Planetary Exploration Missions Research Articles

Aero-structural Characteristics of Membrane Aeroshell at Transonic Speed

Inflatable membrane aeroshell has the potential ability to deliver higher payload in planetary exploration missions. Receiving in-flight aerodynamic force, the membrane surface undergoes large deformation affecting the flow. To investigate the behavior, a coupled aero-structural interactions analysis is required. This paper aims to present a model based on fluid solver SU2, structural solver CalculiX, and coupling library preCICE in a partitioned-coupled manner. The computed results demonstrated that the membrane surface elastically deformed due to the substantial variation in pressure distribution around the aeroshell. Transient behavior has been observed in the aerodynamic coefficients as a result of the small amplitude oscillation of the capsule. This oscillation was induced by the large wake formed behind the vehicle. Wrinkling of the surface caused by the high aerodynamic force shows circumferential movement tendency with time. This study indicated that the analysis model can sufficiently reproduce the coupled aero-structural interaction, which provides insight for developing a flexible structure reentry vehicle.

Patch-Level Unsupervised Planetary Change Detection

Change detection (CD) is critical for analyzing data collected by planetary exploration missions, e.g., for identification of new impact craters. However, CD is still a relatively new topic in the context of planetary exploration. Sheer variation of planetary data makes CD much more challenging than in the case of Earth observation (EO). Unlike CD for EO, patch-level decision is preferred in planetary exploration as it is difficult to obtain perfect pixelwise alignment/coregistration between the bi-temporal planetary images. Lack of labeled bi-temporal data impedes supervised CD. To overcome these challenges, we propose an unsupervised CD method that exploits a pretrained feature extractor to obtain bi-temporal deep features that are further processed using global max-pooling to obtain patch-level feature description. Bi-temporal patch-level features are further analyzed based on difference to determine whether a patch is changed. Additionally, a self-supervised method is proposed to estimate the decision boundary between the changed and unchanged patches. Experimental results on three planetary CD datasets from two different planetary bodies (Mars and Moon) demonstrate that the proposed method often outperforms supervised planetary CD methods. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://gitlab.lrz.de/ai4eo/cd/-/tree/main/planetaryCDUnsup</uri> .

Prospection of multi-spacecraft planetary exploration missions in upcoming future

Prospection of multi-spacecraft planetary exploration missions in upcoming future

Shrimp Robot Mechanism

The Shrimp rover is highly suitable for planetary exploration missions because of its unconventional wheel order, in-built passive adaptability and good ability to climb obstacles. It is a spatial multi-body system and a multi-variable, multi-parameter coupled non-linear system. Thus, kinematic and dynamic analyses for such systems are complex and time consuming. Long-range robotic missions for Martian exploration imply a high degree of autonomy. The most advanced locomotion concepts are based on wheels or caterpillars (e.g. Sojourner, NASA or Nanokhod, ESA). These rovers have clear advantages regarding power efficiency and complexity if compared with walking robots. However, they still have quite limited climbing abilities. Typically they can only overcome obstacle smaller than their wheel size. In this paper we present Shrimp, an innovative long range rover architecture with 6 motorized wheels. Using a rhombus configuration, the rover has a steering wheel in both, the front and the rear, and two wheels arranged on a bogie on each side. The front wheel has a spring suspension to guarantee optimal ground contact of all wheels at any time. The steering of the rover is realized by synchronizing the steering of the front and rear wheel and the speed difference of the bogie wheels. This allows for high precision maneuvers and even turning on the spot with minimum slip. The use of parallel articulations for the front wheel and the bogies enables to set a virtual centre of rotation at the level of the wheel axis while maintaining a high ground clearance. This insures maximum stability and climbing abilities even for relatively low friction coefficients between the wheel and the ground. This rover is able to passively overcome unstructured obstacles of up to two times its wheel diameter. With this high mobility, this architecture is the perfect candidate for long range planetary missions.

Active neutron interrogation experiments and simulation verification using the SIngle-scintillator Neutron and Gamma-Ray spectrometer (SINGR) for geosciences

We present a new SIngle-scintillator Neutron and Gamma-Ray spectrometer (SINGR) instrument for use with both passive and active measurement techniques. Here we discuss, the application of SINGR for planetary exploration missions; however, hydrology, nuclear non-proliferation, and resource prospecting are all potential areas where the instrument could be applied. SINGR uses an elpasolite scintillator, Cs2YLiCl6:Ce (CLYC), that has been shown to have high neutron efficiency even at small volumes, with a gamma-ray energy resolution of approximately 4% full-width-at-half-maximum at 662 keV. Active gamma-ray and neutron (GRNS) measurements were performed with SINGR at the NASA Goddard Space Flight Center (GSFC) Goddard Geophysical and Astronomical Observatory (GGAO) outdoor test site using a pulsed neutron generator (PNG) to interrogate geologically relevant materials (basalt and granite monuments). These experimental results, combined with simulations, demonstrate that SINGR is capable of generating neutron die-away curves that can be used to reconstruct the bulk hydrogen abundance and the depth distribution of hydrogen within the monuments. We compare our experimental results with Monte Carlo N-Particle (MCNP) 6.1 transport simulations to constrain the uncertainties in depth and hydrogen abundance from the neutron die-away data generated by SINGR. For future planetary exploration missions, SINGR provides a single detector system for interrogating the shallow subsurface to characterize the presence and abundance of hydrated phases and to provide bulk elemental analysis.

Mars On-Site Shared Analytics Information and Computing

We study the use of distributed computation in a representative multi-robot planetary exploration mission. We model a network of small rovers with access to computing resources from a static base station based on current design efforts and extrapolation from the Mars 2020 rover autonomy. The key algorithmic problem is simultaneous scheduling of computation, communication, and caching of data, as informed by an autonomous mission planner. We consider scheduling of a dependency chain of required and optional (but rewarding) tasks and present a consensus-backed scheduler for sharedworld, distributed scheduling based on an Integer Linear Program. We validate the pipeline with simulation and field results. Our results are intended to provide a baseline comparison and motivating application domain for future research into network-aware decentralized scheduling and resource allocation.

Deepening the Sense of Touch in Planetary Exploration with Geometric and Topological Deep Learning

Tactile and embedded sensing is a new concept that has recently appeared in the context of rovers and planetary exploration missions. Various sensors such as those measuring pressure and integrated directly on wheels have the potential to add a "sense of touch" to exploratory vehicles. We investigate the utility of deep learning (DL), from conventional Convolutional Neural Networks (CNN) to emerging geometric and topological DL, to terrain classification for planetary exploration based on a novel dataset from an experimental tactile wheel concept. The dataset includes 2D conductivity images from a pressure sensor array, which is wrapped around a rover wheel and is able to read pressure signatures of the ground beneath the wheel. Neither newer nor traditional DL tools have been previously applied to tactile sensing data. We discuss insights into advantages and limitations of these methods for the analysis of non-traditional pressure images and their potential use in planetary surface science.

Intelligent Lunar Landing Site Recommender

Space exploration is brewing to be one of the most sought after fields in today’s world with each country pooling in resources and skilled minds to be one step ahead of the other. The core aspect of space exploration is exoplanet exploration, i.e., by sending unmanned rovers or manned spaceships to planets and celestial bodies within and beyond our solar system to determine habitable planets. Landscape inspection and traversal is the core feature of any planetary exploration mission. It is often a strenuous task to carry out a machine learning experiment on an extraterrestrial surface like the Moon. Consequent lunar explorations undertaken by various space agencies in the last four decades have helped to analyze the nature of the Lunar Terrain through satellite images. The motion of the rovers has traditionally been governed by the use of sensors that achieve obstacle avoidance. In this project we aim to detect craters on the lunar landscape which in turn will be used to determine soft landing sites on the lunar landscape for exploring the terrain, based on the classified lunar landscape images.

Optimization of short-arc ellipse fitting with prior information for planetary optical navigation

Autonomous navigation technology has played an increasingly important role during modern planetary exploration missions. The image-based optical navigation method is one of the most promising solutions for autonomous navigation. Previous studies have mainly focused on image processing and navigation filtering algorithms. Classic ellipse fitting approaches rely on the least square method, which may fail in the case of short-arc ellipse fitting. This paper proposes an optimization method with a priori shape information to achieve a high-precision short-arc ellipse fitting. First, by querying the ephemeris and using the projection principle, the projected ellipse of the entire planet can be obtained. Second, the shape information of the ellipse that represents the useful priori information can be extracted using the least squares ellipse fitting algorithm. Third, the shape information is introduced into the standard elliptic equation, whose parameters are optimized to fit the planetary edge arc obtained by the navigation camera. Finally, simulations are conducted and the results show that the proposed method performs better in the accuracy of short-arc ellipse fitting compared to the traditional least squares method.

Mars3DNet: CNN-Based High-Resolution 3D Reconstruction of the Martian Surface from Single Images

Three-dimensional (3D) surface models, e.g., digital elevation models (DEMs), are important for planetary exploration missions and scientific research. Current DEMs of the Martian surface are mainly generated by laser altimetry or photogrammetry, which have respective limitations. Laser altimetry cannot produce high-resolution DEMs; photogrammetry requires stereo images, but high-resolution stereo images of Mars are rare. An alternative is the convolutional neural network (CNN) technique, which implicitly learns features by assigning corresponding inputs and outputs. In recent years, CNNs have exhibited promising performance in the 3D reconstruction of close-range scenes. In this paper, we present a CNN-based algorithm that is capable of generating DEMs from single images; the DEMs have the same resolutions as the input images. An existing low-resolution DEM is used to provide global information. Synthetic and real data, including context camera (CTX) images and DEMs from stereo High-Resolution Imaging Science Experiment (HiRISE) images, are used as training data. The performance of the proposed method is evaluated using single CTX images of representative landforms on Mars, and the generated DEMs are compared with those obtained from stereo HiRISE images. The experimental results show promising performance of the proposed method. The topographic details are well reconstructed, and the geometric accuracies achieve root-mean-square error (RMSE) values ranging from 2.1 m to 12.2 m (approximately 0.5 to 2 pixels in the image space). The experimental results show that the proposed CNN-based method has great potential for 3D surface reconstruction in planetary applications.

Seeding Biochemistry on Other Worlds: Enceladus as a Case Study.

The Solar System is becoming increasingly accessible to exploration by robotic missions to search for life. However, astrobiologists currently lack well-defined frameworks to quantitatively assess the chemical space accessible to life in these alien environments. Such frameworks will be critical for developing concrete predictions needed for future mission planning, both to determine the potential viability of life on other worlds and to anticipate the molecular biosignatures that life could produce. Here, we describe how uniting existing methods provides a framework to study the accessibility of biochemical space across diverse planetary environments. Our approach combines observational data from planetary missions with genomic data catalogued from across Earth and analyzed using computational methods from network theory. To demonstrate this, we use 307 biochemical networks generated from genomic data collected across Earth and “seed” these networks with molecules confirmed to be present on Saturn's moon Enceladus. By expanding through known biochemical reaction space starting from these seed compounds, we are able to determine which products of Earth's biochemistry are, in principle, reachable from compounds available in the environment on Enceladus, and how this varies across different examples of life from Earth (organisms, ecosystems, planetary-scale biochemistry). While we find that none of the 307 prokaryotes analyzed meet the threshold for viability, the reaction space covered by this process can provide a map of possible targets for detection of Earth-like life on Enceladus, as well as targets for synthetic biology approaches to seed life on Enceladus. In cases where biochemistry is not viable because key compounds are missing, we identify the environmental precursors required to make it viable, thus providing a set of compounds to prioritize for detection in future planetary exploration missions aimed at assessing the ability of Enceladus to sustain Earth-like life or directed panspermia.

Quantitative analysis of binary and ternary organo-mineral solid dispersions by Raman spectroscopy for robotic planetary exploration missions on Mars.

The Mars 2020 and ExoMars 2022, rover-based missions are specifically dedicated to the search for evidence of life and will both utilise Raman spectrometers on the surface of Mars. Raman spectroscopy is indeed a valuable analytical technique for planetary exploration that enables in situ characterisation of rocks and soils collected directly from the surface or retrieved as cores and subsequently crushed when extracted from the subsurface with a drill. On Mars, the miniaturised spectrometers will interrogate ancient geological deposits, in order to try and identify hydrated or aqueously altered minerals and organic matter to assess the habitability of Mars. While the identification of relevant hydrous minerals and organic components is the primary analytical objective of the missions, quantifying their abundances would be of particular significance for interpreting past geological conditions (e.g. formation or alteration processes) and for ascertaining the putative presence of biosignatures. Therefore, we have developed quantitative models that enable the quantification of both mineral proportions from crushed mixtures of geological components and spiked mixtures containing organic analytes dispersed in mineral matrices. Based on data normalisation with appropriate standards (internal and external), we demonstrate that robust quantitative models can be (1) applied for solid dispersions of various complexities relevant to planetary exploration; and (2) applied to different Raman set-ups, including an instrument representative of the ExoMars Raman Laser Spectrometer. With important Raman-active minerals (calcite, gypsum, baryte, quartz), we demonstrate that using a correction factor Fϕ2/ϕ1, based on the ratio of apparent Raman scattering coefficients, the relative proportion of minerals in binary mixtures can be accurately determined. Regarding the organics, evaluated in clay-rich sediments (Fe-smectite) and crushed rocks of coarse-grained fraction (>100μm), we establish calibration curves in the concentration range 2-20 wt% for non-resonant compounds (L-cysteine, phthalic acid, adenine) and even lower (<1 wt%) for pre-resonant anthracene. Despite large levels of heterogeneity, the Raman analyses of these solid dispersions verify that quantitative Raman analyses can be performed in the context of robotic exploration studies.

Detecting Minerals and Organics Relevant to Planetary Exploration Using a Compact Portable Remote Raman System at 122 Meters.

Raman spectroscopy is a technique that can detect and characterize a range of molecular compounds such as water, water ice, water-bearing minerals, and organics of particular interest to planetary science. The detection and characterization of these molecular compounds, which are indications of habitability on planetary bodies, have become an important goal for planetary exploration missions spanning the solar system. Using a compact portable remote Raman system consisting of a 532 nm neodymium-doped yttrium aluminum garnet- (Nd:YAG-) pulsed laser, a 3-in. (7.62 cm) diameter mirror lens and a compact spectrograph with a miniature intensified charge coupled device (mini-ICCD), we were able to detect water (H2O), water ice (H2O-ice), CO2-ice, hydrous minerals, organics, nitrates, and an amino acid from a remote distance of 122 m in natural lighting conditions. To the best of our knowledge, this is the longest remote Raman detection using a compact system. The development of this uniquely compact portable remote Raman system is applicable to a range of solar system exploration missions including stationary landers for ocean worlds and lunar exploration, as they provide unambiguous detection of compounds indicative of life as well as resources necessary for further human exploration.

Electron-Induced Upsets and Stuck Bits in SDRAMs in the Jovian Environment

This study investigates the response of synchronous dynamic random access memories to energetic electrons and especially the possibility of electrons to cause stuck bits in these memories. Three different memories with different node sizes (63, 72, and 110 nm) were tested. Electrons with energies between 6 and 200 MeV were used at RADiation Effects Facility (RADEF) in Jyväskylä, Finland, and at Very energetic Electron facility for Space Planetary Exploration missions in harsh Radiative environments (VESPER) in The European Organization for Nuclear Research (CERN), Switzerland. Photon irradiation was also performed in Jyväskylä. In these irradiation tests, stuck bits originating from electron-induced single-event effects (SEEs) were found, as well as single bit-flips from single electrons. To the best knowledge of the authors, this is the first time that stuck bits from single-electron events have been reported in the literature. It is argued in the article that the single-event bit-flips and stuck bits are caused by the same damage mechanism, which would be large displacement damage clusters, and that the two different fault modes represent different amounts of damage to the memory cell. After a large particle fluence, a rapid increase in the error rate was observed, originating from the accumulation of smaller displacement damage clusters in the memory cells. The 110-nm memory was a candidate component to fly on the European Space Agency (ESA) JUpiter ICy moons Explorer (JUICE) mission, so the SEE cross section as a function of electron energy was compared to the expected electron environment encountered by JUICE to estimate the error rates during the mission.

A Global Gravity Reconstruction Method for Mercury Employing Deep Convolutional Neural Network

Mercury, the enigmatic innermost planet in the solar system, is one of the most important targets of space exploration. High-quality gravity field data are significant to refine the physical characterization of Mercury in planetary exploration missions. However, Mercury’s gravity model is limited by relatively low spatial resolution and stripe noises, preventing fine-scale analysis and applications. By analyzing Mercury’s gravity data and topography data in the 2D spatial field, we find they have fairly high spatial structure similarity. Based on this, in this paper, a novel convolution neural network (CNN) approach is proposed to improve the quality of Mercury’s gravity field data. CNN can extract the spatial structure features of gravity data and construct a nonlinear mapping between low- and high-degree data directly. From a low-degree gravity input, the corresponding initial high-degree result can be obtained. Meanwhile, the structure characteristics of the high-resolution digital elevation model (DEM) are extracted and fused to the initial data, to get the final stripe-free result with improved resolution. Given the paucity of Mercury’s data, high-quality lunar datasets are employed as pretraining data after verifying the spatial similarity between gravity and terrain data of the Moon. The HgM007 gravity field obtained by the MErcury Surface, Space ENvironment, GEochemistry and Ranging (MESSENGER) mission at Mercury is selected for experiments to test the ability of the proposed algorithm to remove the stripes caused by quality differences of the highly eccentric orbit data. Experimental results show that our network can directly obtain stripe-free and higher-degree data via inputting low-degree data and implicitly assuming a lunar-like relation between crustal density and porosity. Albeit the CNN-based method cannot be sensitive to subsurface features not present in the initial dataset, it still provides a new perspective for the gravity field refinement.

Libration points and periodic orbit families near a binary asteroid system with different shapes of the secondary

Due to the limited capability of ground-based observations, the shapes of asteroids cannot be determined precisely, especially for the secondary of a binary system. In this paper, libration points and periodic orbit families near a binary asteroid system with different shapes of the secondary are investigated, with the binary 66391 Moshup, previously known as 1999 KW4, as an example. The gravity fields of the primary and secondary of the binary system are modelled as those of a polyhedron and a triaxial ellipsoid, respectively. With a nominal shape model for the secondary, the positions and stability of libration points are determined, and periodic orbit families emanating from each libration point are generated by numerical methods. The connections and bifurcations of periodic orbit families are analyzed through comparisons with the classic results of the circular restricted three body problem (CR3BP). Then, the effect of the shape uncertainty is investigated by changing the shape of the secondary. Libration points and associated periodic orbit families are calculated and analyzed with different shapes of the secondary. It has been found that the libration points have slight offsets on position, but their topological structures remain the same. In some cases, the halo orbit family bifurcates from the vertical Lyapunov orbit family rather than the planar Lyapunov orbit family, indicating that bifurcations of periodic orbit families are different and new connections between periodic orbit families can exist. The results will provide general insights into variations of the dynamical environments near a binary asteroid system caused by different shapes of the secondary, and will be helpful for the planetary science research and exploration mission design.

A geometry-based slip prediction model for planetary rovers

Wheeled robots commonly undergo slip in various types of natural terrains, particularly in planetary exploration missions. Therefore, it is important to predict slip from a distance for planetary rovers. However, the classical geometry-based slip model has barely been studied in depth in recent years and still has limitations. This paper studies the possibility of model improvement and analyzes the prediction potential of this method. For this purpose, three new input variables, namely, the step differences of pitch and roll angles and the slip of the previous step, are incorporated into the classical model. Based on more accurately measured and densely sampled dataset, these new variables are all proven effective through a series of experiments using different algorithms. The experimental analysis also shows that the slip prediction accuracy can reach a comparable level to the measurement error of the slip. This means that the improved model can achieve the best prediction accuracy, and this method is good enough.

Comparison of novelty detection methods for multispectral images in rover-based planetary exploration missions

Science teams for rover-based planetary exploration missions like the Mars Science Laboratory Curiosity rover have limited time for analyzing new data before making decisions about follow-up observations. There is a need for systems that can rapidly and intelligently extract information from planetary instrument datasets and focus attention on the most promising or novel observations. Several novelty detection methods have been explored in prior work for three-channel color images and non-image datasets, but few have considered multispectral or hyperspectral image datasets for the purpose of scientific discovery. We compared the performance of four novelty detection methods—Reed Xiaoli (RX) detectors, principal component analysis (PCA), autoencoders, and generative adversarial networks (GANs)—and the ability of each method to provide explanatory visualizations to help scientists understand and trust predictions made by the system. We show that pixel-wise RX and autoencoders trained with structural similarity (SSIM) loss can detect morphological novelties that are not detected by PCA, GANs, and mean squared error autoencoders, but that the latter methods are better suited for detecting spectral novelties—i.e., the best method for a given setting depends on the type of novelties that are sought. Additionally, we find that autoencoders provide the most useful explanatory visualizations for enabling users to understand and trust model detections, and that existing GAN approaches to novelty detection may be limited in this respect.

A miniaturized XRF instrument for in situ planetary exploration: The Active X-Ray Spectrometer (AXRS)

The Active X-Ray Spectrometer (AXRS) was developed at Johns Hopkins Applied Physics Lab (APL) to be utilized in landed planetary exploration spacecraft seeking to better understand bulk rock geochemistry of lithophile materials. The AXRS sensor head has a mass of 360 ​g, fits within an envelope of 11 ​× ​6 ​× ​5 ​cm3, and consumes <5 ​W during operation. AXRS has an average relative uncertainty of 9% for deriving the abundances of major and minor elements, and measurement precision ≤10% for all elements. Measurements were made with powdered samples, analogous to high-processed regoliths as expected on the surfaces of airless planetary objects. These specifications are equal to or better than those published for other x-ray instruments used in landed planetary exploration missions. We explored several mission applications for AXRS via analysis of planetary materials under mission conditions. These include the ability to: 1) resolve sub-type of carbonaceous chondrite meteorite via measured abundances of various lithophile element ratios based on volatility, 2) identify low-Fe lunar anorthosite, from high-Fe lunar anorthosite, from lunar mare using bulk Ca, Al, Fe, Mg, and 3) resolve various Venus- and Mars-like volcanic rocks utilizing bulk Si, K, and Na.

Titanium-Water Heat Pipe Radiators for Space Fission Power System Thermal Management

For future space transportation and planetary exploration mission power applications, NASA Glenn Research Center (GRC) is currently developing a small-scale nuclear fission system (i.e. Kilopower system), which has an operable range of 1 to 10 kWe and a design life of 8 to 15 years. The thermal management system of Kilopower system involves two types of heat pipes: high temperature alkali metal heat pipes that are used to transport thermal energy from the nuclear reactor to the Stirling convertors hot end and titanium water heat pipes that are used to remove the waste heat from the convertors cold end and transport it to the radiators for ultimate rejection. This paper presents the development of the titanium water heat pipes, which are featured with a special wick structure design: it has bi-porous screened evaporator and screen-groove hybrid wick in the adiabatic and condenser sections. This will allow the heat pipe to (1) operate in space with zero gravity (2) operate on planetary surface with gravity-aided orientation (3) be tested on ground with slight adverse gravity orientation and (4) to startup smoothly after being frozen. Under a research and development program, several freeze/thaw tolerant heat pipes were designed, fabricated and experimentally validated. Later, various heat pipe radiators were developed and tested in a thermal vacuum chamber (TVC). Test results successfully demonstrated that the titanium heat pipes with radiator attached are able to transfer the required power at the working temperature of 400 K under space-like testing conditions with a thermal resistance of 0.019 °C/W while the total heat pipe radiator weight is less than 0.73 kg.