Search Results (84)

Accurate localization is a key requirement for deep-space exploration, enabling spacecraft operations with limited ground support. Upcoming commercial and scientific missions to the Moon are designed to extensively use optical measurements during low-altitude orbital phases, descent and landing, and high-risk operations, due to the versatility and suitability of these data for onboard processing. Navigation frameworks based on optical data analysis have been developed to support semi- or fully-autonomous onboard systems, enabling precise relative localization. To achieve high-accuracy navigation, optical data have been combined with complementary measurements using sensor fusion techniques. Absolute localization is further supported by integrating onboard maps of cataloged surface features, enabling position estimation in an inertial reference frame. This study presents a navigation framework for optical image processing aimed at supporting the autonomous operations of lunar orbiters. The primary objective is a comprehensive characterization of the navigation camera’s properties and performance to ensure orbit determination uncertainties remain below 1% of the spacecraft altitude. In addition to an analysis of measurement noise, which accounts for both hardware and software contributions and is evaluated across multiple levels consistent with prior literature, this study emphasizes the impact of process noise on orbit determination accuracy. The mismodeling of orbital dynamics significantly degrades orbit estimation performance, even in scenarios involving high-performing navigation cameras. To evaluate the trade-off between measurement and process noise, representing the relative accuracy of the navigation camera and the onboard orbit propagator, numerical simulations were carried out in a synthetic lunar environment using a near-polar, low-altitude orbital configuration. Under nominal conditions, the optical measurement noise was set to 2.5 px, corresponding to a ground resolution of approximately 160 m based on the focal length, pixel pitch, and altitude of the modeled camera. With a conservative process noise model, position errors of about 200 m are observed in both transverse and normal directions. The results demonstrate the estimation framework’s robustness to modeling uncertainties, adaptability to varying measurement conditions, and potential to support increased onboard autonomy for small spacecraft in deep-space missions. Full article

Advanced navigation capabilities are essential for precise landing operations, enabling access to critical lunar sites and supporting future lunar infrastructure. To achieve accurate positioning, innovative navigation methods leveraging neural network frameworks are being developed to detect distinctive lunar surface features, such as craters, from imaging data. By matching detected features with known landmarks stored in an onboard reference database, key navigation measurements are retrieved to refine the spacecraft trajectory, enabling real-time planning for hazard avoidance. This work presents a crater-based navigation system for planetary descent operations, which leverages a robust machine learning approach for crater detection in optical images. A thorough analysis of the attainable detection accuracies was performed by evaluating the network performance on diverse sets of synthetic images rendered at different illumination conditions through a custom Blender-based pipeline. Simulation campaigns, based on the JAXA Smart Lander for Investigating Moon mission, were then carried out to demonstrate the system’s performance, achieving final position errors consistent with 3 − $\sigma$ uncertainties lower than 100 m on the horizontal plane at altitudes as low as 10 km. This level of accuracy is key to achieving enhanced control during the approach and vertical descent phases, thereby ensuring operational safety and facilitating precise landing. Full article

Since 1969, 55 years ago, Lunar Laser Ranging (LLR) has provided accurate and precise (down to ~1 cm RMS) measurements of the Moon’s orbit thanks to the Apollo and Lunokhod Cube Corner Retroreflector (CCR) Laser Retroreflector Arrays (LRAs) deployed on the Moon. Nowadays, the current level of precision of these measurements is largely limited by the lunar librations affecting the old generation of LRAs. To improve this situation, next-generation libration-free retroreflectors are necessary. To this end, the Satellite/lunar/GNSS laser ranging/altimetry and cube/microsat Characterization Facilities Laboratory (SCF_Lab) at the Istituto Nazionale di Fisica Nucleare—Laboratori Nazionali di Frascati (INFN-LNF), in collaboration with the University of Maryland (UMD) and supported by the Italian Space Agency (ASI), developed MoonLIGHT (Moon Laser Instrumentation for General relativity High-accuracy Tests), a single large CCR with a front face diameter of 100 mm, nominally unaffected by librations, and with optical performances comparable to the Apollo/Lunokhod LRAs of CCRs. Such a big CCR (hereafter, ML100) is mounted into a specifically devised, designed, and manufactured robotic actuator, funded by the European Space Agency (ESA), the so-called MoonLIGHT Pointing Actuator (MPAc), which, once its host craft has landed on the Moon, will finely align the front face of the ML100 towards the Earth. The (optical) performances of such a piece of hardware, MoonLIGHT+MPAc, were tested in/by the SCF_Lab in order to ensure that it was space flight ready before its integration onto the deck of the host craft. After its successful deployment on the Moon, additional and better-quality LLR data (down to ~ 1 mm RMS or better for the contribution of the laser retroreflector instrument, MoonLIGHT, to the total LLR error budget) will be available to the community for future and enhanced tests of gravitational theories. Full article

Safe and reliable lunar landings are crucial for future exploration of the Moon. The regolith ejected by a lander’s rocket exhaust plume represents a significant obstacle in achieving this goal. It prevents spacecraft from reliably utilizing their navigation sensors to monitor their trajectory and spot emerging surface hazards as they near the surface. As part of NASA’s 2024 Human Lander Challenge (HuLC), the team at the University of Michigan developed an innovative concept to help mitigate this issue. We developed and implemented a machine learning (ML)-based sensor fusion system, ARC-LIGHT, that integrates sensor data from the cameras, lidars, or radars that landers already carry but disable during the final landing phase. Using these data streams, ARC-LIGHT will remove erroneous signals and recover a useful detection of the surface features to then be used by the spacecraft to correct its descent profile. It also offers a layer of redundancy for other key sensors, like inertial measurement units. The feasibility of this technology was validated through development of a prototype algorithm, which was trained on data from a purpose-built testbed that simulates imaging through a dusty environment. Based on these findings, a development timeline, risk analysis, and budget for ARC-LIGHT to be deployed on a lunar landing was created. Full article

The Chang’e-6 (CE-6) landing area on the far side of the Moon is located in the southern part of the Apollo basin within the South Pole–Aitken (SPA) basin. The statistical analysis of impact craters in this region is crucial for ensuring a safe landing and supporting geological research. Aiming at existing impact crater identification problems such as complex background, low identification accuracy, and high computational costs, an efficient impact crater automatic detection model named YOLOv8-LCNET (YOLOv8-Lunar Crater Net) based on the YOLOv8 network is proposed. The model first incorporated a Partial Self-Attention (PSA) mechanism at the end of the Backbone, allowing the model to enhance global perception and reduce missed detections with a low computational cost. Then, a Gather-and-Distribute mechanism (GD) was integrated into the Neck, enabling the model to fully fuse multi-level feature information and capture global information, enhancing the model’s ability to detect impact craters of various sizes. The experimental results showed that the YOLOv8-LCNET model performs well in the impact crater detection task, achieving 87.7% Precision, 84.3% Recall, and 92% AP, which were 24.7%, 32.7%, and 37.3% higher than the original YOLOv8 model. The improved YOLOv8 model was then used for automatic crater detection in the CE-6 landing area (246 km × 135 km, with a DOM resolution of 3 m/pixel), resulting in a total of 770,671 craters, ranging from 13 m to 19,882 m in diameter. The analysis of this impact crater catalogue has provided critical support for landing site selection and characterization of the CE-6 mission and lays the foundation for future lunar geological studies. Full article

In physics, we construct idealized mathematical models in order to explain various phenomena which we observe or create in our laboratories. In this article, I recall how sophisticated mathematical models evolved from the concept of a number created thousands of years ago, and I discuss some challenges and open questions in quantum foundations and in the Standard Model. We liberated nuclear energy, landed on the Moon and built ‘quantum computers’. Encouraged by these successes, many believe that when we reconcile general relativity with quantum theory we will have the correct theory of everything. Perhaps we should be much humbler. Our perceptions of reality are biased by our senses and by our brain, bending them to meet our priors and expectations. Our abstract mathematical models describe only in an approximate way different layers of physical reality. To describe the motion of a meteorite, we can use a concept of a material point, but the point-like approximation breaks completely when the meteorite hits the Earth. Similarly, thermodynamic, chemical, molecular, atomic, nuclear and elementary particle layers of physical reality are described using specific abstract mathematical models and approximations. In my opinion, the theory of everything does not exist. Full article

Space-based observations of the total lunar eclipse on 21 January 2019 were conducted using the geostationary Earth-orbiting satellite Gaofen-4 (GF-4). This study represents a pioneering effort to address the observational gap in full-disk lunar eclipse photometry from space. With its high resolution and ability to capture the entire lunar disk, GF-4 enabled both quantitative and qualitative analyses of the variations in lunar brightness, as well as spectra and color changes, across two spatial dimensions, from the whole lunar disk to resolved regions. Our results indicate that before the totality phase of the lunar eclipse, the irradiance of the Moon diminishes to below approximately 0.19% of that of the uneclipsed Moon. Additionally, we observed an increase in lunar brightness at the initial entry into the penumbra. This phenomenon is attributed to the opposition effect, providing scientific evidence for this unexpected behavior. To investigate detailed spectral variations, specific calibration sites, including the Chang’E-3 landing site, MS-2 in Mare Serenitatis, and the Apollo 16 highlands, were analyzed. Notably, the red-to-blue ratio dropped below 1 near the umbra, contradicting the common perception that the Moon appears red during lunar eclipses. The red/blue ratio images reveal that as the Moon enters Earth’s umbra, it does not simply turn red; instead, a blue-banded ring appears at the boundary due to ozone absorption and the lunar surface composition. These findings significantly enhance our understanding of atmospheric effects on lunar eclipses and provide crucial reference information for the future modeling of lunar eclipse radiation, promoting the integration of remote sensing science with astronomy. Full article

Particle radiation on the Moon is influenced by a combination of galactic cosmic rays, high-energy solar particles, and secondary particles interacting on the lunar surface. When China’s Chang’e-7 lander lands at the Moon’s South Pole, it will encounter this complex radiation environment. Therefore, a payload detection technology was developed to comprehensively measure the energy spectrum, direction, and radiation effects of medium- and high-energy charged particles on the lunar surface. During the ground development phase, the payload performance was tested against the design specifications. The verification results indicate that the energy measurement ranges are 30 keV to 300 MeV for protons, 30 keV to 12 MeV for electrons, and 8 to 400 MeV/n for heavy ions. The energy resolution is 10.81% for 200 keV electrons of the system facing the lunar surface; the dose rate measurement sensitivity is 7.48 µrad(Si)/h; and the LET spectrum measurement range extends from 0.001 to 37.014 MeV/(mg/cm²). These comprehensive measurements are instrumental in establishing a lunar surface particle radiation model, enhancing the understanding of the lunar radiation environment, and supporting human lunar activities. Full article

The Chang’E-6 (CE-6) mission successfully returned 1935.3 g of lunar soil samples from the Apollo basin within the South Pole–Aitken basin. One of its scientific objectives is to investigate the subsurface structure and regolith thickness at the landing site. Using remote sensing datasets, we estimated the regolith and basalt thicknesses at the landing site by employing the crater morphology method and crater excavation technique. A total of 53 concentric craters and 108 fresh craters with varying excavation depths were identified. Our results indicate that the regolith thickness at the CE-6 landing site ranges from 1.1 to 7.0 m, with an average thickness of 3.5 m. Beneath the regolith, the basalt layer consists of high-Ti basalt overlaying low-Ti basalt, with a total thickness of approximately 64 to 82 m, of which the high-Ti basalt layer accounts for about 22 to 30 m. Based on the local geological history, we proposed a stratigraphy at the CE-6 landing site. These findings provide valuable geological context for interpreting the Lunar Penetrating Radar data and analyzing the returned samples. Full article

The samples from lunar farside have great significance for the study of the Moon, and even the solar system. Chang’e-6 landed successfully on the southern mare of the Apollo basin and returned ~2 kg of samples from lunar farside. To provide a better understanding for the background of the returned samples, we conducted detailed crater size-frequency distribution (CSFD) measurements in the Chang’e-6 landing region, the southern mare of the Apollo basin. The southern mare is divided into the western mare (W region) and the eastern mare (E region), and then subdivided into five subunits (W1, W2, W3, W4, W5) and three units (E1, E2, E3), respectively, according to the elevation, TiO₂, and FeO abundances. Within the W2 and W5 region, more detailed subunits were separated out. The results show that the southern mare surface was active during two epochs, the Imbrian period and the Eratosthenian period. The basalt eruption lasted for ~1.7 Ga, from 3.28 Ga of the eastern mare to 1.54 Ga of the western mare. The W region is younger than the E region, while the three units of the E region have an age of ~3.2 Ga. The ages of the western mare basalts range from 2.98 Ga to 1.54 Ga, lasting for 1.4 Ga. It is worth noting that the age of the basalt at the Chang’e-6 sampling site is ~1.68 Ga, indicating the samples returned may include components with this very young age. Full article

This article explores the relationship between genealogy and the environment as a pathway towards decolonising indigenous minds. In Māori worldviews, everything is categorised, organised, and understood through whakapapa, or genealogy. Whakapapa resides within the land and water, safeguarding ancestral stories as they weave through time, space, and place. The environment serves as a powerful tool for maintaining, reclaiming, and reinforcing indigeneity. Strengthening the connections between whakapapa and the environment offers significant avenues for decolonising Indigenous minds, by recalibrating and releasing colonised ways of being to embody mauritau (mindfulness) through whenua kura (placefulness). Unlike Cartesian dualism, which separates the body and mind, the Māori conception of the mind is multifaceted and embodied. The mind is thought to be situated in the solar plexus, emotions in the gut, and connection to spirit in the head, all of which are deeply rooted in whakapapa and the enduring ties to ancestors and place. Whakapapa’s connections to the land, water, animals, and spiritual entities are imbued with narratives that aid in recollection and provide profound cultural context to place. These narratives offer pathways for communion with the land and water, enabling sensitivity to environmental cues, such as changing seasons, solstices, moon phases, star cycles, and natural rhythms within our inner landscapes of body, heart, and mind, fostering a sense of placefulness. Full article

The safety of lunar landing sites directly impacts the success of lunar exploration missions. This study develops a data-driven predictive model based on machine learning, focusing on engineering safety to assess the suitability of lunar landing sites and provide insights into key factors and feature representations. Six critical engineering factors were selected as constraints for evaluation: slope, elevation, roughness, hillshade, optical maturity, and rock abundance. The XGBoost model was employed to simulate and predict the characteristics of landing areas and Bayesian optimization was used to fine-tune the model’s key hyperparameters, enhancing its predictive performance. The results demonstrate that this method effectively extracts relevant features from multi-source remote sensing data and quantifies the suitability of landing zones, achieving an accuracy of 96% in identifying landing sites (at a resolution of 0.1° × 0.1°), with AUC values exceeding 95%. Notably, slope was recognized as the most critical factor affecting safety. Compared to assessment processes based on Convolutional Neural Networks (CNNs) and Random Forest (RF) models, XGBoost showed superior performance in handling missing values and evaluating feature importance accuracy. The findings suggest that the BO-XGBoost model shows notable classification performance in evaluating the suitability of lunar landing sites, which may provide valuable support for future landing missions and contribute to optimizing lunar exploration efforts. Full article

China’s Chang’E-5 (CE-5) mission has successfully landed in the Northern Oceanus Procellarum of the Moon. Lunar mineralogical spectrometer (LMS), as one of the important payloads onboard CE-5 Lander–Ascender Combination, aims to study the physical and compositional properties of the landing area. This paper applies the Legendre phase function to correct the photometric effects on the LMS in situ spectra and reveal the photometric characteristic of the CE-5 landing area. LMS obtained the reflectance spectra in various geometric configurations by performing full-view scanning of the CE-5 landing area. By fitting these LMS spectral data, the parameters $b=-0.29$ and $c=0.44$ of the Legendre phase function were obtained. This indicates the strong forward scattering characteristic of the CE-5 landing area, which is similar to that of the Chang’E-4 (CE-4) landing area, and the side scattering is weaker than that of CE-4. In addition, we derived the FeO content of the landing area using the photometric-corrected LMS spectral data. Our results demonstrate that the estimated FeO content of the landing area is close to the laboratory measured data of the returned samples. The LMS in situ reflectance data will contribute to a better understanding of the physical and mineralogical properties of the CE-5 landing area. Full article

This article explores the influence of lunar regolith and rover structure, such as mast design and material composition, on antenna parameters. It focuses on the distinctive difficulties of communication in the lunar environment, which need specialized antenna solutions. This study specifically examines the performance of antennas on the lunar Rashid rover within the Atlas crater, a landing site on the moon, considering two antenna types: a sleeve dipole antenna and an all-metal patch antenna. Thermal analyses reveal temperatures in the Atlas crater can exceed 80 °C during lunar mid-day. The findings highlight the effect of different materials used as thermal coatings for Rashid rover antennas, as well as the influence of rover materials on antenna performance. Furthermore, this study extends to analyze the conductivity and depth of lunar regolith within the Atlas crater. Given the critical role of antennas in wireless communication, understanding how lunar regolith properties affect antenna performance is essential. This research contributes to the creation of a strong communication system for the Rashid rover and future lunar missions by considering the features of the lunar regolith in addition to the rover’s size and material attributes. Full article

Lunar DRO pinpoint return is the final stage of manned deep space exploration via a lunar DRO station. A re-entry capsule suffers from complicated dynamic and thermal effects during an entire flight. The optimization of the lunar DRO return trajectory exhibits strong non-linearity. To obtain a global optimal return trajectory, an entire-flight lunar DRO pinpoint return model including a Moon–Earth transfer stage and an Earth atmosphere re-entry stage is constructed. A re-entry point on the atmosphere boundary is introduced to connect these two stages. Then, an entire-flight global optimization framework for lunar DRO pinpoint return is developed. The design of the entire-flight return trajectory is simplified as the optimization of the re-entry point. Moreover, to further improve the design efficiency, a rapid landing point prediction method for the Earth re-entry is developed based on a deep neural network. This predicting network maps the re-entry point in the atmosphere and the landing point on Earth with respect to optimal control re-entry trajectories. Numerical simulations validate the optimization accuracy and efficiency of the proposed methods. The entire-flight return trajectory achieves a high accuracy of the landing point and low fuel consumption. Full article