Assessment of M2020 Terrain Relative Landing Accuracy: Flight Performance vs Predicts
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Chen, Allen↗
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Assessment of M2020 Terrain Relative Landing Accuracy: Flight Performance vs Predicts
Terrain Relative Navigation (TRN) was a critical enabling Entry, Descent, and Landing (EDL) technology that enabled Mars 2020 mission Perseverance rover to land at Jezero crater. TRN pro-vides real-time, autonomous, map-relative position determination and generates a landing target based on a priori knowledge of hazards. The required performance for TRN was to land within 60m of the selected target. The required 60m was sub-allocated to various error sources in three major categories: targeting error, knowledge error, and control error. The targeting error is the error in selecting an appropriate landing target and the knowledge of the target on the surface. It includes the Lander Vision System (LVS) position localization with respect the ground, the synchronization between the Lander Vision System measurement and the main Navigation filter, and errors associated with the LVS Reference Map and Safe Target Selec-tion (STS). The knowledge error is the contribution of knowledge growth from the synchronization with LVS to touchdown. The control error encompasses how accurately the system could stay on the desired reference trajectory. The TRN error budget uses a combination of analysis, simulation, and hardware test-ing results to bound the various error contributions obtained during the verification and validation process. This paper first presents a description the TRN system, focusing on the architecture of LVS and STS. The paper then gives detailed overview of the TRN error budget, with a description of the major error contribu-tions in each of the three categories. Next, the paper gives the results for three versions of the error budget, pre-launch, in-flight pre-landing, and post-landing. The paper compares the pre-flight analysis, the pre-landing analysis using in-flight data during cruise, to the post-landing analysis of the TRN performance. Pre-landing analysis best estimate of the landing performance was 33m, compared to the 60m require-ment. Post-landing analysis estimated a landing accuracy of 8.53m or better, much better than the 33m pre-landing estimate. The actual post-landing imagery calculated the distance of the rover to the targeted location to be 5m. The post-landing analysis closely bounds the image-based assessment of landing accu-racy, indicating the success of the error budget architecture in bounding the landing accuracy, as well as the fidelity of the simulations used to model and predict performance.
Chen, Allen↗
Chemistry and Mineralogy of the Margin Unit, Jezero Crater, Mars, Observed By M2020 / Supercam
The Margin Unit of Jezero crater, Mars, was identified from orbit as one of the most carbonate-rich regions of the planet [e.g., 1,2]. Its presence, along with the adjacent fluvial delta [e.g., 3] made Jezero crater the most compelling landing site for the Mars 2020 mission. Investigation of Jezero’s Margin-Unit carbonates provides a unique opportunity to address the formation of carbonates in sedimentary deposits, possibly under a CO 2 -rich martian atmosphere. Here we report on chemistry and mineralogy of 55 targets observed by the multi-technique SuperCam instrument during Perseverance’s crater-margin campaign.
Mars 2020↗
“Just Do It” Mission Operations Training in a COVID World
“No”- “not”- “can’t do it” – these words don’t fly in M2020 Mission Operations. The M2020 Surface Mission Operations Team trained for landing the Perseverance Rover under COVID-19 remote work/mandatory stay-at-home conditions. Training activities included presenting Flight Schools to the team via video conferencing, training COVID personal safety requirements to on-premises staff, and constantly updating and communicating COVID restrictions to the team as safety requirements changed. This paper explores the impact of COVID on the Mission System Training for M2020 Mission Operations. The layers of COVID, aptly named the “COVID Tax” by team management, affected project roles, communications, personnel interactions, operations facility usage and training exercises practiced by the team. Flight Schools and Operational Readiness Tests (ORTs) are driving forces behind the surface mission operations training. These activities work hand-in-hand to prepare the team for landing day, surface operations that transition from cruise to nominal operations, and nominal operations. Under normal training conditions, Flight Schools and ORTs are only concerned with tactical operations for the Uplink (Command) Downlink (Analysis) and Campaign Implementation (Planning) Teams of scientists and system engineers. Due to COVID, and the necessity to maintain physical distance between people, training for the landing team needed to include the new category of COVID personal safety. COVID also necessitated remote teams and video conferencing of the entirety of Flight Schools. This reliance on distance learning had not been done on previous missions. We will explore the advantages and disadvantages of video conferencing as a training platform; training effectiveness in communicating and practicing the multiple changes to the COVID safety protocols; and the timing in which we received and responded to COVID directives. Additionally, this paper will review the practical measures taken by the on-premises team and how the team adapted during the readiness tests, landing, and early mission operations, as well as how the team responded to the post-vaccine ramping down of COVID Protocols. The M2020 Surface Mission Operations Team responded very well to the challenges of COVID. All pre-landing operational and COVID-related training was completed. Post-landing COVID Training was provided as needed to new on-premises personnel. Training presented on COVID for the first operational readiness test (ORT) consisted of 2.5 hours of training. Each ORT had COVID Training. A person who had participated in all of the ORTs from September 2020 – February 2021 would have received over 7 hours of COVID Training. By ORT-12 (approximately 8 weeks after the first ORT), the Training team consolidated the original COVID training to a one-hour COVID Basic Training course, with additional recommended training. The Basic training course was updated after landing, as vaccines became available. COVID Training had multiple Flight Schools in the self-directed Blackboard Learning system, as well as a Quick Reference Wiki for Onboarding of new on-premises personnel and keeping on-premises personnel up-to-date. COVID Training covered many topics including practical methods of 6-foot distancing, how to interact with an IT professional when help was needed at a workstation, and challenging indoor meal-eating protocols. COVID Training continued to be updated, as the lab and the project respond to the virus variants and federal, state and local ordinances.
Rosette, Theresa↗
Assessment of Retrieved GMI Emissivity Over Land, Snow and Sea Ice in the GEOS System
Measurements from microwave sounders and imagers provide a valuable source of information including atmospheric temperature and water vapor in Numerical Weather Prediction (NWP) systems that assimilate these observations directly over water surfaces (oceans and other large water bodies). In a recent decadal survey, targeted observables in the Planetary Boundary Layer (PBL) were cited as a key need for future observations (NASEM, 2018). Microwave observations which sense in the PBL are currently available, however, utilizing surface-sensitive microwave observations for atmospheric data assimilation remains a challenge over land, snow and sea ice. This is in part due to the inability of surface emissivity models used by NWP data assimilation systems to simulate observations with sufficient accuracy. The GEOS-ADAS (Todling and el Akkraoui, 2018) which utilizes the Community Radiative Transfer Model (CRTM) (Han, 2006; Chen 2009) is no exception. The ECMWF system has retrieved instantaneous surface emissivity from surface-sensitive channels for SSMI/S and MHS radiance observations, and apply these estimates to the closest channels higher in frequency (Baordo and Geer 2016) in the calculation of simulated radiances. This approach currently is also being tested in the GEOS-ADAS for AMSU-A and ATMS radiances (Zhu et al. 2021). No or minimal emissivity spectral variability has been assumed in the above-mentioned studies. Recently, work by Munchak et al., 2020 (hereby referred to as M2020) provided a new database for emissivity over land, snow and sea ice retrieved from the NASA Global Precipitation Mission (GPM). Compared with Tool to Estimate Land Surface Emissivities at Microwave (TELSEM2; Wang et al., 2017), M2020 provides emissivities for more frequencies(i.e., 10.7 GHz V/H). Moreover, this database is unique in that it utilizes both active and passive data to retrieve surface emissivity and normalized radar cross section. While the emissivity values may be useful for other sensors, they are most applicable to the GPM Microwave Imager (GMI). In this work the GEOS-ADAS is modified to utilize emissivity values from Munchak et al, 2020 in place of values used by CRTM. Presently, only GMI radiances over ocean are used in the operational GEOS-ADAS. This study will focus on the GMI radiances over land, snow, and ice, as a first attempt to evaluate GMI radiances over these non-water surface types. Two cases are then presented, one with one week of observation minus background departures using the modified GEOS-ADAS, and one utilizing the original GEOS-ADAS. It should be noted that the surface emissivity models in CRTM are not state of the art and are scheduled to be replaced by the Community Surface Emissivity Module (CSEM; Chen and Weng, 2016). Simulations using default CRTM emissivity values are used merely as reference comparing against M2020, and is not a thorough comparison against other more state of the art modules such as CSEM.
GMI↗
Maven Navigation Support of the Mars 2020 Perseverance Entry, Descent and Landing
Through three years of maneuvering, the MAVEN orbiter was able to support the Mars 2020 (M2020) Perseverance Entry, Descent and Landing (EDL), while also changing to a more relay friendly orbit and preserving a mission lifetime through 2030. The synchronization of the MAVEN orbit for M2020 EDL will be reviewed, followed by a description of the MAVEN phasing for EDL. Poten-tial concerns that arose from large density variations will be discussed, along with the interfaces between MAVEN and M2020.
Kangas, Julie↗
Will MSR Samples Cached on the Martian Surface Experience Significantly Greater Thermal Degradation than Samples Retained in the Rover?
Since the first Mars Sample Return (MSR)-related report published by the Jet Propulsion Laboratory (JPL) in 1974, a series of panels, reports, and white papers have recognized the importance of sample temperature in meeting mission goals and defined a sample maximum temperature (henceforth SMT) limit. The Mars Sample Handling and Requirements Panel (MSHARP, 1999) flatly stated that “[t]he main issue in sample preservation is temperature”. The Mars Exploration Program Analysis Group (MEPAG)’s “Science Priorities for Mars Sample Return” report (2008), declared that “[s]ignificant loss, particularly to biological studies, occurs if samples reach +50°C for three hours ” whereby “scientific objectives related to life goals could be seriously compromised”. Overall, a total of seven panels, white papers, and conference reports adopted a SMT of -40±17°C to preserve samples sufficiently to confidently achieve success in studies of past or present Martian life (see more detail in [5]). In contrast, the Mars 2020 rover (M2020) mission adopted a SMT of +60°C for samples stored on the Martian surface and +50°C for samples retained inside the rover, as stated in a conference poster presented by Beaty et al., 2016. M2020 is currently collecting samples for MSR in tubes. Half of those tubes will be retained within the M2020 rover body (hereafter rover samples, or RS) and half will be deposited on the Martian surface (cached samples or CS), with a currently undetermined number of each collected up to ten years later for return to Earth. CS samples can be expected to experience significantly higher temperatures than RS samples based on their exposed location in sunlight. This work will explore differences in deleterious chemical reaction rates due to thermal environment of both tube types. The findings here should be debated openly and considered when deciding which samples to return to Earth.
M Fries↗
Mars 2020 Site-Specific Mission Performance Analysis: Part 2. Surface Traversability
The Mars 2020 Rover Mission (M2020) is characterized by long-range traverses between scientific Regions Of Interest (ROIs), as well as the demanding requirement on the distance and time for the inter-ROI traverses. As a result, surface traversability is one of the major driving factors for the landing site selection of M2020. With the newly developed Mars Terrain Traversability analysis Tools (MTTT), we performed traversability analysis of the eight candidate landing sites with an unprecedented granularity. This paper describes the MTTT analysis capabilities, as well as how the MTTT capabilities were used to down-select from eight to three candidate landing sites for further evaluation.
Milkovich, Sarah↗
Relative Detectability of Iron-Bearing Phases for the Mars 2020 Sherloc Deep UV Raman Instrument: 1. Focusing on Carbonates
A deep ultraviolet (DUV) Raman and fluorescence instrument is a surface standoff instrument mounted on the robotic arm of the Mars 2020 (M2020) rover Perseverance, and it is a key element of the Scanning Habitable Environments with Luminescence for Organics and Chemicals (SHERLOC) investigation [1]. Measurement and science objectives include mineralogical and organic images (~100 μm/pixel) that map sub-millimeter spatial distributions and characterization of primary and secondary minerals, potential organics, and their interaction/alteration products. The results of data analysis pertain to understanding igneous and alteration processes on Mars through time, assessing habitability, evidencing in situ biosignatures, and, along with results from other Perseverance instruments, selecting samples to cache for Mars sample return. Acceptance of the SHERLOC investigation for the NASA M2020 mission [2] created a need for Mars-relevant DUV Raman spectra, particularly for inorganic materials [e.g., 3, 4]. As reported previously [e.g., 4, 5], phases with Fe cations as essential elements (e.g., siderite (FeCO3) and ankerite (FeCa(CO3)2) for carbonates) significantly hinder detection by DUV Raman because of intense absorption of incident and scattered DUV laser radiation by Fe cations. We report here the relative detectability of carbonates (Mg,Ca,Fe,Mn)CO3 by DUV Raman using a SHERLOC analog laboratory instrument.
R V Morris↗
A ROS-based Simulator for Testing the Enhanced Autonomous Navigation of the Mars 2020 Rover
In order to achieve the ambitious objectives of the Mars 2020 (M2020) mission, in particular the ability to autonomously traverse more challenging terrains more efficiently, new surface mobility software was developed for Enhanced Navigation (ENav). That decision was made early in the project, before most of the new surface flight software (FSW) existed, which created a need for a separate framework where the new navigation algorithms could be quickly prototyped and tested, before more realistic FSW-based testbeds became available. The JPL robotics team chose the Robot Operating System [1] (ROS) as the environment in which to test the new ENav algorithms. This made it possible to write the algorithms in the C language required by the FSW, so they could be directly ported over to the flight module later on, while leveraging all the C++ libraries and tools provided by ROS for simulation and testing. The ENav algorithms were developed as a separate C library, and stubs were used to replace any FSW-specific code, such as Event Reporting (EVRs) and data products (DPs). A ROS simulator was developed to generate a rich set of varied 3D terrains representative of the candidate Mars landing sites and simulate the physics of the rover motion, the point cloud perceived by the rover’s stereo vision system, and the new thinking-while-driving (TWD) navigation logic which directs the rover to drive autonomously to user-specified waypoints. To simulate the rover motion and perception, a ROS node was developed that uses a software library called HyperDrive Sim (HDSim), which is a wrapper for the Rover Sequencing and Visualization Program [2] (RSVP). That library provides roverterrain settling, realistic slip modelling, and camera rendering capability based on the rover’s NavCam machine vision models. To simulate the navigation logic, a ROS node was created that initializes and runs the ENav algorithms in a way that mimics the FSW execution, while also providing the capability to load and replay data products, including re-running the recorded inputs through the ENav algorithms for testing. An engineering Graphical User Interface (GUI) was also developed to visualize various elements, such as the rover pose during the drive, the simulated and perceived terrain, the selected local and global paths to the goal, the evaluated candidate paths and the reasons why they were rejected, the keep-in and keep-out zones (KIOZs), etc. Finally, an advanced Monte Carlo (MC) framework that can run many simulations in parallel on the Cloud and automatically generate reports that capture the key ENav performance metrics was developed to evaluate the system in a statisticallymeaningful way. This paper provides an overview of the ROSbased simulator used for testing the M2020 ENav algorithms.
Toupet, Olivier↗
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↗
The Mars 2020 Perseverance Navigation Filter
The Mars 2020 (M2020) Entry Descent and Landing (EDL) system delivered a rover named “Perseverance” at Jezero crater (Mars) on February 18th, 2021. M2020 EDL used a Guidance Navigation and Control (GN&C) system to achieve its landing target objectives. The navigation filter (NavFilter) integrated Descent Inertial Measurement Unit (DIMU) measurements to estimate position and attitude. The NavFilter also used the Terminal Descent Sensor (TDS) to es-timate surface-relative position and velocity corrections. The NavFilter provided all of the dynamic state information to the entry controller, entry guidance, Lander Vision System (LVS) and powered descent guidance and control algo-rithms. The NavFilter for Perseverance is based on Curiosity’s NavFilter with a couple of modifications.
Casoliva, Jordi↗
Formal Methods for Trusted Space Autonomy, Boon or Bane?
Trusted Space Autonomy is challenging in that space systems are complex artifacts deployed in a high stakes environment with complicated operational settings. Thus far these challenges have been met using the full arsenal of tools: formal methods, informal methods, testing, runtime techniques, and operations processes. Using examples from previous deployments of autonomy to the Remote Agent on DS-1, Autonomous Sciencecraft on EO-1, WATCH on MER, IPEX, AEGIS on MER, MSL, and M2020, and the M2020 Onboard planner, we discuss how each of these approaches have been used to enable successful deployment of autonomy. We next focus on relatively limited use of formal methods (both prior to deployment and runtime methods). From the needs perspective, formal methods represent the best chance for reliable autonomy as testing, informal methods, and operations accommodations do not scale well with increasing complexity of the autonomous system. However from the practice perspective, formal methods have been limited in their application due to difficulty in eliciting formal specifications and challenges in representing complex constraints such as metric time and resources. We discuss some of these challenges as well as the opportunity to extend formal and informal methods into runtime validation systems.
Chien, Steve↗
Real-World Testing of LiDAR-Inertial Based Navigation and Mapping for Precision Landing
The fusion of LiDAR and inertial measurements duringspacecraft descent and landing can be used to estimate alander’s navigation state and map the terrain below. Together,these data products can be used to enable safe and preciselanding on celestial bodies for which a priori orbital reconnaissanceis insufficient for hazard detection and avoidance.Unlike camera images used in visual terrain relative navigation,LiDAR scans are insensitive to changes in illumination; as aresult, the technique can be used to land in poorly lit areas,or at times of day when the lighting conditions are incongruentwith existing orbital imagery. In this paper, we extend previouswork in which we introduced a factor graph based smoothingapproach for LiDAR-inertial navigation and mapping. Whereasthe algorithms were previously tested on simulated data, thispaper presents testing on real-world data. Data from theAutonomous Landing Hazard Avoidance Technology (ALHAT)airplane flight tests in the Yucca Flats and Death Valley in2009 (FT3), the Morpheus vertical take off and landing flighttests at Kennedy Space Center in 2014 (FT6), and the landingof Perseverance and Ingenuity on Mars in 2021 (M2020) wereused to evaluate algorithm performance. In this paper, weextend our LiDAR-inertial technique to work with a variety ofranging technologies: single point laser altimetry (FT3), denseflash LiDAR (FT6), and six-beam radar (M2020). A thoroughperformance analysis for all three datasets is presented. Datasetpreparation, improvements in algorithm robustness, and outlierrejection, which were necessitated by the transition to realworlddata, are discussed.
Trawny, Nikolas↗
Testing Mars 2020 Flight Software and Hardware in the Surface System Development Environment
The Mars 2020 (M2020) Perseverance Rover is NASA's most advanced planetary rover mission to date. It includes a novel Sample Caching Subsystem (SCS) which will collect rock cores for possible future return to Earth, as well as an improved mobility system with enhanced autonomous navigation which will enable it to traverse faster and farther than prior rovers. The development of both systems required extensive flight software and flight hardware testing. To support this testing, we developed the Surface System Development Environment (SSDEV) and used it for a wide variety of testing. SSDEV is a bundled subset of M2020 Flight Software which runs on commercially available Linux computers and can be combined with multiple backend options for simulation and hardware control. The SSDEV architecture enabled our teams to perform much more testing of flight software and flight hardware than would have otherwise been possible. As a secondary benefit, the SSDEV-based test campaigns also helped our teams enter the operations phase of the mission with greater readiness of operations products and tools. In this paper, we summarize the motivation for SSDEV, provide an overview of the SSDEV architecture, list several examples of how SSDEV was used, and summarize lessons learned. SSDEV is not a substitute for integrated testing with flight-like avionics, but it enabled substantially more testing than would have otherwise been possible and also provided some unique benefits. We recommend architectures like SSDEV to future projects that need to perform extensive hardware and software testing using a limited set of flight-like avionics.
Wai, Dennis↗
Seeking Signs of Life on Mars: A Strategy for Selecting and Analyzing Returned Samples from Hydrothermal Deposits
Highly promising locales for biosignature prospecting on Mars are ancient hydrothermal deposits, formed by the interaction of surface water with heat from volcanism or impacts. On Earth, they occur throughout the geological record (to at least approx. 3.5 Ga), preserving robust mineralogical, textural and compositional evidence of thermophilic microbial activity. Hydrothermal systems were likely present early in Mars' history, including at two of the three finalist candidate landing sites for M2020, Columbia Hills and NE Syrtis Major. Hydrothermal environments on Earth's surface are varied, constituting subaerial hot spring aprons, mounds and fumaroles; shallow to deep-sea hydrothermal vents (black and white smokers); and vent mounds and hot-spring discharges in lacustrine and fluvial settings. Biological information can be preserved by rapid, spring-sourced mineral precipitation, but also could be altered or destroyed by postdepositional events. Thus, field observations need to be followed by detailed laboratory analysis to verify potential biosignatures. See Attachment
Campbell, K. A.↗
M2020/MSL: TDS (Terminal Descent Sensor) DEA (Digital Electronics Assembly)
TDS is the radar used in MSL mission to land Curiosity rover on Mars. TDS will be used again in M2020 mission. DEA is the brain of the TDS where it serves as the controller and digital signal processor. The DEA commands TDS to cycle through the antenna beams making measurements using one of the 6 antenna beams at a time. DEA uses beam sequences and parameter tables to generate measurements. The most important measurements are velocity and range that are used to guide the landing of Curiosity rover. DEA performs these measurement tasks using radar signal processing FPGA firmware and radar microprocessor ‘s hardware and software.
Ashtijou, Mohammad↗
Mars Sample Return Science Planning Group Phase 2 (MSPG2): Overview & Interim Report
Mars Sample Return (MSR) has been a high priority of the international planetary science community for decades. In recent years, significant programmatic advances have brought MSR closer to becoming a reality. In 2018, NASA and the European Space Agency (ESA) signed a joint Statement of Intent to continue defining respective roles and responsibilities in the flight missions required to realize MSR. In October 2020, NASA and ESA formalized this partnership with the signature of a Memorandum of Understanding for the MSR flight elements. The MSR campaign consists of M2020, two MSR flight elements and the ground-based infrastructure to receive, handle and curate the samples from Mars. In an engineering sense, MSR consists of a linked set of missions, and a concluding set of ground-based activities, that we refer to as the MSR Campaign.
G. Kminek↗