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NASA NTRSDate not supplied

NASA’s Human Landing System: A Sustaining Presence on the Moon

For more than a decade, efforts have been ongoing at NASA’s Marshall Space Flight Center (MSFC) in Huntsville, Alabama, to land humans and cargo on planetary bodies like the Moon and Mars and today this work continues under the Center’s Lander Programs (LP) office. In August of 2019, NASA stood up the Human Landing System (HLS) program to be responsible for spacecrafts that will land astronauts on the Moon under Artemis. Work is well underway with the historic Artemis III mission to land the first Americans on the lunar surface in more than 50 years through the Appendix H Option A [1] contract with SpaceX. In November, NASA awarded SpaceX an Option B modification to its existing HLS Appendix H contract, which will further develop its Starship HLS to meet NASA’s sustaining lander requirements for lunar missions beyond Artemis III. In September of 2022, NASA issued an HLS Sustaining Lunar Development solicitation under the NextSTEP-2 Appendix P Broad Agency Announcement [2] asking a second provider, in addition to SpaceX, to develop and demonstrate a lander that meets the program’s extended set of requirements for missions beyond Artemis III. Under the umbrella of Sustaining Lunar Development [3], these requirements will meet NASA’s needs for recurring, long-term access to the lunar surface. Proposals were received from industry late last year and NASA is planning to award an Appendix P contract in the summer of 2023. This paper will provide an update of the Lander Program office’s progress and will discuss how the program is bridging from the initial demonstration phase of development for the Human Landing System program to the Sustaining Lunar Development phase. The paper will include publicly available information on SpaceX’s Starship HLS design as well as near-term and future milestones for HLS and the Artemis program.

Lisa Watson-Morgan↗

DOE OSTI2026

Intelligent Experiments through Real-Time AI: Fast Data Processing and Autonomous Detector Control for High-Energy Nuclear Experiments

The aim of this project is to develop software and hardware for fast real-time data processing and autonomous detector control and calibration for the sPHENIX and the future EIC experiments. Below summarizes Georgia Tech team efforts in the past year: 1. We developed a real-time clustering algorithm and FPGA-based pipeline architecture for processing fired pixel data from ALPIDE sensors in sPHENIX experiments. Our Columnar Clustering Co-Design introduces a hardware-aware, stream-friendly approach that segments pixel data by column pairs using a Column Pair Clustering (CPC) strategy, followed by Cluster Stitching to merge adjacent subclusters. Implemented in Vitis HLS, the pipeline comprises five stages—read-in, subclustering, stitching, analysis, and write-out—connected by tagged HLS streams with custom end-of-event signaling for robust synchronization. We designed a pipelined dataflow model optimized for throughput, low latency, and minimal buffering, enabling scalable clustering across events of arbitrary size. Our system maintains spatial precision via center-of-mass and shape key extraction and efficiently handles edge cases such as fragmented or nested clusters. Compared against DBSCAN in both software and hardware, our approach demonstrates competitive performance under FPGA constraints. 2. We also conducted a comprehensive algorithm-to-hardware co-design of connected component analysis tailored for sPHENIX experiments, focusing on real-time, low-latency processing using FPGAs and High-Level Synthesis (HLS). Starting from a Python-based particle tracking pipeline, the team translated the core logic—graph traversal via DFS and Union-Find—into an HLS-compatible C++ model, replacing dynamic memory and recursion with static arrays and pipelined control flow. The final design includes a fully streamed and dataflow-compatible Union-Find kernel optimized across five iterations, incorporating loop pipelining, array partitioning, AXI/FIFO interface tuning, and function flattening. Experimental results show up to 14.8× speedup over the CPU baseline, reducing per-graph latency to 1.58 μs and demonstrating strong resource efficiency with only ~7k LUTs and zero BRAM usage. The design maintains functional correctness against the Python reference using a Python-based C-simulation framework and Mean Squared Error metrics. This work validates the potential of HLS-driven FPGA designs for edge-level HEP data acquisition, laying a scalable foundation for future integration with real-time detector pipelines and multi-graph processing systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can be integrated into full designs for field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). With its flexible and modular design, hls4ml supports a large number of deep learning frameworks and can target HLS compilers from several vendors, including Vitis HLS, Intel oneAPI and Catapult HLS. Together with a wider eco-system for software-hardware co-design, hls4ml has enabled the acceleration of ML inference in a wide range of commercial and scientific applications where low latency, resource usage, and power consumption are critical. In this paper, we describe the structure and functionality of the hls4ml platform. The overarching design considerations for the generated HLS code are discussed, together with selected performance results.

FOS: Computer and information sciences↗

NASA NTRS1994

Interaction of Mechanical Load with Growth Hormone (GH) and Insulin-Like Growth Factor I (IGF-I) on Slow-Twitch Skeletal Muscle and Bone

Exogenous humoral growth factors, combined with increased mechanical loading, reportedly induce hypertrophy of fast-, but not slow-twitch skeletal muscles, and have little effect in attenuating atrophy of slow-twitch muscle associated with exposure to microgravity in animals with intact neuroendocrine systems. These observations suggest that anabolic adjuvants and muscle tension do not interact to stimulate growth or maintenance of slow-twitch skeletal muscle. The purpose of the present study was to determine whether a chronic increase in mechanical loading (synergistic ablation) or hindlimb unweighting (hindlimb suspension) interact with exogenous GH and IGF-I (Genentech, So San Francisco, CA) in the slow-twitch soleus muscles of female rats (approx. 250 g). Bilateral ablation of the plantaris and gastrocnemius muscles induced 38% and 40% increases in the absolute (mg/pair) and relative (mg/100 g body weight) weights of the soleus, respectively (p less than or = 0.05), in ambulatory rats. GH and IGF-I interacted with chronic loading to increase absolute soleus mass an additional 20% (p less than or = 0.05), and mixed and myofibrillar protein contents an additional 12% and 7%, respectively (NS). In contrast, hindlimb suspension (HLS) resulted in 20% and 18% decreases in the absolute and relative weights of the soleus, respectively (p less than or = 0.05); GH and IGF-I did not spare loss of soleus mass or protein content in HLS rats. HLS decreased tibial plate thickness approx. 11% (p less than or = 0.05), but not weights of the tibia or femus. GH and IGF-I increased tibial plate thickness approx. 30% (p less than or = 0.05), in ambulatory and HLS rats, and increased femur and tibial weights 12% (p less than or = 0.05) and 8% (NS), respectively, in ambulatory rats, but had no effect in HLS rats. Results of the present investigation suggest that GH and IGF-I can stimulate hypertrophy of slow-twitch skeletal muscle when chronically overloaded, but can also stimulate growth of hindlimb bones in the absence of mechanical load.

Linderman, Jon K.↗

NASA NTRS2004

The Effect of Feeding Purified versus Chow Diet on Bone Changes Produced by Hindlimb Suspension of Female Rats

Spaceflight simulation studies use chow diets while spaceflight studies use a semi-purified &et. To determine whether the differences in these diets would affect the changes in unweighted bone, we compared the effects of purified vs chow diet on bone parameters, urinary calcium, plasma estradiol, and urinary corticosterone (CORT) in sexually mature female Sprague-Dawley rats. Rats fed purified AIN-93G or chow diet were kept ambulatory (AMB) or subjected to a spaceflight simulation model of unweighted hindlimbs (HLS) for 38 days. Body mass of treatment groups was similar although food intake and caloric density of the diets differed. Both HLS diet groups showed similar decreases in bone mineral content and mechanical strength in unweighted femurs compared to AMB (p<0.05). However, femur length was lower (p<0.05) in the chow-fed than AIN-93G fed groups. Urinary calcium excretion was greater in chow than AIN-93G fed rats, consistent with the higher level of calcium in the diet. Plasma estradiol was lower in HLS than in AMB fed AIN-93G, but similar in HLS and AMB chow fed groups. Femur mineral content was related to plasma estradiol (r(sup 2) =0.91, p<0.00l). Urinary CORT excretion was increased during initial HLS and elevated in HLS/chow-fed rats. Diets did not appear to affect the osteopenia induced by unweighting, but effects on bone growth, calcium excretion, plasma estradiol and urinary CORT do not support the view that these diets can by used interchangeably in bone studies.

Tou, Janet↗

NASA NTRSDate not supplied

Probabilistic Modeling of a Three-Stage Human Landing System Architecture

Unmitigated uncertainties are known to have previously led to failed development programs; in order to combat these uncertainties, risks and their impacts must be understood and handled to ensure program success. In this paper, a probabilistic methodology to handle uncertainties is demonstrated on a three-element Human Landing System (HLS) concept, which allows tracking of current best estimates of the vehicle’s performance and assessment of its robustness against uncertainties. This methodology has two key parts: ﬁrst, the creation of a dynamic architecture model of a three-element HLS concept; and second, its use with surrogate modeling and range estimating techniques to capture and propagate uncertainties. The DYnamic Rocket EQuation Tool (DYREQT), a space systems synthesis and sizing framework used by NASA, was used as to model the HLS architecture. For the probabilistic analysis, uncertainties of interest within the HLS concept were enumerated and represented as parameters within the DYREQT model as inputs for vehicle stages or mission proﬁle events. Range estimating — a probabilistic method that combines Monte Carlo sampling, focus on critical parameters, and heuristics to assess risk and opportunities — is then adapted with operational parameters as well as vehicle parameters in the DYREQT model to capture mission uncertainty alongside vehicle uncertainty. To perform the range estimation portion of this methodology, the DYREQT model was sampled using a Design of Experiments (DoE) to eﬃciently explore the architecture design space with respect to the set of uncertainty parameters. Then, the results were used to create surrogate models, multivariate regressions that can visualize hypercube trends in the design space, of the architecture with respect to the uncertainty parameters. Using a correlation matrix constructed for the uncertainty parameters, previously independent samples were transformed to perform a Correlated Monte Carlo on the surrogate models. This probabilistic methodology was proved to provide insight into the underlying uncertainties of the three-element HLS architecture.

Stephanie Y Zhu↗

NASA NTRSDate not supplied

Probabilistic Modeling of a Three-Stage Human Landing System Architecture

Stephanie Y. Zhu↗

NASA NTRSDate not supplied

NASA’s Human Landing System: Enabling the Next Generation of Lunar Science

The Human Landing System (HLS) is the mode of transportation that will take astronauts to the lunar surface as part of NASA's Artemis exploration program. HLS also serves as a research platform both on the surface and in lunar orbit, enabling critical scientific investigations on and of the Moon. With support at NASA centers around the country, the HLS program, based at Marshall Space Flight Center in Huntsville, Alabama, is working closely with its commercial partners throughout the development process to design and build innovative and technically advanced lunar landers – new vehicles designed for the modern era of space travel – leveraging decades of human spaceflight experience and the speed of the commercial sector. In 2019, NASA asked U.S. industry for proposals to design and develop a human lander for the first human mission to the lunar surface under Artemis. Initial contracts were awarded to Blue Origin Federation, Dynetics, and SpaceX to advance their designs. Following the execution of the ten-month Base Period, NASA announced in April 2021 that the agency selected SpaceX to move forward with its human landing system [1] and land the first two astronauts on the lunar surface during the Artemis III mission. Following two protest periods, NASA awarded the contract, known as Option A, to SpaceX in July 2021 and resumed work in November 2021 (Fig. 1). In parallel, the HLS program has been preparing for the acquisition that will procure regular crewed transportation to the lunar surface following Artemis III. NASA released a request for information (RFI) in July 2021 asking U.S. industry for feedback to help inform the future solicitation, known as Lunar Exploration Transportation Services (LETS) [2]. Also in July 2021, NASA released the NextSTEP-2 Appendix N broad agency announcement soliciting new work from U.S. industry to mature their HLS designs and perform risk reduction activities in advance of the LETS procurement [3]. NASA selected Blue Origin Federation, Dynetics, Lockheed Martin, Northrop Grumman, and SpaceX to participate [4]. This work will also help better inform the LETS procurement and prepare industry to propose. NASA hopes to release a draft request for proposals (RFP) in Spring 2022.

R C Weber↗

NASA NTRS2021

Cosmology with the Roman Space Telescope - Multiprobe Strategies

We simulate the scientific performance of the Nancy Grace Roman Space Telescope High Latitude Survey (HLS) on dark energy and modified gravity. The 1.6-yr HLS Reference survey is currently envisioned to image 2000 deg2 in multiple bands to a depthof∼26.5 in Y, J, H and to cover the same area with slit-less spectroscopy beyond z=3. The combination of deep, multiband photometry and deep spectroscopy will allow scientists to measure the growth and geometry of the Universe through a variety of cosmological probes (e.g. weak lensing, galaxy clusters, galaxy clustering, BAO, Type Ia supernova) and, equally, it will allow an exquisite control of observational and astrophysical systematic effects. In this paper, we explore multiprobe strategies that can be implemented, given the telescope’s instrument capabilities. We model cosmological probes individually and jointly and account for correlated systematics and statistical uncertainties due to the higher order moments of the density field. We explore different levels of observational systematics for the HLS survey (photo-z and shear calibration) and ultimately run a joint likelihood analysis in N-dim parameter space. We find that the HLS reference survey alone can achieve a standard dark energy FoM of>300 when including all probes. This assumes no information from external data sets, we assume a flat universe however, and includes realistic assumptions for systematics. Our study of the HLS reference survey should be seen as part of a future community-driven effort to simulate and optimize the science return of the Roman Space Telescope.

Cosmological parameters↗

NASA NTRS2021

Augmenting Landsat time series with Harmonized Landsat Sentinel-2 data products: Assessment of spectral correspondence

An increase in the temporal revisit of satellite data is often sought to increase the likelihood of obtaining cloud- and shadow-free observations as well as to improve mapping of rapidly- or seasonally-changing features. Currently, as a tandem, Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and −8 Operational Land Imager (OLI) provide an acquisition opportunity on an 8-day revisit interval. Sentinel-2A and -2B MultiSpectral Instrument (MSI), with a wider swath, have a 5-day revisit interval at the equator. Due to robust pre- and post-launch cross-calibration, it has been possible for NASA to produce the Harmonized Landsat Sentinel-2 (HLS) data product from Landsat-8 OLI and Sentinel-2 MSI: L30 and S30, respectively. Knowledge of the agreement of HLS outputs (especially S30) with historic Landsat surface reflectance products will inform the ability to integrate historic time-series information with new and more frequent measures as delivered by HLS. In this research, we control for acquisition date and data source to cross-compare the HLS data (L30, S30) with established Landsat-8 OLI surface-reflectance measures as delivered by the USGS (hereafter BAP, Best Available Pixel). S30 and L30 were found to have high agreement (R = 0.87–0.96) for spectral channels and an r = 0.99 for Normalized Burn Ratio (NBR) with low relative root-mean-square difference values (1.7%–3.3%). Agreement between L30 and BAP was lower, with R values ranging from 0.85 to 0.92 for spectral channels and R = 0.94 for NBR. S30 and BAP had the lowest agreement, with R values ranging from 0.71 to 0.85 for spectral channels and r = 0.90 for NBR. Comparisons indicated a stronger agreement at latitudes above 55° N. Some dependency between spectral agreement and land cover was found, with stronger correspondence for non-vegetated cover types. The level of agreement between S30 and BAP reported herein would enable integration of HLS outputs with historic Landsat data. The resulting increased temporal frequency of data allows for improvements to current cloud screening practices and increases data density and the likelihood of temporal proximity to target date for pixel compositing approaches. Furthermore, additional within-year observations will enable change products with a higher temporal fidelity and allow for the incorporation of phenological trends into land cover classification algorithms.

Michael A. Wulder↗

DOE OSTI2025

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

Schulte, Jan-Frederik [Purdue U.] (ORCID:000000034↗

DOE OSTI2025

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗

NASA NTRS2005

The effect of purified compared with nonpurified diet on bone changes induced by hindlimb suspension of female rats

The purpose of this study was to compare the bone changes induced by unloading in rats fed different diets, because space flight studies use a semipurified diet, whereas space flight simulation studies typically use nonpurified diets. Female Sprague-Dawley rats were fed a purified American Institute of Nutrition (AIN) 93G diet or a standard nonpurified diet and kept ambulatory or subjected to unloading by hindlimb suspension (HLS) for 38 days. Bone mineral content (BMC), mechanical strength, and factors related to the diet that affect bone (i.e., urinary calcium excretion, estradiol, and corticosterone) were measured. Average food intakes (grams per day) differed for diets, but caloric intake (kilocalories per day) and the final body masses of treatment groups were similar. The HLS-induced decrease in femoral BMC was not statistically different for rats fed a nonpurified diet (-8.6%) compared with a purified AIN-93G diet (-11.4%). The HLS-induced decrease in femoral mechanical strength was not statistically different for rats fed a nonpurified diet (-24%) compared with a purified AIN-93G diet (-31%). However, bone lengths were decreased (P < 0.05) in rats fed a nonpurified diet compared with a purified diet. Plasma estradiol levels were lower (P < 0.05) in the HLS/AIN-93G group but similar in the HLS and ambulatory rats fed a nonpurified diet. Plasma estradiol was related to femoral BMC (r = 0.85, P < 0.01). Urinary calcium excretion was higher (P < 0.05) in rats fed a nonpurified diet than those fed a purified AIN-93G diet, which is consistent with the higher level of calcium in the nonpurified diet. Urinary corticosterone levels were higher (P < 0.05) in rats fed a nonpurified diet than rats fed the AIN-93G diet. Although the osteopenia induced by unloading was similar in both diet groups, there were differences in longitudinal bone growth, calcium excretion, plasma estradiol levels, and urinary corticosterone levels. Results indicate that the type of standard diet used is an important factor to consider when measuring bone end points.

Bone and Bones/pathology↗

NASA NTRS2018

Harmonized Landsat/Sentinel-2 Products for Land Monitoring

The Harmonized Landsat-8 and Sentinel-2 (HLS) project is a NASA initiative aiming to produce a seamless, harmonized surface reflectance record from the Operational Land Imager (OLI) and Multi-Spectral Instrument (MSI) aboard Landsat-8 and Sentinel-2 remote sensing satellites, respectively. The HLS products are based on a set of algorithms to obtain seamless products from both sensors (OLI and MSI): atmospheric correction, cloud and cloud-shadow masking, geographic co-registration and common gridding, bidirectional reflectance distribution function normalization and bandpass adjustment. As of version 1.3, the HLS v1.3 data set covers 9.12 million km2 and spans from first Landsat-8 data (2013) to present. HLS products provide near-daily surface reflectance information with a common geometric framework, and are suitable for a variety of agricultural and vegetation monitoring tasks, including analysis of crop type, condition, and phenology.

Masek, Jeffrey↗

NASA NTRSDate not supplied

NASA's Artemis Human Landing Systems

The Human Landing System (HLS) is the mode of transportation that will take astronauts to the lunar surface in NASA's Artemis lunar exploration program. On early missions, the astronauts will live inside the pressurized crew cabin portion of the lander for up to a week. The HLS program, based at Marshall Space Flight Center in Huntsville, Alabama, is working closely with commercial partners to build innovative and technically advanced lunar landers, leveraging decades of human spaceflight experience and the speed of the commercial sector. In addition to the NextSTEP-2 Broad Agency Announcement Appendix H Option A contract that includes one uncrewed and one crewed demonstration mission to the lunar surface, the HLS program is taking steps to establish a regular cadence of crewed missions to the surface of the Moon. This paper will discuss the HLS program’s latest development activities.

Lisa Watson-Morgan↗

NASA NTRSDate not supplied

NASA’s Artemis Human Landing Systems

The Human Landing System (HLS) is the mode of transportation that will take astronauts to the lunar surface in NASA's Artemis lunar exploration program. On early missions, the astronauts will live inside the pressurized crew cabin portion of the lander for up to a week. The HLS program, based at NASA’s Marshall Space Flight Center in Huntsville, Alabama, is working closely with commercial partners to build innovative and technically advanced lunar landers, leveraging decades of human spaceflight experience and the speed of the commercial sector. In addition to the NextSTEP-2 Broad Agency Announcement Appendix H Option A contract that includes one uncrewed and one crewed demonstration mission to the lunar surface, the HLS program is taking steps to establish a regular cadence of crewed missions to the surface of the Moon. This paper will discuss the HLS program’s latest development activities.

Lisa Watson-Morgan↗

NASA NTRSDate not supplied

Artemis: An Overview of NASA’s Activities to Return Humans to the Moon

NASA is well underway in its plans to return humans to the Moon and build long-term infrastructure both in orbit and on the surface. Following the Artemis I and Artemis II flight tests, NASA will begin crewed landings and regular missions to the Moon. Artemis will return value to the American public and the global community, enable groundbreaking scientific discovery, and prepare NASA and its partners for exploration of the solar system. Work on the initial Human Landing System (HLS), procurement activities for future HLS, and work on the initial modules of the lunar-orbiting Gateway are underway. International partnerships are playing a critical role in the planning, development, and execution of these missions. As of December 2021, three international partners have entered agreements to contribute to the Gateway and 14 nations have signed the Artemis accords. A new spacesuit acquisition strategy will stimulate economic growth and meet the growing needs of NASA and emerging customers. Requirements development is underway for an unpressurized lunar surface rover and plans for a pressurized rover, foundation surface habitat, and the Artemis Base Camp are in work. This paper will provide an update on the HLS demonstration missions and the procurement status for HLS services; a comprehensive look at the Gateway’s components and related statuses; a summary of planned surface systems; and an outline of the strategies NASA will use to successfully implement these plans, preparing for humanity's next giant leap: Mars.

Artemis↗

NASA NTRSDate not supplied

An Orbit Determination Comparison Study and Demonstration for Rendezvous and Docking in a Near Rectilinear Halo Orbit from the Lunar Surface

For the upcoming NASA Artemis III mission and those that follow, both the Human Landing System (HLS) and Orion programs are invested in understanding the impacts of ground tracking performance in supporting rendezvous and docking in a Near Rectilinear Halo Orbit (NRHO). Several critical questions must be answered to ensure mission success and crew safety and an assortment of analysis tools are being incorporated to address them. Two of these tools, LINCOV and MONTE, are currently providing program decision making results through HLS Insight, HLS NASA-collaborations, and Orion/Gateway cross-program analysis. To ensure consistency in the orbit determination performance, a comparison trade-study is performed using a low-lunar orbit to NRHO rendezvous scenario anticipated for the upcoming Artemis missions. An overview of the two analysis tools is provided along with a detailed step-by-step evaluation of the core capabilities and models related to the orbit determination process. This incremental comparison effort reveals both tools produce consistent solutions for the criteria investigated. Given the confidence in the orbit determination process and solutions generated, these results are then applied to demonstrate an integrated, closed-loop system performance where the HLS lander ascends from the lunar surface and successfully inserts into the NRHO relative to the Orion spacecraft in preparation for the final rendezvous and docking phase.

Linear Covariance Analysis↗