Effectiveness of Scheduling Embedded in Execution

Scheduling often interacts with execution. When the scheduler is developing a schedule, real time (execution) proceeds. Usually a scheduler cannot modify portions of the schedule expected to start execution prior to the scheduler's expected completion. In deployed systems, often little effort is spent on predicting scheduler runtime and instead an extremely conservative, simple model is used, resulting in loss of performance as less of the schedule can be updated. We develop predictive model(s) of scheduler runtime and use these models to improve scheduler and execution performance. We present several models of scheduler runtime based on a scheduler being deployed onboard NASA's next Mars rover, the M2020 rover Perseverance. The models consider algorithmic complexity, characteristics of the input plan, and prior runtime data. First, we show how these still relatively unsophisticated models can more accurately predict scheduler runtime compared to the static conservative baseline being used for the actual M2020 onboard scheduler. Second, we show how the more accurate scheduler runtime models' tighter (shorter) runtime predictions enable better scheduler performance as measured by makespan and percentage of activities executed. Finally, we discuss a number of future steps to further advance this line of work.

Chi, Wayne↗
NASA NTRS2020

Mars 2020 Rover Adaptive Caching Assembly: Caching Martian Samples for Potential Earth Return

The Adaptive Caching Assembly (ACA) is part of the Sampling and Caching System on the Mars 2020 Perseverance Rover, and consists of multiple stations that process, hermetically seal, and store sample tubes containing collected Martian material, either rock cores or regolith samples, in preparation for caching on the surface of Mars. The ACA stations consist of seven active degrees-of-freedom, as well as a large number of passive mechanisms that must operate in extreme Mars temperature and pressure conditions. A robotic arm within the Rover manipulates the sample tubes between ACA stations as part of an end-to-end sampling sequence, and utilizes a compliant end effector to accommodate misalignments during station interactions. Stringent hardware cleanliness requirements were dictated to ensure collected samples would not be compromised, which significantly impacted the design, assembly, and test operations of the ACA. Three ACAs were assembled to support ground testing and flight operations, which were exposed to environmental testing to validate functionality in Mars-like conditions. A number of challenges existed from design through test, including volume constraints, mechanism controllability and operation, the effects of tight tolerances, and cleanliness requirements.

Lin, Justin↗
NASA NTRS2020

Using Explainable Scheduling for the Mars 2020 Rover Mission

Understanding the reasoning behind the behavior of an auto- mated scheduling system is essential to ensure that it will be trusted and consequently used to its full capabilities in critical applications. In cases where a scheduler schedules activities in an invalid location, it is usually easy for the user to infer the missing constraint by inspecting the schedule with the in- valid activity to determine the missing constraint. If a sched- uler fails to schedule activities because constraints could not be satisfied, determining the cause can be more challenging. In such cases it is important to understand which constraints caused the activities to fail to be scheduled and how to al- ter constraints to achieve the desired schedule. In this pa- per, we describe such a scheduling system for NASA’s Mars 2020 Perseverance Rover, as well as Crosscheck, an explain- able scheduling tool that explains the scheduler behavior. The scheduling system and Crosscheck are the baseline for oper- ational use to schedule activities for the Mars 2020 rover. As we describe, the scheduler generates a schedule given a set of activities and their constraints and Crosscheck: (1) provides a visual representation of the generated schedule; (2) analyzes and explains why activities failed to schedule given the con- straints provided; and (3) provides guidance on potential con- straint relaxations to enable the activities to schedule in future scheduler runs.

Chien, Steve↗
NASA NTRS2022

Sample Materials Considerations for Curating and Processing Pristine MSR Samples

The perseverance rover is collecting and caching samples of Mars as part of the Mars 2020 mission, which represents the first leg of a multi-mission Mars Sample Return Campaign. The MSR Campaign is an international partnership that will result in delivery of the first martian samples to Earth that were not delivered through meteoritic infall. All meteorites, regardless of how they were handled from recovery to curation, have experienced uncontrolled entry and exposure to the terrestrial environment. Whilst meteorite deliveries are serendipitous, they are also unplanned events that require reactionary responses for recovery and curation. However, with the direct return of pristine astromaterials from another body, we are afforded the ability to design a facility in advance of sample delivery to keep those samples in a pristine (i.e., as returned) state for an indefinite period of time. Given that the curation and processing infrastructure needs to be made out of something, it is important to choose materials for the pristine curation environment that will optimize between the need to effectively process samples and the need to minimize contamination of the samples. The Johnson Space Center (JSC) has an optimized list of materials that have been used in previous sample return missions that includes 304/316 Stainless Steel, Teflon, and T6061 Aluminum (1). This set of materials are compatible with inorganic, organic, and biological cleanliness requirements and protocols. Furthermore, only these materials are permitted to come in contact with pristine samples. We note that JSC uses Neoprene and Hypalon for the gloves on their gloveboxes, but the glove material never comes in direct contact with the samples, only the approved materials. The MSR sample tubes will be made of Ti, so Ti may be an acceptable material for making tools, but the minor and trace element abundances of 304 and 316 stainless steel are well known and do not inhibit scientific investigations of metals, including HSE (2). More work is needed to determine whether the same is true for Ti alloys. In addition to defining the materials in the pristine environment, one must also choose whether the pristine environment will be under vacuum or under a specific atmospheric composition and pressure. Although JAXA has successfully implemented pristine curation vacuum chambers for their Hayabusa and Hayabusa2 samples (3), a vacuum environment is not appropriate for martian samples because it may drive deliquescence of mineral phases in the samples that are sensitive to pressure and relative humidity (4). Consequently, the pristine environment for the martian samples should be under an inert gas. It will be crucial to minimize the number of gases that come into direct contact with samples and these gases will need to be high purity and consistent throughout the pristine isolators. Samples at JSC are stored under high purity gaseous nitrogen (1). Dry N2 gas has not been a problem for N isotope studies for high-T release phases, but an additional inert atmosphere like Ar may be needed for samples where there is a particular concern about low-T release of N from bulk sample analysis. References: (1) McCubbin FM, et al. (2019) Space Science Reviews, 215, 1-81. (2) Day JMD, et al. (2018) Meteorit. Planet. Sci. 53:1283-1291. (3) Yada, T., et al., (2014). Meteorit. Planet. Sci. 49, 135-153. (4) Tosca NJ, et al. (2021). Astrobiology, in press, doi:10.1089/ast.2021.0115.

F M McCubbin↗
NASA NTRSDate not supplied

A Simulated Drilling Mission to Search for Bio-molecular Signatures of Life on Mars Performed in the Atacama Desert (Chile): Demonstrating Drilling, Sample Handling, and Life-Detection Instruments Remotely Operated with Mission-like Protocols

The search for evidence of life on Mars requires accessing materials that are protected from the oxidizing and irradiated conditions at the surface. Mars rovers Curiosity and Perseverance have performed shallow (few cm) drilling to access relevant samples. The upcoming ExoMars mission will acquire samples from up to 2 meters depth. Field experiments in Mars analog sites help prepare for this and other future deep drilling missions. In Sept. 2019 the ARADS (Atacama Rover Astrobiology Drilling Studies project (Glass et al. 2022) conducted a rover-based drilling mission to search for bio-molecular evidence of life in a Mars analog site in Atacama Chile.

Carol R. Stoker↗
NASA NTRSDate not supplied

The Planetary Protection Strategy of the Earth Return Orbiter–Capture, Containment & Return System in the Context of the Mars Sample Return Campaign

The Mars Sample Return Campaign aims at bringing back to Earth the rock and atmospheric samples that the rover Perseverance has started to collect on the surface of Mars with the goal of analyzing them in a facility built specifically for this purpose to answer questions about the habitability of Mars. The Campaign consists of several missions, including the Earth Return Orbiter–Capture, Containment & Return System (ERO-CCRS), which will capture the samples previously put in Martian orbit, contain them in redundant containers to ensure that no unsterilized particles are released, and return them to Earth through a parachute-less entry vehicle. Both NASA and ESA policies address the United Nations’ Outer Space Treaty by addressing potential harm from material returned from solar system bodies beyond the Earth-Moon system. In the conduct of Mars Sample Return, the two agencies have agreed to apply approaches consistent with their own standards to campaign elements each provides. This work presents the overall strategy for both forward and backward planetary protection for the ERO-CCRS mission. Specifically, for forward planetary protection, CCRS is not required to meet specific bioburden requirements as a Category III mission provided the ERO (1) meets orbital lifetime requirements during orbiter operations and (2) any elements jettisoned at Mars meet orbital lifetime requirements. CCRS is required to be built in ISO-8 or better cleanrooms and, by agreement with ERO, be compatible with direct bioburden verification methods. For backward planetary protection, the overall approach includes building robust, highly reliable systems to prevent inadvertent release of unsterilized Mars material through redundant containment vessels and particle transport analyses. Ongoing work to define verification approaches and quantify containment assurance levels for specific sample return systems will also be discussed, along with how those data will inform launch approval for ERO-CCRS.

Giuseppe Cataldo↗
NASA NTRSDate not supplied

Mars Sample Return – An Overview of the Capture, Containment and Return System

The Mars Sample Return campaign aims at bringing back soil, rock and atmospheric samples from Mars to Earth to answer key questions about Mars’ biological evolution by means of four missions. The first one, Mars 2020, landed on the red planet on February 18, 2021 and has to date collected a number of samples through the Perseverance rover. The three subsequent missions will recover the sample tubes, launch them into Mars orbit and transport them back to Earth. These missions are currently in the planning and design stages of development and represent an international effort comprising NASA, ESA and many industry partners. The work presented here provides an overview of the current design and concept of operations of the NASA-provided Capture, Containment, and Return System (CCRS), which is the payload of the ESA-provided Earth Return Orbiter (ERO). ERO will rendezvous with the orbiting samples and CCRS will capture them, contain them and robotically insert them into a capsule that will return the samples to Earth, the Earth Entry System (EES). Three days before arrival on Earth, CCRS will release the EES, which will fly through space, enter Earth’s atmosphere, descend on a well-defined trajectory and safely land at the Utah Test and Training Range. The decision to implement Mars Sample Return will not be finalized until NASA’s completion of the National Environmental Policy Act process. This document is being made available for information purposes only.

Mars Sample Return↗
NASA NTRS2021

Verifying Mars 2020 Sampling and Caching Robotic Functions with Position Budgeting Process and Tool

The Mars 2020 Perseverance Rover was launched on July 30th, 2020 with one of the most complex robotic systems ever implemented on an interplanetary mission. Much of this robotic complexity resides in the Rover Sampling and Caching Subsystem (SCS) to enable collection of Martian samples for eventual return to earth and preparation of surfaces for close-up surface science observations. Two robotic arms are used by SCS: the large Robotic Arm (RA) positions the coring drill and science instruments mounted to the Turret for surface interactions, and the smaller Sample Handling Assembly (SHA) manipulates Sample Tube Assemblies (STA) within the Adaptive Caching Assembly (ACA) to prepare them for sample collection, processing, and hermetic sealing. The robotic arms interact with the Martian surface and other SCS components in many ways and in a variety of configurations, with positioning accuracy requirements ranging from tens of millimeters for some surface interactions down to sub-millimeter accuracy for some ACA interactions.This paper describes the process and tool used to calculate the SCS robotic interaction positioning budgets and verify the as-built hardware when delivered. To ensure that these robotic systems are able to perform their tasks, each robotic interaction with another element was broken down into its composite functions. To calculate a positioning budget margin for each function, an allowable was defined and then compared to the list of error sources that contribute to misalignment. Across the subsystem, over 250 functions were identified to be assessed, with almost 500 error sources feeding into their budget calculations. In addition to using as-built values in the budgets for SCS Verification and Validation (V&V) after the hardware was complete, these budgets were populated with design data during the design phase to identify areas of concern and guide hardware design to ensure positive position budget margins.Because of the size of the SCS team that had inputs to the positioning budgets and the sheer number of items in the budgets that needed to be created, updated, and verified, having a tool that would allow for simultaneous access and robust data integrity and processing was imperative. To accomplish this, a web-based MySQL database was created that allowed users to create function position budgets, link individual errors and allowables to them, and view function position budget margin reports. Each error and allowable records data for lateral, normal, angular, and clocking errors, with the ability to add as-built data for up to four different hardware builds. Margin reports can be generated for either design values alone or replacing design data with as-built data when available. These as-built reports are used for final verification of the SCS positioning requirements. Ultimately, the SCS positioning budget process and database tool led to successful interactions during test and an SCS robotic system that is ready to perform sample acquisition and caching on the surface of Mars.

Williams, Jeffrey↗
NASA NTRS2021

Machine Learning Based Path Planning for Improved Rover Navigation

Enhanced AutoNav (ENav), the baseline surface navigation software for NASA’s Perseverance rover, sorts a list of candidate paths for the rover to traverse, then uses the Approximate Clearance Evaluation (ACE) algorithm to evaluate whether the most highly ranked paths are safe. ACE is crucial for maintaining the safety of the rover, but is computationally expensive. If the most promising candidates in the list of paths are all found to be infeasible, ENav must continue to search the list and run time-consuming ACE evaluations until a feasible path is found. In this paper, we present two heuristics that, given a terrain heightmap around the rover, produce cost estimates that more effectively rank the candidate paths before ACE evaluation. The first heuristic uses Sobel operators and convolution to incorporate the cost of traversing high-gradient terrain. The second heuristic uses a machine learning (ML) model to predict areas that will be deemed untraversable by ACE. We used physics simulations to collect training data for the ML model and to run Monte Carlo trials to quantify navigation performance across a variety of terrains with various slopes and rock distributions. Compared to ENav's baseline performance, integrating the heuristics can lead to a significant reduction in ACE evaluations and average computation time per planning cycle, increase path efficiency, and maintain or improve the rate of successful traverses. This strategy of targeting specific bottlenecks with ML while maintaining the original ACE safety checks provides an example of how ML can be infused into planetary science missions and other safety-critical software.

Yue, Yisong↗
NASA NTRSDate not supplied

A Simulated Drilling Mission to Search for Biomolecular Signatures of Life on Mars Performed in the Atacama Desert (Chile): Demonstrating Drilling, Sample Handling, and Life-Detection Instruments Remotely Operated with Mission-like Protocols

The search for evidence of life on Mars requires accessing materials that are protected from the oxidizing and irradiated conditions at the surface. Mars rovers Curiosity and Perseverance have performed shallow (few cm) drilling to access relevant samples. The upcoming ExoMars mission will acquire samples from up to 2 meters depth. Field experiments in Mars analog sites help prepare for this and other future deep drilling missions. In Sept. 2019 the ARADS (Atacama Rover Astrobiology Drilling Studies project (Glass et al. 2022) conducted a roverbased drilling mission to search for biomolecular evidence of life in a Mars analog site in Atacama, Chile.

Carol R. Stoker↗