Lunar Landing Research Articles

MEDEA: A New Model for Emulating Radio Antenna Beam Patterns for 21 cm Cosmology and Antenna Design Studies

In 21 cm experimental cosmology, accurate characterization of a radio telescope’s antenna beam response is essential to measure the 21 cm signal. Computational electromagnetic (CEM) simulations estimate the antenna beam pattern and frequency response by subjecting the EM model to different dependencies, or beam hyperparameters, such as soil dielectric constant or orientation with the environment. However, it is computationally expensive to search all possible parameter spaces to optimize the antenna design or accurately represent the beam to the level required for use as a systematic model in 21 cm cosmology. We therefore present the Model for Emulating Directivities and Electric fields of Antennas (MEDEA), an emulator that rapidly and accurately generates far-field radiation patterns over a large hyperparameter space. MEDEA takes a subset of beams simulated by CEM software, spatially decomposes them into coefficients on a complete, linear basis, and then interpolates them to form new beams at arbitrary hyperparameters. We test MEDEA on an analytical dipole and two numerical beams motivated by upcoming lunar lander missions, and then employ MEDEA as a model to fit mock radio spectrometer data to extract covariances on the input beam hyperparameters. We find that the interpolated beams have rms relative errors of at most 10−2 using 20 input beams or less, and that fits to mock data are able to recover the input beam hyperparameters when the model and mock are derived from the same set of beams. When a systematic bias is introduced into the mock data, extracted beam hyperparameters exhibit bias, as expected. We propose several extensions to MEDEA to potentially account for such bias.

A New Robust Lunar Landing Selection Method Using the Bayesian Optimization of Extreme Gradient Boosting Model (BO-XGBoost)

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.

Onboard Burn Targeting and Guidance of NASA Human Flight Vehicles

The mission requirements of crewed space vehicles flown by NASA drove the development of new techniques for burn targeting and guidance for ascent, rendezvous, lunar landing, and deorbit. The Saturn V Iterative Guidance Mode used an approximately optimal steering law that could adjust the trajectory to compensate for performance issues while meeting trajectory constraints at propulsion-system cutoff. Hypersurface targeting provided constraints for the Saturn V Trans-Lunar Injection burn. E Guidance, in quadratic- and linear-acceleration forms, provided closed-loop descent and ascent guidance for the Apollo Lunar Module. The Space Shuttle’s Powered Explicit Guidance algorithm was more flexible than Apollo-era guidance algorithms, and it had better convergence characteristics, enabling it to support challenging abort profiles. The Space Launch System has built on the heritage of the Space Shuttle’s guidance algorithm. The Orion spacecraft’s Two-Level Targeter enables onboard, autonomous targeting of an entire mission profile subject to three-body dynamics in cislunar space. Orion uses Shuttle-derived guidance as the basis for an orbit-guidance package with more capability than guidance algorithms of previous vehicles.

The Peregrine Ion Trap Mass Spectrometer (PITMS) Investigation Development and Preflight Planning

The Peregrine Ion Trap Mass Spectrometer (PITMS) is a mass spectrometer instrument that operated during the Astrobotic Peregrine Mission-1 as part of the NASA Commercial Lunar Payload Services initiative. This paper describes the instrument and investigation design, development, and planning conducted by the PITMS team, consisting of a successful partnership between NASA Goddard Space Flight Center (GSFC), The Open University, NASA, and ESA. PITMS was designed to measure the abundance and temporal variability of volatile species in the near-surface lunar exosphere from a landed platform on the lunar surface. The PITMS instrument consisted of a European Space Agency–provided Exospheric Mass Spectrometer (including sensor, electronics, controller, and power supply boards) and a GSFC wrapper that provided structural elements, thermal control, and a deployable dust cover. PITMS was designed to operate as a passive sampler, where ambient gases would enter PITMS through an aperture, diffuse around the mass analyzer cavity, become ionized by electron impact and trapped in an RF field, and then sequentially be released to a detector to build a mass spectrum. PITMS was capable of measuring species with a mass-to-charge ratio (m/z) from 10 to 150 Da, with a mass resolution of approximately 0.5 amu. The PITMS science investigation was planned to be operated by GSFC with an international team of scientists. Though the mission did not achieve its lunar landing, information about the PITMS instrument and planning is provided to be able to understand and effectively use data that will be forthcoming from the investigation.

Organ dose equivalents of albedo protons and neutrons under exposure to large solar particle events during lunar human landing missions

Astronauts participating in lunar landing missions will encounter exposure to albedo particles emitted from the lunar surface as well as primary high-energy particles in the spectra of galactic cosmic rays (GCRs) and solar particle events (SPEs). While existing studies have examined particle energy spectra and absorbed doses in limited radiation exposure scenarios on and near the Moon, comprehensive research encompassing various shielding amounts and large SPEs on the lunar surface remains lacking. Additionally, detailed organ dose equivalents of albedo particles in a human model on the lunar surface have yet to be investigated. This work assesses the organ dose equivalents of albedo neutrons and albedo protons during historically large SPEs in August 1972 and September 1989 utilizing realistic computational anthropomorphic human phantom for the first time. Dosimetric quantities within human organs have been evaluated based on the PHITS Monte Carlo simulation results and quality factors of the state-of-the-art NASA Space Cancer Risk (NSCR) model, as well as ICRP publications. The results with the NSCR model indicate that the albedo contribution to organ dose equivalent is less than 3 % for 1 g/cm2 aluminum shielding, while it increases to more than 30 % in some organs for 50 g/cm2 aluminum shielding during exposure to low-energy-proton-rich SPEs.

Real-time vacuum plume flow field reconstruction during lunar landings based on deep learning

In space missions, the vacuum plume generated by rocket engines can negatively impact spacecraft. Therefore, researching the vacuum plume is crucial to guarantee the regular operation of spacecraft. The conventional numerical simulation methodology, the direct simulation Monte Carlo (DSMC) method, is time-consuming and lacks real-time calculation capabilities. Recently, deep learning (DL) methods have emerged in the field of fluid dynamics. In this study, a DL model trained by a convolutional neural network with multiple decoders is introduced to predict the vacuum plume flow field during lunar landings. The network processes shape topology information and boundary conditions as inputs, yielding flow field data including velocity and pressure fields as outputs. Meanwhile, the flow field prediction results under different conditions and training methods are discussed. The results show that the predicted flow field under different lunar surface conditions is in accord with the DSMC results. The maximum mean and standard deviation errors of the data distribution of each flow field do not exceed 9.72% and 9.07%, respectively. Different training methods with flat and inclined lunar surfaces also have an impact on the prediction results. Compared with the DSMC method, the DL method exhibits higher efficiency with a speedup of about four orders of magnitude, indicating that the DL-based flow field reconstruction method has strong application prospects in the real-time computation of vacuum plume flow fields.

Numerical simulation of plume–surface interaction and lunar dust dispersion during lunar landing using four engines

As the lander approaches the lunar surface, the engine plumes impinge on the lunar regolith and entrain lunar dust from the surface. This plume–surface interaction and the resulting dispersion of lunar dust form a multi-physics, multi-scale problem, which becomes even more complex under multi-engine conditions. This study employed the direct simulation Monte Carlo method to simulate the plume–surface interaction flow field of a four-engine lunar lander at various landing altitudes and lunar surface angles. Flow characteristics were analyzed, and the impact of the plume and backflow on the lander was assessed. Subsequently, lunar dust simulation was conducted using the plume field as a basis. The study determined the spatial distribution of particles with different diameters at various landing altitudes and surface angles, as well as their impact velocities on the lander. Furthermore, taking into account the variations in the lander's altitude and attitude, a dynamic simulation of lunar dust during the landing process was conducted. This process resulted in the dynamic distribution of lunar dust during landing, laying the groundwork for real-time simulation of lunar dust distribution and reliable visualization during landing simulations. These findings are valuable for assessing and mitigating the hazards posed by lunar dust.

Fuel-optimal powered descent guidance for lunar pinpoint landing using neural networks

This paper presents a Neural Networks (NNs) based approach to generate the fuel-optimal powered descent guidance for lunar pinpoint landing. According to Pontryagin’s Minimum Principle, the optimality conditions are first derived. To generate the dataset of optimal trajectories for training NNs, we formulate a parameterized system, which allows for generating each optimal trajectory by a simple propagation without using any optimization method. Then, a dataset containing the optimal state-thrust vector pairs can be readily collected. Since it is challenging for NNs to approximate a bang-bang (or discontinuous) type of optimal thrust magnitude, we introduce a regularisation function to the switching function so that the regularized switching function approximated by a simple NN can be used to represent the optimal thrust magnitude. Meanwhile, another two well-trained NNs are used to predict the thrust steering angle and the time of flight given a flight state. Finally, numerical simulations show that the proposed method is capable of generating the optimal guidance that steers the lunar lander to the desired landing site with acceptable landing errors.

Life Detection on Icy Moons Using Flow Cytometry and Intrinsically Fluorescent Biomolecules.

In a previous experiment, we demonstrated the capability of flow cytometry as a potential life detection technology for icy moons using exogenous fluorescent stains (Wallace et al., 2023). In this companion experiment, we demonstrated the capability of flow cytometry to detect life using intrinsically fluorescent biomolecules in addition to exogenous stains. We used a method similar to our previous work to positively identify six classes of intrinsically fluorescent biomolecules: flavins, carotenoids, chlorophyll, tryptophan, NAD+, and NAD(P)H. We demonstrated the effectiveness of this method with six known organisms and known abiotic material and showed that the cytometer is easily able to distinguish the known organisms and the known abiotic material by using the intrinsic fluorescence of these six biomolecules. To simulate a life detection experiment on an icy moon lander, we used six natural samples with unknown biotic and abiotic content. We showed that flow cytometry can identify all six intrinsically fluorescent biomolecules and can separate the biotic material from the known abiotic material on scatter plots. The use of intrinsically fluorescent biomolecules in addition to exogenous stains will potentially cast a wider net for life detection on icy moons using flow cytometry.

Development of convolutional neural network-based surrogate model for three-dimensional vacuum plume prediction via direct simulation Monte Carlo method

The vacuum plume phenomenon encountered during lunar exploration missions poses significant challenges, such as impingement forces, heat fluxes, and spacecraft contamination. Numerical simulation represents the predominant method for evaluating the impacts of vacuum plumes. However, the conventional direct simulation Monte Carlo (DSMC) method, despite being the standard, is notably time-consuming and impractical for real-time analysis. Addressing this limitation, our research explores deep learning, specifically convolutional neural networks (CNN), for the efficient prediction of vacuum plume dynamics. We introduce a novel CNN-based DSMC method (CNN-DSMC-3D), leveraging a dataset obtained from three-dimensional DSMC simulations. This approach translates the spacecraft's shape and boundary conditions into a signed distance function and an identifier matrix. The CNN-DSMC-3D method effectively predicts the vacuum plume field, aligning closely with DSMC results across various lunar surface conditions. Crucially, the CNN-DSMC-3D method achieves a speed increase in four to six orders of magnitude over the conventional DSMC method, demonstrating substantial potential for real-time aerospace applications and offering a paradigm shift in the simulation of lunar landing scenarios.

Passive thermal control design and flight operation results of nano moon lander OMOTENASHI

OMOTENASHI is a 6U CubeSat launched by the first NASA SLS rocket. Its mission was to demonstrate that a CubeSat can make a semi-hard landing on the Moon. Tight resource constraints make it difficult to mount a heater and a radiator in a CubeSat, so passive thermal control is a prominent design feature of OMOTENASHI that resolves the technical difficulty. In November 2019, a thermal vacuum test was conducted. This paper discusses the thermal design and the test results. OMOTENASHI was launched by SLS rocket on November 16, 2022. After the SLS rocket separation, OMOTENASHI's attitude control malfunctioned so it could not generate power from its solar cells. Eventually, communications were lost and have not been restored. Brief telemetry data confirmed that the temperature monitor functioned correctly for 30 min. This paper reports the flight results of the thermal status during the first ground station pass. Also, a comparison between the analysis model and telemetry data is shown. Finally, based on its estimated trajectory, the future thermal profile of the spacecraft and the result of the search operation are discussed.

Trajectory/propulsion integrated design optimization of the manned lunar lander propelled by hybrid rocket motors using analytical target cascading

Lunar missions are currently experiencing a significant surge in popularity, presenting expansive opportunities for further exploration and development. To thoroughly explore the design margins and potential of lunar landers, and to foster the development of overall designs driven by comprehensive performance objectives, it is crucial to conduct optimization design considering the coupling between key disciplines such as trajectory and propulsion. Considering the significant increase in computational complexity caused by conducting trajectory/propulsion integrated design optimization, the analytical target cascading method is employed to hierarchically decompose and coordinate optimization of the complex systems. This article presents a phased soft-landing strategy on the manned lunar lander propelled by hybrid rocket motors, utilizing powered explicit guidance and Apollo powered descent guidance, and proceeds with the trajectory/propulsion integrated design optimization involving diverse grain shapes and feed systems. This optimization process is separately undertaken utilizing multidisciplinary feasible method and analytical target cascading method. The analysis reveals that integrating trajectory and propulsion considerations into the optimization process facilitates a 5 % reduction in the overall mass relative to optimizations constrained solely by velocity increment and lack comprehensive trajectory design considerations. This highlights the profound impact of trajectory requirements on propulsion system design and the advantages of powered explicit guidance laws in minimizing fuel consumption. Crucially, the use of analytical target cascading achieves the better optimization results, and significantly reduces subsystem evaluation times, enhancing operational efficiency by 48 %, demonstrating the advantage in handling complex, large-scale systems. On another level, with different β values, the Mean Relative Error of the target values for the three schemes obtained by the analytical target cascading method is 0.0016, indicating good stability and strong robustness. The practical exploration in this article provides methods and frameworks for high-performance optimization design of complex aerospace mission profiles in the future.

Accurate Mapping and Evaluation of Small Impact Craters within the Lunar Landing Area

Impact craters, as the most distinct lunar structural unit and geological structure, are marked on the Moon’s surface. For over a decade, researchers have focused on identifying and exploring large- to medium-sized impact craters on the surface of the Moon (craters with a diameter greater than 1 km). Small impact craters have obvious statistical significance owing to their magnitude in numbers. The identification and analysis of small craters provide indispensable clues for the study of lunar geological evolution. However, such craters only remain in specific images and regions. At present, there is no comprehensive record of small impact craters in the existing lunar impact crater databases. The small impact craters on the surface of the Moon are enormous and vary in size by orders of magnitude, exhibiting small target characteristics in space. The present study focuses on the identification and spatial analysis of small impact craters on the surface of the Moon. A feature amplification strategy-based identification model was established for small impact crater detection, achieving accurate recognition of the small impact craters on the surface of the Moon (with a recall rate of 86.97% and a false-positive rate as low as 0.54% ± 0.16%). In total, 228,897, 142,872, and 42,008 new small lunar impact craters (with diameters as low as 4.5 m) were identified in the ten lunar landing areas of returned samples from the Apollo, Luna, and Chang’e-5 missions. In addition, the spatial distribution characteristics of small impact craters during different geological periods in the landing area are provided. Data on the newly identified small impact craters will provide an important basis for revealing the lunar impact fluxes and young lunar surface dating in lunar geological evolution research.

Tripping on the Moon

Two recent lunar lander missions have toppled over on touchdown. We analyse a simplified model to determine the lateral speed of an object that results in tipping, as a practical example of an inelastic collision that conserves angular momentum. The solution employs rotational dynamics and conservation laws covered in an introductory mechanics course. We suggest further investigations, including simple experiments that can be performed in the teaching laboratory.

Performance evaluation and improvement of deep Q network for lunar landing task

Reinforcement learning is now being applied more and more in a variety of scenarios, the majority of which are based on the deep Q network (DQN) technology. However, the algorithm is heavily influenced by multiple factors. In this paper, we take the lunar lander as a case to study how various hyper-parameters affect the performance of the DQN algorithm, based on which we tune to get a model with better performance. At present, it is known that the DQN model has an average reward of 280+ on 100 test episodes, and the reward value of the model in this article can reach 290+. Meanwhile, its robustness is tested and verified by introducing additional uncertainty tests into the original problem. In addition, to speed up the training process, imitation learning is incorporated in our model, using heuristic function model guidance method to obtain demonstration data, which accelerates training speed and improves performance. Simulation results have proven the effectiveness of this method.

Positioning of a lunar surface rover on the south pole using LCNS and DEMs

With the renewed interest in the lunar surface exploration, the European Space Agency is supporting, as part of the Moonlight program, the development of a dedicated Lunar Communications and Navigation Service (LCNS) infrastructure. This should enable to achieve real time positioning and autonomous navigation in the cislunar space, including, among others, lunar landing and surface operations. One of the services proposed, as part of the LunaNet framework (NASA, 2022), is called the Lunar Augmented Navigation Service (LANS) and it is based on a GNSS-like concept. However, compared to Earth-based GNSS, the expected number of available satellites in lunar orbit will initially be limited, which triggers the interest of coupling these technologies at the receiver level with other navigation data sources. In the case of surface rovers, use of digital elevation model (DEM) data has shown to be a potential other source of information to support positioning. The purpose of this contribution is to look at the actual navigation performances achievable when using a simple (four satellites constellation) lunar radio navigation constellation combined with a DEM, through a state estimation filter, for different rover dynamic and different DEM accuracy levels. It is shown that a 5 meter real-time horizontal position accuracy can be achieved, for 99.7% of the time, using a precise enough DEM (5 m/pixel) and a 4 satellite-constellation under different rover’s dynamic conditions (i.e. velocities).

The Role of Literature in Anticipating Future Trends

his paper explores the role of literature in shaping some tremendous inventions that came later. It shows the prophecy of some authors. The study analyzes literary works to demonstrate the importance of literature in forecasting future sociological, technical, and cultural changes via themes, motifs, and story structures. A multidisciplinary approach is used, in this study, to investigate how literature reflects and shapes the collective imagination, fostering analytical thinking and generating discussions about possible future situations. It displays scientific innovations that were predicted by writers in their literary works long before they were really developed. Key innovations in contemporary life include the spaceship, Moon landing, and submarine as depicted in Jules Verne's books, “From the Earth to the Moon” and “Twenty Thousand Leagues Under the Sea”. This study tries to show the role of science fiction literature in predicting future routes and how it acts as a prophetic medium, offering insights into the potential paths of humanity.

LiDAR-Inertial-Based Absolute Positioning With Sparse DEM for Accurate Lunar Landing

LiDAR-Inertial-Based Absolute Positioning With Sparse DEM for Accurate Lunar Landing

Erosion rate of lunar soil under a landing rocket, part 2: Benchmarking and predictions

In the companion paper (“Erosion rate of lunar soil under a landing rocket, part 1: identifying the rate-limiting physics”, this issue) an equation was developed for the rate that lunar soil erodes under the exhaust of a landing rocket. That equation has only one parameter that is not calibrated from first principles, so here it is calibrated by the blowing soil's optical density curve during an Apollo landing. An excellent fit is obtained, helping validate the equation. However, when extrapolating the erosion rate all the way to touchdown on the lunar surface, a soil model is needed to handle the increased resistance to erosion as the deeper, more compacted soil is exposed. Relying on models derived from Apollo measurements and from Lunar Reconnaissance Orbiter (LRO) Diviner thermal inertia measurements, only one additional soil parameter is unknown: the scale of increasing cohesive energy with soil compaction. Treating this as an additional fitting parameter results in some degeneracy in the solutions, but the depth of erosion scour in the post-landing imagery provides an additional constraint on the solution. The results show that about 4 to 10 times more soil was blown in each Apollo landing than previously believed, so the potential for sandblasting damage is worse than prior estimates. This also shows that, with further development, instruments to measure the soil erosion during lunar landings can constrain the soil column's density profile complementary to the thermal inertia measurements, providing insight into the landing site's geology.

The Deep Space Radiation Probe: Development of a first lunar science payload for space environment studies and capacity building

Regions outside of Low Earth Orbit (LEO, altitudes above approximately 1000 km) are classified as “deep space”, including Medium Earth Orbit (MEO), geostationary orbit (GEO), as well as cislunar and lunar space. The deep space environment poses many challenges for human and robotic exploration, including stronger ionizing radiation fluxes, more extreme temperature variations, as well as limited data downlink volume. With the growth of the rideshare and hosted payload model aboard government and commercial lunar missions, developing the capacity to design and implement payloads and other space avionics for this environment is of increased importance this decade. Utilizing one of the growing number of rideshare opportunities offered by commercial lunar mission providers, National Central University (NCU) has been working on the rapid development of Taiwan’s first scientific payload for lunar lander use, with launch aboard the HAKUTO-R Mission 2 (M2) lander from ispace, inc. scheduled not earlier than Q4 2024. This Deep Space Radiation Probe (DSRP) will provide measurements of radiation dose, dose rate, and single event upset (SEU) rate during the Earth-Moon transit, in lunar orbit, as well as on the lunar surface. DSRP utilizes elements of the on-board computer (OBC) developed and flight qualified aboard the NCU-developed IDEASSat 3U CubeSat mission in 2021, and was developed by a student team, in consultation with experienced engineers from the lunar lander team. In this paper, we will report on the objectives, concept of operation, design, and implementation of the DSRP project. We will also describe the steps taken to facilitate parallel development of the DSRP payload and the HAKUTO-R M2 lander, as well as lessons learned during the design, implementation, and qualification process. The radiation data provided by DSRP will be beneficial for the development of future deep space spacecraft avionics, as well as crewed missions, and will also serve to build the capacity for deep space spacecraft and payload development at NCU.