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The Curiosity Rover, currently operating on Mars, contains flight software onboard to autonomously handle aspects of system fault protection. Over 1000 monitors and 39 responses are present in the flight software. Orchestrating these behaviors is the flight software's fault protection engine. In this paper, we discuss the engine's design, responsibilities, and present some lessons learned for future missions.
Over the first 9 years of the Mars Science Laboratory (MSL) Curiosity rover's surface mission, more than 87\% of its driving has been performed using Visual Odometry (VO). The benefits of using VO during driving are that it minimizes rover position uncertainty and can be used to monitor wheel slip, halting a drive if excessive wheel slip is occurring. The VO implementation on board Curiosity acquires and processes VO images in between drive steps while the rover is stationary. A VO Thinking While Driving (VTWD) flight software capability has been developed which enables the processing of VO images during rover driving, increasing the distance Curiosity can drive with VO in a given time period up to as much as 1.75x total distance. Verification and Validation (V\&V) of the capability has been challenging due to impacts from the COVID-19 pandemic and unavailability of the JPL Mars Yard outdoor test site. The VTWD V\&V test procedures were modified to use a small indoor space with Mars-like terrain. This paper describes the 3 year V\&V effort under challenging conditions to approve the VTWD capability for use on the Curiosity rover.
NASA’s Mars Science Laboratory (MSL) mission landed the Curiosity rover on Mars on August 6, 2012. As of August 6, 2019 (sol 2488), Curiosity has driven 21,318.5 meters over a variety of terrain types and slopes, employing multiple drive modes with varying amounts of onboard autonomy. Curiosity’s drive distances each sol have ranged from its shortest drive of 2.6 centimeters to its longest drive of 142.5 meters, with an average drive distance of 28.9 meters. Real-time human intervention during Curiosity drives on Mars is not possible due to the latency in uplinking commands and downlinking telemetry, so the operations team relies on the rover’s flight software to prevent an unsafe state during driving. Over the first seven years of the mission, Curiosity has attempted 738 drives. While 622 drives have completed successfully, 116 drives were prevented or stopped early by the rover’s fault protection software. The primary risks to mobility success have been wheel wear, wheel entrapment, progressive wheel sinkage (which can lead to rover embedding), and terrain interactions or hardware or cabling failures that result in an inability to command one or more steer or drive actuators. In this paper, we describe mobility trends over the first 21.3km of the mission, operational aspects of the mobility fault protection, and risk mitigation strategies that will support continued mobility success for the remainder of the mission.
This paper presents machine learning-based approaches to automate and optimize the detection of volume loss for the downlink process of telemetry data from the Mars Curiosity Rover. The Curiosity observes volume loss and data corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the data flow. To resolve this issue, we created a data pipeline to accumulate data from various data sources in the downlink process and detect where the data is missed. In this paper, we benchmarked different methodologies based on the accuracy and excitability of them to identify whether a downlink data that is received to the ground system is complete or incomplete. Our results show that machine learning methods can improve the performance of the GDSA by 55% while the user can diagnose why data is missed and provide an explanation for the data accountability problem.
The Mars Science Laboratory (MSL) Curiosity Rover is currently exploring the surface of Mars with a suite of tools and instruments mounted to the end of a five degree-of-freedom robotic arm. To verify and meet a set of end-to-end system level accuracy requirements, a detailed positioning uncertainty model of the arm was developed and exercised over the arm operational workspace. Error sources at each link in the arm kinematic chain were estimated and their effects propagated to the tool frames.A rigorous test and measurement program was developed and implemented to collect data to characterize and calibrate the kinematic and stiffness parameters of the arm. Numerous absolute and relative accuracy and repeatability requirements were validated with a combination of analysis and test data extrapolated to the Mars gravity and thermal environment. Initial results of arm accuracy and repeatability on Mars demonstrate the effectiveness of the modeling and test program as the rover continues to explore the foothills of Mount Sharp.
The Mars Reconnaissance Orbiter (MRO) continues its dual-mode mission of scientific exploration and programmatic support. Now in its 13th year of flight (and its 12th year in its low-altitude, sun-synchronous orbit around Mars), the spacecraft continues in its normal flight mode and its science instruments continue to acquire a treasure trove of data which is being used to study the Martian environment (surface, sub-surface, and atmosphere). To date (July 2018), more than 333 TB of scientific data have been returned to Earth. Building onto the 2017 IAC paper: “The Mars Reconnaissance Orbiter Mission: Continuing a Record of Exploration from Mars Orbit,” [1] this paper provides a 2018 status update on the MRO Mission. A signature event since the 2017 writing is the onset and evolution of a planet-encircling dust event (PEDE), the first such event since 2007. Additional recent science highlights for MRO include the discovery of mid-latitude ice cliffs, polar cap characterization, daily global mapping of the 2018 PEDE dust activity and the atmospheric thermal response, further characterization of the tantalizing recurring slope lineae (RSL), and continued detection of surface changes. Programmatically, UHF relay support for the Mars Science Laboratory (MSL) rover Curiosity and the Mars Exploration Rover (MER) rover Opportunity continues at a pace that provides the engineers and scientists operating those surface vehicles timely information to support their mission planning and science analysis. Most of the landing site characterization requests for the NASA Mars 2020 rover and the ESA ExoMars Rover/Surface Platform (RSP) missions have been completed, with final acquisitions delayed due to the obscuring dust haze from the current PEDE. In parallel, the flight team has faced new engineering challenges as the spacecraft has aged. The team has developed and is implementing actions that are aimed at stretching spacecraft battery life. A new all-stellar capability using the spacecraft’s star trackers now allows for normal spacecraft operations without IMUs. In addition to its current relay duties, MRO will be synchronized to provide critical communications support for the InSight Entry, Descent, and Landing (EDL) event on November 26, 2018, and will be readied to support InSight’s critical commissioning phase with UHF relay support and environmental characterization. A new split-pass relay capability has been developed which will allow MRO to relay with two surface vehicles in “close proximity” on the same overflight, i.e. the InSight lander and the Curiosity rover. Extended for another year of operations by NASA, this paper will highlight recent scientific progress and describe actions that will extend MRO’s life well into the 2020’s as a key element of the Mars Exploration Program.
The Mars Science Laboratory (MSL) spacecraft carrying the Curiosity rover launched from Cape Canaveral Air Force Station (CCAFS) on November 26, 2011. Following an 8.5-month cruise and after a successful Entry, Descent and Landing (EDL) phase, the Curiosity rover arrived at the surface of Mars on August 6, 2012 UTC. At the core of the Curiosity rover power subsystem is the F1 Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) supplied by the Department of Energy. Integration of the F1 MMRTG into the MSL spacecraft has provided the first opportunity to architect a power subsystem that also included a Solar Array (during the cruise phase of the mission and up to the initial stage of the EDL phase) and secondary Li-ion batteries for operation during the planned one Martian year surface phase of the mission. This paper describes the F1 MMRTG functional features as an enabler of the MSL mission and as a novel component of the MSL power subsystem architecture.
If all goes according to plan, in February 2021, NASA will land the Mars 2020 Rover on the surface of Mars. Mars 2020 is the latest in a series of unmanned Martian robotic rover missions that are part of NASA’s Mars Exploration Program, a long-term effort of robotic exploration of the planet. The mission seeks to address high-priority goals for Mars exploration, including answering questions about the potential for past life on Mars. Mars 2020 will look for evidence of habitable conditions on Mars in the ancient past, as well as look for signs of past microbial life itself. The mission also seeks to understanding the geological history and evolution of the planet, and to prepare for future robotic and human exploration. The Mars 2020 spacecraft and rover borrow heavily from the Mars Science Laboratory (MSL) mission and Curiosity rover which landed on Mars in 2012. This reliance on proven technology helps reduce mission risk and cost. Mars 2020 does contain new technology, including a drill for coring samples from Martian rock and soil and a Sample Caching System for gathering, storing and preserving samples for possible future return to Earth. In this paper, we will review the primary goals of the Mars 2020 Mission and look at the reasons for choosing Jezero Crater as the landing site. We will discuss the design and build of the Mars 2020 Spacecraft system and its similarities and differences with Mars Science Laboratory and the Curiosity Rover. We will also review the Mars 2020 Scientific Instrument Suite and their goals. Finally, we will review the Return Sample Contamination Control requirements and the design choices that were made to facilitate meeting these requirements.
The Stimson formation is a basaltic eolian sandstone perched unconformably above the Mount Sharp group rocks in Gale crater, and it is exposed in a number of plateaus observed by the Mars Science Laboratory Curiosity rover. Despite being one of the least geochemically and mineralogically diverse units observed by the Curiosity rover, the Stimson formation is uniquely positioned to offer significant information about sand weathering and lithification processes on Mars because of two factors: (1) the Stimson formation is the only lithologic formation on Mars for which we have a modern analog, the basaltic eolian Bagnold dunes, and (2) Curiosity obtained 33 chemistry analyses of 18 unique targets with the Alpha-Particle X-ray Spectrometer and 2 mineralogical samples of unaltered Stimson sandstone (discounting samples of Stimson altered by late-stage fluid events). Comparison between the Stimson sandstone and the Bagnold sands yields clues to source rock and lithification processes on Mars, and differences between different Stimson samples reveal weathering trends affecting the ancient dune field.
As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The GDSA team can significantly benefit from the automation and optimization of the downlink process of telemetry data. In fact, there is a need for a better understanding of why the data is corrupted, so that the GDSA team can best determine the root cause of the issues in the GDS. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implement various machine learning and deep learning based models, including support vector machines, ensemble methods, and deep neural networks and evaluate their accuracies in identifying whether a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in real time. This hyperparameter optimization also allows our methods to be quickly integrated into other JPL missions. Our results show that our best-performing machine learning and deep learning based models outperform the existing GDSA detection software by 6 accuracy points and can aid analysts by providing insights into the data accountability problem. Since these various machine learning and deep learning approaches vary significantly in interpretability, we provide a discussion on the tradeoffs between their performance and trustworthiness in helping detect issues in data transmission.
The Mars Science Laboratory rover Curiosity has been exploring outcrop and regolith in Gale crater since August 6, 2012. During this exploration, the mission has collected 10 samples for mineralogical analysis by X-ray diffraction (XRD), using the CheMin instrument. The CheMin (Chemistry and Mineralogy) instrument on the Mars Science Laboratory rover Curiosity uses a CCD detector and a Co-anode tube source to acquire both mineralogy (from the pat-tern of Co diffraction) and chemical information (from energies of fluoresced X-rays). A detailed description of CheMin is provided in [1]. As part of the rover checkout after landing, the first sample selected for analysis was an eolian sand deposit (the Rocknest "sand shadow"). This sample was selected in part to characterize unconsolidated eolian regolith, but primarily to prove performance of the scoop collection system on the rover. The focus of the mission after Rocknest was on the consolidated sediments of Gale crater, so all of the nine subsequent samples were collected by drilling into bedrock com-posed of lithified sedimentary materials, including mudstone and sandstone. No scoop samples have been collected since Rocknest, but at the time this abstract was written the mission stands poised to use the scoop again, to collect active dune sands from the Bagnold dune field. Several abstracts at this conference outline the Bagnold dune campaign and summarize preliminary results from analyses on approach to the Namib dune sampling site. In this abstract we review the mineralogy of Rocknest, contrast that with the mineralogy of local sediments, and anticipate what will be learned by XRD analysis of Bagnold dune sands.
In March, 2021 the Mars Science Laboratory Curiosity rover officially started exploring the Clay Sulfate Transition (CST). This area of Mt. Sharp has been interpreted to have hydrated Mg-sulfate spectral signatures in CRISM orbital data, and it no longer has the strong phyllosilicate spectral signature as the previous Glen Torridon region. Recently, the Curiosity rover has also encountered the marker band that was also identified in orbital data [1,3]. This shift from phyllosilicates to hydrated Mg-sulfates suggests a major change in the environmental conditions during which these rocks were deposited. This abstract focuses on changes seen in the Mastcam multispectral data between the CST drill targets.
The CheMin instrument on the Mars Science Laboratory (MSL) rover Curiosity is an X-ray diffraction (XRD) and X-ray fluorescence (XRF) instrument capable of providing the mineralogical and chemical compositions of rocks and soils on the surface of Mars. CheMin uses a microfocus X-ray tube with a Co target, transmission geometry, and an energy-discriminating X-ray sensitive CCD to produce simultaneous 2-D XRD patterns and energy-dispersive X-ray histograms from powdered samples. Piezoelectric vibration of the cell is used to randomize the sample to reduce preferred orientation effects. Instrument details are provided in [1, 2, 3]. Analyses of rock and soil samples by the Mars Exploration Rovers (MER) show nanophase ferric oxide (npOx) is a significant component of the Martian global soil [4] and is thought to be one of the major contributing phases that the Curiosity rover will encounter if a soil sample is analyzed in Gale Crater. Because of the nature of this material, npOx will likely contribute to an X-ray amorphous or short-order component of a XRD pattern measured by the CheMin instrument.
One objective of rover missions is exploring the geological context of the surroundings. Over the years, igneous petrology and sedimentology have been disconnected, the first investigating magmatic processes and volcanic activities, and the second seeking environmental conditions in the past and assessing the habitability of the planet. Although different, one is related to the other: igneous rocks are altered and broken down, leading to the formation of sedimentary rocks, which can in turn be used to back out the nature of their magmatic source. The Curiosity rover that landed in the 3.7 Gyr old impact crater Gale is traveling through sedimentary rocks. About fifty float rocks have been observed, and several of them with ambiguous texture and composition have been classified as igneous or sedimentary depending on studies such as Jake_M. The composition of several unambiguous igneous rocks has been analyzed [4- 6] but their heterogeneity at a larger instrumental (measurement size < 2 cm) scale prevents the measurement of a bulk composition as performed on Earth. An original approach avoiding these two last issues is to consider igneous mineral chemistry analyzed within igneous and sedimentary rocks to assess magmatic processes that could have formed them. Most Curiosity data are used to explore ancient environmental conditions, and a significant number of compositional analyses are under-explored for constraining magmatic activities. We will present how we can make use of sedimentary data for investigating igneous processes in the vicinity of Gale crater. Geological Context: We focus on the first 750 martian days, corresponding to measurements in a coherent lacustrine sedimentary unit called Bradbury, because all sedimentary rocks were sourced from the same watershed and appear to have a consistent source with minimal alteration [2-3]. Igneous detrital minerals including feldspar and pyroxene, are observed in sedimentary rocks. Monte Carlo models showed that minimal cation loss is observed based on the composition of all Bradbury rocks, implying negligible weathering [3]. Although clay minerals are detected in few rocks [7], chemical compositions of rocks can be explained by a mixture of primary igneous minerals [3]. Variation of composition within Bradbury rocks can be explained by mineral sorting and one distinct source component. While a common magmatic source is suggested, Bradbury sediments likely come from several volcanic eruptions from a single magmatic chamber [9- 10]. The occurrence of alkali minerals like sanidine and K-rich rocks throughout Bradbury supports the presence of a potassic component, likely trachytic, while plagioclase and a mafic composition suggest a basaltic component [8-9]. Instruments: Mineral chemistry can be estimated by three instruments onboard Curiosity. The CheMin instrument enables detection of mineral assemblages using X-ray diffraction (XRD). Using Rietveld refinement, each mineral is identified according to their 1D XRD pattern [11]. Note that distinction between pyroxene minerals is challenging with the CheMin instrument due to overlapping peaks on XRD patterns and low angular resolution of the instrument [12]. Then, using least square regression and optimization algorithms based on unit-cell parameters, mineral chemistry has been estimated by [11]. Plagioclase composition has been estimated using the NaAlSi3O8- CaAl2Si2O8 system and alkali feldspar is based on the NaAlSi3O8-KAlSi3O8 system (stars in Fig.1). Two mudstone samples (John Klein and Cumberland) and one sandstone sample (Windjana) were analyzed by CheMin at Yellowknife Bay and Kimberley, respectively. Figure 1. Ternary diagrams of feldspars (top) and pyroxene (bottom) quadrilateral. Stars correspond to CheMin composition and the gray patches to ChemCam composition. The colored dots are the composition of feldspar and pyroxene that crystallized during fractional crystallization at FMQ+1 of a melt extracted at distinct melting degree during the adiabatic ascent of a primitive mantle composition, without any water (left panels) and with 0.5 wt.% of water (right panels) at distinct pressure. The ChemCam instrument enables the analysis of the chemical compositions of rocks at hundreds of micrometer scale (350-550 μm) using laser induced breakdown spectroscopy (LIBS), which may provide the composition of minerals when they are larger than the beam spot (>550 μm) [13]. Within >5000 LIBS points, we performed a typical stoichiometric filtering allowing us to distinguish 56 feldspar and 10 pyroxene mineral compositions (grey patches in Fig. 1). Finally, the Alpha Particle X-ray Spectrometer (APXS) analyzes the composition of rocks with a 1.6 cm diameter spot size. Monte Carlo mass balance modeling allowed [3] to decipher a feldspar range varying between An30 and An40 (Fig. 1). Discussion: Although there could be a more complex history and other ways to form the whole compositional range of igneous minerals analyzed within the Bradbury formation, we are presenting here simple magmatic pathways commonly occurring on Earth using the thermodynamical softwares pMELTS and rhyoliteMELTS [14]. The objective is to find reasonable igneous processes that produce minerals that parallel the compositions of feldspar and pyroxene analyzed by the Curiosity rover. As commonly observed for mid-ocean ridge basalts, the adiabatic ascent of a primitive mantle composition [15] partially melting at 2 GPa has been modeled, followed by the extraction of a liquid at distinct degrees of partial melting, which undergoes fractional crystallization at an oxygen fugacity +1 log unit above the fayalite-magnetite-quartz (FMQ) buffer within the crust (0.02-0.4 GPa) with H2O = 0- 0.5 wt. %. These latter conditions correspond to those recorded within igneous clasts from the Noachian martian breccia NWA 7034 and paired and within Gale igneous rocks (colored dots in Fig. 1) [16-17]. To check the reliability of these 2-step models, we also tested fractional crystallization at similar conditions (FMQ+1; P=0.02-0.4 GPa; H2O = 0-0.5 wt. %) of starting compositions corresponding to that of magmas with distinct melting degrees obtained from isobaric experiments at 2 GPa [18]. Mineral compositions obtained from both models are similar. As shown on Fig. 1, the whole range of observed feldspar compositions cannot be reproduced by fractionation of one magma only. Indeed, while alkali feldspar and Na-plagioclase likely crystallized from fractional crystallization of a low-degree melt (here <15%), plagioclase and pyroxene can only be formed by fractional crystallization of a higher degree melt (here >19%). The corresponding liquid descent lines are broadly in agreement with compositions estimated by ChemCam corresponding to float igneous rocks (Fig. 2) [4-6]. Figure 2. Silica versus alkali content. Lines show the liquid lines of descent from magmas with distinct degrees of melting. Gray patches represents the composition of Gale igneous rocks [4-6]. Trachytic to rhyolitic magmas crystallize alkali feldspar, and andesite to dacite magmas likely form plagioclase. Therefore, at least two starting magmas at distinct melting degrees, which could easily come from a single mantle source, are necessary to explain the whole compositional range of feldspar and pyroxene analyzed within Bradbury rocks. Conclusion: Because rocks from the Bradbury formation are likely originating from the same magmatic source with minimal weathering as supported by several studies using different approaches, igneous mineral chemistry analyzed by CheMin and ChemCam allows us to back out reasonable magmatic pathways that could have crystallized them. Fractional crystallization of at least two starting magmas originating from distinct melting degrees of a single mantle source can explain the whole range of feldspar and pyroxene composition. Both alkaline and sub-alkaline liquids can be produced, with compositions corresponding to those of the igneous rocks analyzed by ChemCam within the Bradbury formation, highlighting the complexity of Mars magmatism.
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.
Detailed measurements of the energetic particle radiation environment on the surface of Mars have been made by the Radiation Assessment Detector (RAD) on the Curiosity rover since August 2012. RAD is a particle detector that measures the energy spectrum of charged particles (10 to approx. 200 MeV/u) and high energy neutrons (approx 8 to 200 MeV). The data obtained on the surface of Mars for 300 sols are compared to the simulation results using the Badhwar-O'Neill galactic cosmic ray (GCR) environment model and the high-charge and energy transport (HZETRN) code. For the nuclear interactions of primary GCR through Mars atmosphere and Curiosity rover, the quantum multiple scattering theory of nuclear fragmentation (QMSFRG) is used. For describing the daily column depth of atmosphere, daily atmospheric pressure measurements at Gale Crater by the MSL Rover Environmental Monitoring Station (REMS) are implemented into transport calculations. Particle flux at RAD after traversing varying depths of atmosphere depends on the slant angles, and the model accounts for shielding of the RAD "E" dosimetry detector by the rest of the instrument. Detailed comparisons between model predictions and spectral data of various particle types provide the validation of radiation transport models, and suggest that future radiation environments on Mars can be predicted accurately. These contributions lend support to the understanding of radiation health risks to astronauts for the planning of various mission scenarios
The SAM suite of instruments on the "Curiosity" Rover of the Mars Science Laboratory (MSL) is designed to provide chemical and isotopic analysis of organic and inorganic volatiles for both atmospheric and solid samples. The mission of the MSL investigations is to advance beyond the successful search for aqueous transformation in surface environments at Mars toward a quantitative assessment of habitability and preservation through a series of chemical and geological measurements. The SAM suite was delivered in December 2010 (Figure 1) to the Jet Propulsion Laboratory for integration into the Curiosity Rover. We previously outlined the range of SAM solid and gas calibrations implemented or planned and here we discuss a specific set of calibration experiments to establish the response of the SAM Quadrupole Mass Spectrometer (QMS) to the four most abundant gases in the Martian atmosphere CO2, N2, Ar, and O2, A full SAM instrument description and calibration report is presently in preparation.
The Sample Analysis at Mars (SAM) investigation of the Mars Science Laboratory(MSL) addresses the chemical and isotopic composition of the atmosphere and volatilesextracted from solid samples. The SAM investigation is designed to contribute substantiallyto the mission goal of quantitatively assessing the habitability of Mars as an essentialstep in the search for past or present life on Mars. SAM is a 40 kg instrument suite locatedin the interior of MSLs Curiosity rover. The SAM instruments are a quadrupole massspectrometer, a tunable laser spectrometer, and a 6-column gas chromatograph all coupledthrough solid and gas processing systems to provide complementary information on thesame samples. The SAM suite is able to measure a suite of light isotopes and to analyzevolatiles directly from the atmosphere or thermally released from solid samples. In additionto measurements of simple inorganic compounds and noble gases SAM will conducta sensitive search for organic compounds with either thermal or chemical extraction fromsieved samples delivered by the sample processing system on the Curiosity rovers roboticarm.