Planetary Exploration Missions Research Articles

Raman Match: Application for Automated Identification of Minerals from Raman Spectroscopy Data

Abstract Raman spectroscopy is a rapid, nondestructive analysis technique that is used in various scientific disciplines, including chemistry, materials science, and biology. The analysis of Raman spectra and the identification of specific substances in unknown samples can be complex and time-consuming tasks due to the large database of Raman spectra. The Raman Match application was developed to simplify and automate the sample identification process through a search and match method. The application integrates the well-established RRUFF Raman database with the Python programming language and provides a user-friendly graphical interface to load Raman spectra, identify and fit peaks, match peaks to the reference libraries, visualize the results, and generate publication-ready figures. The application offers a swift and automated method for mineral identification using Raman spectroscopy in both laboratory and field settings, as well as during planetary exploration missions to extraterrestrial environments with constraints on time and resources.

Joint analysis of JUICE and Europa Clipper tracking data to study the Jovian system ephemerides and dissipative parameters

Jupiter and its moons form a complex dynamical system that includes several coupling dynamics at different frequencies. In particular, the Laplace resonance is fundamental to maintaining the energy dissipation that sustain Io’s volcanic activity and Europa’s subsurface ocean; studying its stability is thus crucial for characterizing the potential habitability of these moons. The origin and evolution of the Laplace resonance is driven by the strong tidal interactions between Jupiter and its Galilean moons, and the future planetary exploration missions JUICE and Europa Clipper could bring new light to this unsolved mechanism. During the Jupiter tours of both missions and JUICE’s Ganymede orbital phase, two-way radiometric range and Doppler data will be acquired between Earth ground stations and the spacecraft, which will be processed to recover the static and time-varying gravity field of the moons. Moreover, range and Doppler data will improve the orbit accuracy of the moons, providing precise measurements of Jupiter’s tidal parameters. This work presents a covariance analysis of the joint orbit determination of JUICE and Europa Clipper, aimed at quantifying the expected uncertainties on the main parameters that characterize the dynamics of the Jupiter system. We simulated radio science data from JUICE and Clipper missions under conservative noise assumptions, using a multi-arc approach to estimate the ephemerides and dissipation in the system. Even though JUICE and Europa Clipper will not perform flybys of Io, the strong coupling with Europa and Ganymede will allow an improvement of our knowledge of the Jupiter-Io dissipation parameters thanks to JUICE and Europa Clipper radiometric data. Moreover, the expected uncertainty in Jupiter’s dissipation at the frequency of Callisto could unveil a potential resonance locking mechanism between Jupiter and Callisto.

MEMS miniaturized low-noise magnetic field sensor for the observation of sub-millihertz magnetic fluctuations in space exploration

The objective of this paper is to show that, using magnetic field modulation with a MEMS resonator, it is possible to reduce the noise floor of a commercial Tunneling Magnetic Resistance by one order of magnitude, in the ultra-low frequency range. Low noise at this frequency range is a strong requirement in planetary or space exploration missions like LISA (Laser Interferometer Space Antenna), where the observed gravitational waves will be in the 0.1 mHz to 0.1 Hz range. It will be shown that the noise spectral amplitude after demodulation from 0.1 mHz to 100 mHz is well below the 100 nT/Hz requirement for the future space-borne gravitational wave detector LISA – that we take as reference – being at 20 nT/Hz at 0.1 mHz, and reaching an average 1.5nT/Hz at higher frequencies, from 100 mHz to 1 Hz.

Spring Less Suspension using Rocker Bogie Mechanism

The rocker bogie mechanism is a type of suspension system commonly used in robotic vehicles, particularly in space exploration rovers like NASA’s Mars rovers. Unlike traditional spring-based suspensions, the rocker bogie mechanism relies on a system of pivoting joints and linkages to maintain stability and traction over uneven terrain. This mechanism allows the vehicle to traverse rough surfaces by distributing weight and adjusting wheel positions to accommodate obstacles. Its abstract nature lies in its ability to provide stability and mobility without relying on springs, making it well-suited for navigating challenging environments such as rocky terrain or steep slopes. Overall, the rocker bogie suspension system is well-suited for applications where stability, mobility, and reliability are paramount, making it a preferred choice for planetary exploration missions and other off-road vehicles operating in rugged terrain

Permittivity sensor development for lunar and planetary surface exploration

Permittivity sensors measure the electric permittivity (formerly known as the dielectric constant) of a sample between its electrodes and offer a reliable method to characterize the subsurface of planetary bodies in situ. One potential application is the identification and mapping of water ice at the poles of the Moon. In this paper, the scientific background and heritage of the permittivity sensor concept are discussed, and the implemented electronic architecture is introduced, focusing on the novel patch electrodes. The data processing approach for the measurements is based on Fourier transformation, and numerical simulation setups are used for performance predictions. The calibration of the sensor validates the functionality of the electronics, and the results from both simulations and characterization experiments show that the concept is applicable in the exploration scenario. Considering both engineering and scientific aspects, the results highlight the permittivity sensor’s suitability for lunar and planetary exploration missions, albeit further points for improvement are identified.

Fraunhofer line-based wavelength-calibration method without calibration targets for planetary lander instruments

High-accuracy wavelength calibration is critical for qualitative and quantitative spectroscopic measurements. Many spectrometers employed in planetary-exploration missions have onboard calibration sources, including standard lamps and calibration targets. However, such calibration sources are not always available because planetary missions, particularly landing missions, usually have limitations in size and mass. Thus, a wavelength calibration method without requiring hardware addition can be highly beneficial. In this study, we demonstrate a method for wavelength calibration using solar Fraunhofer lines observed in the reflectance spectra of planetary surfaces. Using a Raman spectrometer prototype developed for a Phobos rover, we measured the spectrum of the sunlight reflected from a spectral standard, manufactured to provide similar reflectance spectra to the surface of Phobos. We identified 35 Fraunhofer absorption lines in the wavelength range between 530 and 700 nm and utilized these features for the wavelength calibration of the spectrometer. This approach using Fraunhofer lines achieved good results (better than +0.04/−0.06 nm), comparable to the results achieved using a conventional Ne lamp. The wavelength accuracy corresponds to a wavenumber accuracy better than ±1.5 cm−1 in the 0–4000 cm−1 Raman shift (Stokes shift) range with a 532 nm excitation laser. This result enabled the estimation of the magnesium number (Mg#) of olivine, achieving a value more precise than 1.5% based on the Raman peak positions. In addition, we examined the number of solar Fraunhofer lines detectable at different wavelength resolutions by binning the solar spectrum acquired in this study. We found that more than 10 Fraunhofer lines could be detected as prominent absorption lines when the wavelength resolution is higher than 1 nm/pix (30 cm−1/pix at 1000 cm−1). This result suggests that the target-free wavelength-calibration method using solar Fraunhofer lines can be applied to other spectrometers simply by observing sunlit planetary surfaces.

Design of a deployable aerodynamic decelerator based on a tensegrity structure

Deployable aerodynamic decelerators are more suitable for planetary exploration missions than rigid aerodynamic decelerators. Their sizes are less limited by the launch vehicle shroud, allowing heavier payloads to be delivered to the surfaces of planets. Aiming at the problem of lightweight designs in deployable aerodynamic decelerators, a deployable tensegrity structure composed of two frustum tensegrity units and a prism tensegrity unit is proposed. To design the deployable tensegrity structure, a self-equilibrium model that is especially suitable for the structure with rotational symmetry is established according to prestressability conditions and force equilibrium equations. Based on the deployable tensegrity structure, the deployment mechanism, with the action of drive units, is designed to realize the transformation from the stowed state to the deployed state of the decelerator. The tensegrity structure always has rotational symmetry to make the deployment process highly synchronized due to the fact that drive units can work simultaneously. During the deployment process, the transverse distance and longitudinal distance are used to measure the deployment degree of the tensegrity structure. The types of axial forces for members are used to determine whether the tensegrity structure is stable. The result shows that the deployment process has continuity and that the tensegrity structure always stays stable.

A deep learning-based local feature extraction method for improved image matching and surface reconstruction from Yutu-2 PCAM images on the Moon

Topographic reconstruction of the lunar surface or other planets is important to engineering applications and scientific research in a planetary exploration mission. The typical methods of terrain reconstruction are usually based on photogrammetry techniques. Structure-from-Motion (SfM) is one of the most effective and commonly used photogrammetric technologies that estimate three-dimensional structures from two-dimensional image sequences. To find correspondences and align photos, SfM approaches require invariant features, such as Scale Invariant Feature Transform (SIFT). The hand-crafted features, however, seriously degrade performance due to weak texture and low light conditions causing image matching failure, which will directly affect the accuracy and robustness of reconstruction. Robust local descriptors based on deep learning outperform hand-crafted descriptors since convolutional neural networks are more robust than hand-engineered representations. Therefore, a novel and robust deep learning-based local feature extraction method is proposed, comprising two branch networks integrated with attention mechanisms for generating reliable keypoints and descriptors, respectively. Furthermore, a 3D terrain surface reconstruction workflow is constructed by combining it with the modern advanced image matching method and SfM system. The effectiveness of the proposed method and the workflow were verified in experiments using Panoramic Camera (PCAM) images acquired from three waypoints explored by the Yutu-2 lunar rover during the Chang’e-4 mission. We also illustrate how our approach supports other applications, such as creating panoramic mosaics of surface imagery. This provides a new and powerful method for planetary terrain reconstruction at a high spatial resolution that can meet the requirements for rover navigation and positioning, as well as geological analysis of the Moon and other planets. The source codes developed in this study are openly available at https://github.com/Atypical-Programmer/Deep_Reconstruction_Workflow.

Hierarchical Motion Planning Under Probabilistic Temporal Tasks and Safe-Return Constraints

Safety is crucial for robotic missions within an uncertain environment. Common safety requirements such as collision avoidance are only state-dependent, which can be restrictive for complex missions. In this work, we address a more general formulation as safe-return constraints, which require the existence of a return-policy to drive the system back to a set of safe states with high probability. The robot motion is modeled as a Markov Decision Process (MDP) with probabilistic labels, which can be highly non-ergodic. The robotic task is specified as Linear Temporal Logic (LTL) formulas over these labels, such as surveillance and transportation. We first provide theoretical guarantees on the re-formulation of such safe-return constraints, and a baseline solution based on computing two complete product automata. Furthermore, to tackle the computational complexity, we propose a hierarchical planning algorithm that combines the feature-based symbolic and temporal abstraction with constrained optimization. It synthesizes simultaneously two dependent motion policies: the outbound policy minimizes the overall cost of satisfying the task with a high probability, while the return policy ensures the safe-return constraints. The problem formulation is versatile regarding the robot model, task specifications and safety constraints. The proposed hierarchical algorithm is more efficient and can solve much larger problems than the baseline solution, with only a slight loss of optimality. Numerical validations include simulations and hardware experiments of a search-and-rescue mission and a planetary exploration mission over various system sizes.

Development of Sensitive Methods for the Detection of Minimum Concentrations of DNA on Martian Soil Simulants.

Several methods used for the quantification of DNA are based on UV absorbance or the fluorescence of complexes with intercalator dyes. Most of these intercalators are used in gels to visualize DNA and its structural integrity. Due to many extraterrestrial samples, such as meteorites or comets, which are likely to contain very small amounts of biological material, and because the ability to detect this material is crucial for understanding the origin and evolution of life in the universe, the development of assays that can detect DNA at low limits and withstand the rigors of space exploration is a pressing need in the field of astrobiology. In this study, we present a comparison of optimized protocols used for the fast and accurate quantification of DNA using common intercalator dyes. The sensitivity of assays exceeded that generated by any commercial kit and allowed for the accurate quantification of minimum concentrations of DNA. The methods were successful when applied to the detection and measurement of DNA spiked on soil samples. Furthermore, the impact of UV radiation as a harsh condition on the surface of Mars was assessed by DNA degradation and this was also confirmed by gel electrophoresis. Overall, the methods described provide economical, simple-step, and efficient approaches for the detection of DNA and can be used in future planetary exploration missions as tests used for the extraction of nucleic acid biosignatures.

Vision-Based Virtual Simulation Platform for Planetary Rovers

In order to evaluate the feasibility of planetary exploration missions, it is imperative to construct planetary terrain environments on the ground. Nevertheless, the implementation of this method is characterized by time-consuming and challenging factors. By contrast, resorting to simulation approaches represents a cost-effective and high-efficiency alternative, which can facilitate the simulation validation of planetary rover exploration missions in an effective manner. In this paper, a modular planet rover simulation platform is proposed; by building and overlaying multiple feature layers corresponding to the surface of the planet, we realize the simulation of high-resolution fine terrain and through adjusting the terrain parameters to meet the needs of different simulated terrain. The experimental results show that we have built a scene to satisfy the requirements of visual effects and physically realistic characteristics of simulation. Using an improved PatchMatch stereomethod to recover images captured real time by a planetary rover navigation camera in a virtual environment provided more complete three-dimensional terrain data for subsequent simulation validation of local path planning. Finally, a simulation environment that combines high-fidelity visual effects and kinematic characteristics supports visualizing the simulation platform: we propose a path planning method using global planning combined with local obstacle avoidance, and we obtain the optimal path that satisfies the kinematic constraints.

Poroviscoelastic Gravitational Dynamics

AbstractGlobal‐scale periodic deformation has been studied using the (visco)elastic gravitational theory, which assumes a planetary body consists of solid or liquid layers. Recent planetary exploration missions, however, suggest that a global layer of a mixture of solid and liquid exists in several planetary bodies. This study provides a theory of periodic deformation of such a layer unifying the viscoelastic gravitational theory with the theory of poroelasticity without introducing additional constraints. The governing equation system and a formulation suitable for numerical calculation are given. Equations used to calculate the energy dissipation rate are also given. The analytical solutions for a homogeneous sphere are obtained using an eigenvalue approach. Simple numerical calculations assuming a homogeneous sphere reveal that a numerical instability occurs if a thick porous layer, a low permeability, or a high frequency is assumed. This instability can be avoided by choosing an appropriate interior structure model that is numerically equivalent. Different simple numerical calculations adopting a multilayered, radially varying interior profile reveal that the radial profile of the tidal heating rate for a fluid‐saturated porous layer and that for a low‐viscosity solid layer are completely different. In addition, the radial variation in porosity can lead to a factor of ∼100 increase in the local heating rate. These results indicate that future studies should consider a wider variety of detailed interior structure models.

A Simple Biochemical Method for the Detection of Proteins as Biomarkers of Life on Martian Soil Simulants and the Impact of UV Radiation.

The search for life on other planets relies on the detection of biosignatures of life. Many macromolecules have been suggested as potential targets, among which are proteins that are considered vital components of life due to their essential roles in forming cellular structures, facilitating cellular communication and signaling, and catalyzing metabolic reactions. In this context, accurate quantification of protein signatures in soil would be advantageous, and while several proposed methods exist, which are limited by their sensitivity and specificity, their applicability needs further testing and validation. To this aim, we optimized a Bradford-based assay with high sensitivity and reproducibility and a simple protocol to quantify protein extracted from a Martian soil simulant. Methods for protein spiking, extraction, and recovery were optimized, using protein standards and bacterial proteins as representative models. The proposed method achieved high sensitivity and reproducibility. Taking into account that life remains could exist on the surface of Mars, which is subjected to UV radiation, a simulation of UV exposure was performed on a spiked soil simulant. UV radiation degraded the protein spike, thus highlighting the importance of searching for the remaining signal from degraded proteins. Finally, the applicability of the method was explored in relation to the storage of the reagent which was stable even up to 12 months, thus making its application possible for future planetary exploration missions.

Heuristics for Multi-Vehicle Routing Problem Considering Human-Robot Interactions

Autonomous mobile robots (AMRs) are being used extensively in civilian and military applications for applications such as underground mining, nuclear plant operations, planetary exploration, intelligence, surveillance and reconnaissance (ISR) missions and manned-unmanned teaming. We consider a multi-objective, multiple-vehicle routing problem in which teams of manned ground vehicles (MGVs) and AMRs are deployed respectively in a leader-follower framework to execute missions with differing requirements for MGVs and AMRs while considering human-robot interactions (HRI). HRI studies highlight the costs of managing a team of follower AMRs by a leader MGV. This paper aims to compute feasible visit sequences, replenishments, team compositions and number of MGV-AMR teams deployed such that the requirements for MGVs and AMRs for the missions are met and the routing, replenishment, HRI and team deployment costs are at minimum. The problem is first modeled as a a mixed-integer linear program (MILP) that can be solved to optimality by off-the-shelf commercial solvers for small-sized instances. For larger instances, a variable neighborhood search algorithm is offered to compute near optimal solutions and address the challenges that arise when solving the combinatorial multi-objective routing optimization problem. Finally, computational experiments that corroborate the effectiveness of the proposed algorithms are presented.

Terrain classification using mars raw images based on deep learning algorithms with application to wheeled planetary rovers

Scene information plays a crucial role in motion control, attitude perception, and path planning for wheeled planetary rovers (WPRs). Terrain recognition is the fundamental component of scene recognition. Due to the rich information, visual sensors are usually used in terrain classification. However, teleoperation delay prevents WPRs from using visual information efficiently. End-to-end learning method of deep learning (DL) that does not need complex image preprocessing was proposed to deal with this issue. This paper first built a terrain dataset (consists of loose sand, bedrock, small rock, large rock, and outcrop) using real Mars images to directly support You Only Look Once (YOLOv5) to test its performance on terrain classification. Because the capability of end-to-end training scheme is positively correlated with dataset, the performance of YOLOv5 can be significantly improved by exploiting orders of magnitude more data. The best combination of hyperparameters and models was achieved by slightly tuning YOLOv5, and data augmentation was also applied to optimize its accuracy. Furthermore, its performance was compared with two other end-to-end network architectures. Deep learning algorithms can be used in the future planetary exploration missions, such as WPRs autonomy improvement, traversability analysis, and avoiding getting trapped.

TeCVP: A Time-Efficient Control Method for a Hexapod Wheel-Legged Robot Based on Velocity Planning.

Addressing the problem that control methods of wheel-legged robots for future Mars exploration missions are too complex, a time-efficient control method based on velocity planning for a hexapod wheel-legged robot is proposed in this paper, which is named time-efficient control based on velocity planning (TeCVP). When the foot end or wheel at knee comes into contact with the ground, the desired velocity of the foot end or knee is transformed according to the velocity transformation of the rigid body from the desired velocity of the torso which is obtained by the deviation of torso position and posture. Furthermore, the torques of joints can be obtained by impedance control. When suspended, the leg is regarded as a system consisting of a virtual spring and a virtual damper to realize control of legs in the swing phase. In addition, leg sequences of switching motion between wheeled configuration and legged configuration are planned. According to a complexity analysis, velocity planning control has lower time complexity and less times of multiplication and addition compared with virtual model control. In addition, simulations show that velocity planning control can realize stable periodic gait motion, wheel-leg switching motion and wheeled motion and the operation time of velocity planning control is about 33.89% less than that of virtual model control, which promises a great prospect for velocity planning control in future planetary exploration missions.

Short-Arc Horizon-Based Optical Navigation by Total Least-Squares Estimation

Horizon-based optical navigation (OPNAV) is an attractive solution for deep space exploration missions, with strong autonomy and high accuracy. In some scenarios, especially those with large variations in spacecraft distance from celestial bodies, the visible horizon arc could be very short. In this case, the traditional Christian–Robinson algorithm with least-squares (LS) estimation is inappropriate and would introduce a large mean residual that can be even larger than the standard deviation (STD). To solve this problem, a simplified measurement covariance model was proposed by analyzing the propagation of measurement errors. Then, an unbiased solution with the element-wise total least-squares (EW-TLS) algorithm was developed in which the measurement equation and the covariance of each measurement are fully considered. To further simplify this problem, an approximate generalized total least-squares algorithm (AG-TLS) was then proposed, which achieves a non-iterative solution by using approximate measurement covariances. The covariance analysis and numerical simulations show that the proposed algorithms have impressive advantages in the short-arc horizon scenario, for the mean residuals are always close to zero. Compared with the EW-TLS algorithm, the AG-TLS algorithm trades a negligible accuracy loss for a huge reduction in execution time and achieves a computing speed comparable to the traditional algorithm. Furthermore, a simulated navigation scenario reveals that a short-arc horizon can provide reliable position estimates for planetary exploration missions.

Impact of reduced weight on motor and cognitive function in astronaut analogs: A simulated lunar gravity workload study

The purpose of this study was to investigate changes in gait patterns, motor dexterity, cognition, neuromuscular performance, neural activation of the prefrontal cortex, and perception of effort and exertion during a 60-min walking trial at a reduced body weight using a positive pressure treadmill (PPT). Eleven screened astronaut analog participants performed 60-min PPT sessions at either 100% (PPT100) or 20% body weight (PPT20) which were separated into six 10-min segments. During each segment, a 1-min gait analysis, Pegboard Task, Assembly Task, and Memory Recall Task were performed. Step frequency, rating of perceived physical and cognitive exertion, electromyographic amplitude and frequency from the vastus lateralis, metabolic markers, and functional near-infrared spectroscopy (fNIRS) of the prefrontal cortex were obtained for all segments during each condition. The primary findings of the study indicated that PPT20 was able to induce differences in step frequency relative to PPT100 and that PPT100 resulted in greater metabolic expenditures and electromyographic amplitude compared to PPT20. The PPT20, however, exhibited increased fNIRS prefrontal cortex activity during the Assembly Task and Memory Recall compared to the PPT100. In addition, the PPT20 resulted in an increase in Memory Recall errors compared to PPT100. In conclusion, the findings of the present study indicated that altered gait patterns associated with reduced weight does not impact basic motor dexterity tasks, such as gait and Pegboard Tasks, but does increase the difficulty of Assembly Tasks and Memory Recall in astronaut analogs. Thus, novel gait patterns can impact the cognitive loads that astronaut analogs can perform. Special considerations should be given to the impact that gravity-induced changes in gait patterns have on motor and cognitive tasks in future planetary exploration missions.

A statistical approach to removing diffraction from X-ray fluorescence spectra

Diffraction peaks can lead to inaccurate elemental abundances in X-ray fluorescence maps. However, manually removing diffraction peaks is laborious while existing automated methods unnecessarily remove significant volumes of fluorescence data. Here we propose a new automated method to remove diffraction from multiple-detector spectra based on a statistical threshold. This method eliminates only the diffraction peaks from the spectra, retaining more of the fluorescence data and increasing the information content available for diffraction-free elemental quantification and mapping. By retaining the majority of the fluorescence data, the proposed method does not require an increase in dwell times or additional data to be collected to compensate for the removed fluorescence data. This method is therefore particularly valuable in instances where measurement time and data volumes are highly restricted, such as for instruments on planetary exploration missions like the Planetary Instrument for X-ray Lithochemistry on NASA's Mars 2020 mission Perseverance Rover.

UAV4PE: An Open-Source Framework to Plan UAV Autonomous Missions for Planetary Exploration

Autonomous Unmanned Aerial Vehicles (UAV) for planetary exploration missions require increased onboard mission-planning and decision-making capabilities to access full operational potential in remote environments (e.g., Antarctica, Mars or Titan). However, the uncertainty introduced by the environment and the limitation of available sensors has presented challenges for planning such missions. Partially Observable Markov Decision Processes (POMDPs) are commonly used to enable decision-making and mission-planning processes that account for environmental, perceptional (extrinsic) and actuation (intrinsics) uncertainty. Here, we propose the UAV4PE framework, a testing framework for autonomous UAV missions using POMDP formulations. This framework integrates modular components for simulation, emulation, UAV guidance, navigation and mission planning. State-of-the-art tools such as python, C++, ROS, PX4 and JuliaPOMDP are employed by the framework, and we used python data-science libraries for the analysis of the experimental results. The source code and the experiment data are included in the UAV4PE framework. The POMDP formulation proposed here was able to plan and command a UAV-based planetary exploration mission in simulation, emulation and real-world experiments. The experiments evaluated key indicators such as the mission success rate, the surface area explored and the number of commands (actions) executed. We also discuss future work aimed at improving the UAV4PE framework, and the autonomous UAV mission planning formulation for planetary exploration.