Search Results (212)

Lunar-rover path planning is a key topic in lunar exploration research, with safety and computational efficiency critical for achieving long-distance planning. This paper proposes a distributed path-planning method that considers multiple lunar environmental factors, addressing the issues of inadequate safety considerations and low computational efficiency in current research. First, a set of safety evaluation rules is constructed by considering factors such as terrain slope, roughness, illumination, and rock abundance. Second, a distributed path-planning strategy based on a safety-map tile pyramid (DPPS-STP) is proposed, using a weighted A* algorithm with hash table-based open and closed lists (OC-WHT-A*) on a Spark cluster for efficient and safer path planning. Additionally, high-resolution digital orthophoto maps (DOM) are utilized for small crater detection, enabling more refined path planning built upon the overall mission-planning result. The method was validated in four lunar regions with distinct characteristics. The results show that DPPS-STP, which considers multiple environmental factors, effectively reduces the number of hazardous nodes and avoids crater obstacles. For long-distance tasks, it achieves an average speedup of up to 11.5 times compared to the single-machine OC-WHT-A*, significantly improving computational efficiency. Full article

The intensified charge-coupled device (ICCD), known for its exceptional low-light detection performance and time-gating capability, has been widely applied in remote Raman spectroscopy systems. However, existing ICCDs face significant challenges in meeting the comprehensive requirements of high gating speed, high sensitivity, high resolution, miniaturization, and adaptability to extreme environments for the upcoming lunar remote Raman spectroscopy missions. To address these challenges, this study developed a microstrip photocathode (MP-ICCD) specifically designed for lunar remote Raman spectroscopy. A comprehensive testing method was also proposed to evaluate critical performance parameters, including optical gating width, optimal gain voltage, and relative resolution. The MP-ICCD was integrated into a prototype remote Raman spectrometer equipped with a 40 mm aperture telescope and tested under outdoor sunlight conditions. The experimental results demonstrated that the developed MP-ICCD successfully achieved a minimum optical gating width of 6.0 ns and an optimal gain voltage of 870 V, with resolution meeting the requirements for Raman spectroscopy detection. Under outdoor solar illumination, the prototype remote Raman spectrometer utilizing the MP-ICCD accurately detected the Raman spectra of typical lunar minerals, including quartz, olivine, pyroxene, and plagioclase, at a distance of 1.5 m. This study provides essential technical support and experimental validation for the application of MP-ICCD in lunar Raman spectroscopy missions. Full article

Raman spectroscopy is capable of precisely identifying and analyzing the composition and properties of samples collected from the lunar surface, providing crucial data support for lunar scientific research. However, in situ Raman spectroscopy on the lunar surface faces challenges such as weak Raman scattering from targets, alongside requirements for lightweight and long-distance detection. To address these challenges, time-gated Raman spectroscopy (TG-LRS) based on a passively Q-switched pulsed laser and a linear intensified charge-coupled device (ICCD), which enable simultaneous signal amplification and background suppression, has been developed to evaluate the impact of key operational parameters on Raman signal detection and to explore miniaturization optimization. The TG-LRS system includes a 40 mm zoom telescope, a passively Q-switched 532 nm pulsed laser, a fiber optic delay line, a miniature spectrometer, and a linear ICCD detector. It achieves an electronic gating width under 20 ns. Within a detection range of 1.1–3.0 m, the optimal delay time varies linearly from 20 to 33 ns. Raman signal intensity increases with image intensifier gain, while the signal-to-noise ratio peaks at a gain range of 800–900 V before declining. Furthermore, the effects of focal depth, telescope aperture, laser energy, and integration time were studied. The Raman spectra of lunar minerals were successfully obtained in the lab, confirming the system’s ability to suppress solar background light. This demonstrates the feasibility of in situ Raman spectroscopy on the lunar surface and offers strong technical support for future missions. 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 Narrow-Angle Cameras (NACs) onboard the Lunar Reconnaissance Orbiter Camera (LROC) capture lunar images that play a crucial role in current lunar exploration missions. Among these images, those of the Moon’s permanently shadowed regions (PSRs) are highly noisy, obscuring the lunar topographic features within these areas. While significant advancements have been made in denoising techniques based on deep learning, the direct acquisition of paired clean and noisy images from the PSRs of the Moon is costly, making dataset acquisition expensive and hindering network training. To address this issue, we employ a physical noise model based on the imaging principles of the LROC NACs to generate noisy pairs of images for the Moon’s PSRs, simulating realistic lunar imagery. Furthermore, inspired by the ideas of full-scale skip connections and self-attention models (Transformers), we propose a denoising method based on deep information convolutional neural networks. Using a dataset synthesized through the physical noise model, we conduct a comparative analysis between the proposed method and existing state-of-the-art denoising approaches. The experimental results demonstrate that the proposed method can effectively recover topographic features obscured by noise, achieving the highest quantitative metrics and superior visual results. Full article

SAR data from the lunar spaceborne Reconnaissance Orbiter’s (LRO) Mini-RF and Chandrayaan-1’s Mini-SAR provide valuable insights into the properties of the lunar surface. However, public lunar SAR data products are not properly registered and are limited by localization issues. Existing registration methods for Earth SAR have proven to be inadequate in their robustness for lunar data registration. And current research on methods for lunar SAR has not yet focused on producing globally registered datasets. To solve these problems, this article introduces a robust automatic registration method tailored for S-band Level-1 Mini-RF and Mini-SAR data with the assistance of lunar DEM. A simulated SAR image based on real lunar DEM data is first generated to assist the registration work, and then an offset calculation approach based on normalized cross-correlation (NCC) and specific processing, including background removal, is proposed to achieve the registration between the simulated image, and the real image. When applying Mini-RF images and Mini-SAR images, high robustness and good accuracy are exhibited, which produces fully registered datasets. After processing using the proposed method, the average error between Mini-RF images and DEM references was reduced from approximately 3000 m to about 100 m. To further explore the additional improvement of the proposed method, the registered lunar SAR datasets are used for further analysis, including a review of the circular polarization ratio (CPR) characteristics of anomalous craters. Full article

This study introduces a novel composite construction material composed of lunar regolith combined with PEEK in dry powder form. The work demonstrates significant advantages over alternative methods, primarily by reducing production power consumption and simplifying the manufacturing process. Building on previous research that explored binder optimization through process simplification and targeting predefined shapes, this work delves deeper into a comparative analysis of high-performance thermoplastics. Among the various options, PEEK demonstrates the most favorable properties. The study investigates key processing parameters and evaluates the effects of vacuum processing and temperature testing on mechanical properties. The research also evaluates the effects of vacuum processing and temperature testing to assess the material’s performance under lunar conditions. Comparative analysis is performed with standard performance of various reinforced and unreinforced concretes and with standard requirements for construction bricks as per ASTM standards. This shows that the composite, with an organic binder content as low as 5 wt%, has great potential. Notably, the improvements achieved through vacuum curing ensure compliance with lunar environmental conditions and alignment with most Earth-based engineering standards. Samples compacted at 7.50 MPa with 10 wt% binder, and tested at room temperature, achieve a compression strength of 16.3 MPa, exceeding that of industrial floor bricks and matching that of building bricks used on Earth. Bending strength (7.4 MPa) aligns with steel fiber-reinforced and high-strength concretes. Vacuum curing further enhances these properties, with an observed increase of +66% in bending strength and +33% in compression strength. Full article

Adequate water supplies are crucial for missions to the Moon, since water is essential for astronauts’ health. Ionic liquids (ILs) have been investigated for processing metal oxides, the main components of lunar regolith, to separate oxygen and metals. The IL must be diluted in the process. Recycling this diluted IL post-processing is important to reduce the materials required in resupply missions. In addition, water will be needed in lunar greenhouses for growing food and aiding in sustaining a habitable environment. Direct contact membrane distillation (DCMD) is a new technology for water purification that was examined in this study for its feasibility to concentrate IL. Hydrophobic membranes composed of polytetrafluoroethylene (PTFE) and polyvinylidene (PVDF) were found to hold promise in separating solutes from water to concentrate a diluted IL solution and to recover water. A bench-scale DCMD system was employed to test this method at temperatures of 50 °C, 65 °C, and 80 °C. Hence, the benefits and limitations of DCMD with PTFE and PVDF membranes were explored for the aqueous IL 1-ethyl-3 methylimidazolium hydrogen sulfate for DCMD performed at different temperatures. Full article

In recent years, the fusion of hyperspectral and multispectral images in remote sensing image processing still faces challenges, primarily due to their complexity and multimodal characteristics. Diffusion models, known for their stable training process and exceptional image generation capabilities, have shown good application potential in this field. However, when dealing with multimodal data, it may prove challenging for the models to fully capture the intricate relationships between the modalities, which may result in incomplete information integration and a small amount of remaining noise in the generated images. To address these problems, we propose a new model, KanDiff, for hyperspectral and multispectral image fusion. To address the differences in modal information between multispectral and hyperspectral images, KANDiff incorporates Kolmogorov–Arnold Networks (KAN) to guide the inputs. It helps the model understand the complex relationships between the modalities by replacing the fixed activation function in the traditional MLP with a learnable activation function. Furthermore, the image generated by the diffusion model may exhibit a small amount of the remaining noise. Convolutional Neural Networks (CNNs) effectively extract local features through their convolutional layers and achieve noise suppression via layer-by-layer feature representation. Therefore, the MergeCNN module is further introduced to enhance the fusion effect, resulting in smoother and more accurate outcomes. Experimental results on the public CAVE and Harvard datasets indicate that KanDiff has achieved improvements over current high-performance methods across several metrics, particularly showing significant enhancements in the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM), thus demonstrating superior performance. Additionally, we have created an image fusion dataset of the lunar surface, and KANDiff exhibits robust performance on this dataset as well. This work introduces an effective solution for addressing the challenges posed by missing high-resolution hyperspectral images (HRHS) data, which is essential for tasks including landing site selection and resource exploration within the realm of deep space exploration. Full article

Understanding rock hardness on extraterrestrial planets offers valuable insights into planetary geological evolution. Rock hardness correlates with morphological parameters, which can be extracted from navigation images, bypassing the time and cost of rock sampling and return. This research proposes a machine-learning approach to predict extraterrestrial rock hardness using morphological features. A custom dataset of 1496 rock images, including granite, limestone, basalt, and sandstone, was created. Ten features, such as roundness, elongation, convexity, and Lab color values, were extracted for prediction. A foundational model combining Random Forest (RF) and Support Vector Regression (SVR) was trained through cross-validation. The output of this model was used as the input for a meta-model, undergoing linear fitting to predict Mohs hardness, forming the Meta-Random Forest and Support Vector Regression (MRFSVR) model. The model achieved an R² of 0.8219, an MSE of 0.2514, and a mean absolute error of 0.2431 during validation. Meteorite samples were used to validate the MRFSVR model’s predictions. The model is used to predict the hardness distribution of extraterrestrial rocks using images from the Tianwen-1 Mars Rover Navigation and Terrain Camera (NaTeCam) and a simulated lunar rock dataset from an open-source website. The results demonstrate the method’s potential for enhancing extraterrestrial exploration. Full article

The characterization of the lunar surface and subsurface through the utilization of synthetic aperture radar data has assumed a pivotal role in the domain of lunar exploration science. This investigation concentrated on the polarimetric analysis aimed at identifying water ice within a specific crater, designated Erlanger, located at the lunar north pole, which is fundamentally a region that is perpetually shaded from solar illumination. The area that is perpetually shaded on the moon is defined as that region that is never exposed to sunlight due to the moon’s slightly tilted rotational axis. These permanently shaded regions serve as cold traps for water molecules. To ascertain the presence of water ice within the designated study area, we conducted an analysis of two datasets from the Chandrayaan mission: Mini-SAR data from Chandrayaan-1 and Dual-Frequency Synthetic Aperture Radar (DFSAR) data from Chandrayaan-2. The polarimetric analysis of the Erlanger Crater, located in a permanently shadowed region of the lunar north pole, utilizes data from the Dual-Frequency Synthetic Aperture Radar (DFSAR) and the Mini-SAR. This study focuses exclusively on the L-band DFSAR data due to the unavailability of S-band data for the Erlanger Crater. The crater, identified by the PSR ID NP_869610_0287570, is of particular interest for its potential water ice deposits. The analysis employs three decomposition models—m-delta, m-chi, and m-alpha—derived from the Mini-SAR data, along with the H-A-Alpha model known as an Eigenvector and Eigenvalue model, applied to the DFSAR data. The H-A-Alpha helps in assessing the entropy and anisotropy of the lunar surface. The results reveal a correlation between the hybrid polarimetric models (m-delta, m-chi, and m-alpha) and fully polarimetric parameters (entropy, anisotropy, and alpha), suggesting that volume scattering predominates inside the crater walls, while surface and double bounce scattering are more prevalent in the right side of the crater wall and surrounding areas. Additionally, the analysis of the circular polarization ratio (CPR) from both datasets suggests the presence of water ice within and around the crater, as values greater than 1 were observed. This finding aligns with other studies indicating that the high CPR values are indicative of ice deposits in the lunar polar regions. The polarimetric analysis of the Erlanger Crater contributes to the understanding of lunar polar regions and highlights the potential for future exploration and resource utilization on the Moon. Full article

Lunar in situ resource utilization is a core goal in lunar exploration, with accurate lunar rock pose estimation being essential. To address the challenges posed by the lack of texture features and extreme lighting conditions, this study proposes the Simulation-YOLO-Hourglass-Transformer (SYHT) method. The method enhances accuracy and robustness in complex lunar environments, demonstrating strong adaptability and excellent performance, particularly in conditions of extreme lighting and scarce texture. This approach provides valuable insights for object pose estimation in lunar exploration tasks and lays the foundation for lunar resource development. First, the YOLO-Hourglass-Transformer (YHT) network is used to extract keypoint information from each rock and generate the corresponding 3D pose. Then, a lunar surface imaging physics simulation model is employed to generate simulated lunar rock data for testing the method. The experimental results show that the SYHT method performs exceptionally well on simulated lunar rock data, achieving a mean per-joint position error (MPJPE) of 37.93 mm and a percentage of correct keypoints (PCK) of 99.94%, significantly outperforming existing methods. Finally, transfer learning experiments on real-world datasets validate its strong generalization capability, highlighting its effectiveness for lunar rock pose estimation in both simulated and real lunar environments. Full article

In recent years, the focus of space research and exploration by various countries and international space agencies has been on the return of humans to the moon. Astronauts on lunar missions need to utilize network communication and exchange data. Against this backdrop, it is necessary to consider the performance of communication systems and the extreme conditions of the lunar environment, such as signal attenuation and frequency selection, to ensure the reliability and stability of communication systems. Therefore, providing technical performance adapted to the lunar environment is crucial. In this article, we investigated the applicability of Orthogonal Frequency Division Multiple Access (OFDMA) and Single-Carrier Frequency Division Multiple Access (SC-FDMA) waveforms in the lunar communication environment. Specifically, we used Peak-to-Average Power Ratio (PAPR) and Bit Error Rate (BER) as performance indicators. By studying the impact of different modulation schemes and cyclic prefix lengths on communication performance, we completed the research on waveform adaptability based on lunar channels. Simulation results indicate that the transmission structure we designed can meet the system-level performance requirements of lunar communications. This research provides valuable insights for the design and optimization of communication systems for future lunar missions, paving the way for the seamless integration of advanced ground technologies in extraterrestrial environments. Full article

Planetary photogrammetry remains an important technical means of producing high-precision planetary maps. High-quality control networks are fundamental to successful bundle adjustment. However, current software tools used by the planetary mapping community to construct and edit control networks exhibit very low efficiency. Moreover, redundant and invalid control points in the control network can further increase the time required for the bundle adjustment process. Due to a lack of targeted algorithm optimization, existing software tools and methods are unable to meet the photogrammetric processing requirements of massive planetary remote sensing images. To address these issues, we first proposed an efficient control network construction framework based on approximate orthoimage matching and hash quick search. Next, to effectively reduce the redundant control points in the control network and decrease the computation time required for bundle adjustment, we then proposed a control network-thinning algorithm based on a K-D tree fast search. Finally, we developed an automatic detection method based on ray tracing for identifying invalid control points in the control network. To validate the proposed methods, we conducted photogrammetric processing experiments using both the Lunar Reconnaissance Orbiter (LRO) narrow-angle camera (NAC) images and the Origins Spectral Interpretation Resource Identification Security Regolith Explorer (OSIRIS-REx) PolyCam images; we then compared the results with those derived from the famous open-source planetary photogrammetric software, the United States Geological Survey (USGS) Integrated Software for Imagers and Spectrometers (ISIS) version 8.0.0. The experimental results demonstrate that the proposed methods significantly improve the efficiency and quality of constructing control networks for large-scale planetary images. For thousands of planetary images, we were able to speed up the generation and editing of the control network by more than two orders of magnitude. Full article

Current space exploration focuses on returning to the Moon to expand space exploration capacity by improving technology. The long-term presence of humans and robots on the Moon requires the development of durable habitats for space missions. In recent decades, in situ resource utilization (ISRU) for construction materials has been recognized as a viable option. However, the addition of nanomaterials, which exhibit a high strength-to-weight ratio, has not been incorporated with the ISRU framework in space missions. This paper investigates the impact of carbon nanotubes (CNTs) on lunar simulant-based geopolymers’ compressive strength and water retention. The evaluation of water retention indicates another potential in water recapturing capability. In this study, CNTs can enhance the mechanical properties of lunar simulant-based geopolymer. Two lunar simulants were used, representing the Highland and Mare regions of the Moon. Experimental variables included CNT concentration, four curing regimes (ambient curing, two oven-curing methods, and microwave radiation), and dispersion time in aqueous solutions. Results showed that CNTs can positively influence both strength gain and water retention during curing regimes, but the extent of influence appears to be dependent on simulant type and curing regime. The Highland simulant consistently outperformed the Mare simulant in oven-curing regimes from a strength perspective, regardless of CNT presence. The strength benefits of CNTs were more pronounced at ambient curing temperatures. Even under poor curing conditions—where water availability may be limited at temperatures of 80 °C—CNTs aid in retaining water within the geopolymer matrix, leading to improved strength compared to counterparts. Under the same conditions, a higher concentration of CNTs further confirmed their role in water retention during geopolymerization, with consistently greater water retention observed in samples containing CNTs. Additionally, microwave radiation was explored as an alternative to conventional oven drying, showing potential for reducing curing duration. Finally, the findings suggest that combining CNTs and microwave radiation could enhance water recovery and reuse, contributing to the development of high-strength infrastructure materials on the Moon with reduced energy and cost requirements. Full article