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Navigation of the orbit phase of the Near Earth Asteroid Rendezbous (NEAR) mission will require determination of certain physical parameters describing the size, shape, gravity field, attitude and inertial properties of Eros.
Robust navigation through rocky terrain by small mobile robots is important for maximizing science return from upcoming missions to Mars.
We examine, from a multimission program perspective, key issues and recommended strategies for three aspects of telecommunications and navigation support to future Mars exploration missions.
The new generation of Navigation Doppler Lidar has been designed, developed, and tested for lunar missions. Comprehensive environmental testing is performed to assess the performance of the instrument for upcoming lunar missions and future missions to the Moon and other planetary bodies.
The new generation of Navigation Doppler Lidar has been designed, developed, and tested for lunar missions. Comprehensive environmental testing is performed to assess the performance of the instrument for upcoming lunar missions and future missions to the Moon and other planetary bodies.
This document summarizes and updates the NESC Guidance, Navigation, and Control (GN&C) Technical Discipline Team’s (TDT) work to synthesize and document the current best practices for the design & development of robust and reliable GN&C systems for robotic and crewed (human-rated) spacecraft. These GN&C best practices for future science and exploration missions were derived from the lessons learned, both positive and negative, on earlier spaceflight projects, both robotic and crewed. An attempt has been made to capture preferred practices that reflect the key considerations, trades, and processes directly attributed to past mission success.
Transparent electro-optic (EO) devices can potentially be used for efficient, high speed, and low mass non-mechanical beam steering. Transmission through EO devices will affect the wavefront quality and aberration of both the transmitted and received laser light. The consequences of wavefront distortions on coherent detection are not fully understood. Therefore, we have tested the performance of coherent detection lidar using liquid crystal polarization grating (LCPG) beam steering device. We will discuss the impact of LCPG beam steering devices on the performance of NASA’s Navigation Doppler Lidar, built for providing altitude and vector velocity data to aerial and space vehicles.
BioSentinel was one of the ten 6U CubeSats launched by the Space Launch System (SLS) as part of the Artemis-I mission on November 16th, 2022. The spacecraft was deployed after the trans-lunar injection imparted by the upper stage of SLS, and performed a lunar flyby that provided the necessary energy to escape the Earth-Moon system into heliocentric space. The BioSentinel mission required flight dynamics support prior to flight to assess the deployment conditions and the trajectory design, as well as navigation support during flight, to provide orbit determination solutions for the mission and the Deep Space Network (DSN). This paper discusses the BioSentinel mission flight dynamics campaign, from mission design to the challenges and lessons learned encountered during flight.
During mission planning and execution, spacecraft operators must balance data collection and downlink, systems constraints, human factors, and navigation. As missions become increasingly complex and ambitious, these factors become more intricately entwined and conflicted. For example, a spacecraft’s position must be known accurately in order to point to and image a target. Large position errors may cause missed observations or require additional scanning that increases operations complexity and data volume. Some observations require imaging from specific relative geometries which adds orbit control and timing considerations. Adjusting the orbit may allow for optimal observability of environmental parameters and/or enable more efficient sensor coverage, but maneuver execution error adds uncertainty to the current state which impacts both characterization and coverage objectives.
The Artemis program advances towards operations on the lunar surface, where precise surface localization is a driving need for safety and science. Using the observable horizon as an image landmark allows for estimating the photographer’s position. This work analyzes the application of Perspective-n-Point (PnP) algorithms to this lunar localization problem. Batch simulations using lunar topography display the accuracy and drawbacks of these methods. Monte Carlo techniques show the pipeline’s solutions as measurements provided to a navigation filter. When filtered, these solutions have position errors of 50 meters or better, which is similar to or better than the performance of other methods of surface localization.
Landing on the Moon safely, softly, and precisely is still an extremely difficult task to achieve. Navigation Doppler Lidar (NDL) was designed to provide a solution to spaceflight landing vehicles in achieving this task. NDL is a high-performance, compact, and cost-effective velocity and altitude measurement instrument that has been developed, tested and improved over many years. Testing has now culminated with NDL’s successful operation on two spaceflight vehicles this year: Astrobotic’s Peregrine lunar lander and Intuitive Machines’ Odysseus lunar lander. Odysseus became the first U.S. vehicle to land successfully on the Moon since Apollo 17. The performance data obtained by NDL during the Intuitive Machines mission is exceptional, and this mission fully demonstrated NDL’s ability to produce critical velocity and altitude data for future robotic and human exploration missions to the Moon and other planetary bodies. This paper will give an overview of NDL briefly describing its history, architecture, and theory of operation. NDL’s system checkout on Astrobotic’s Peregrine lunar lander is then discussed followed by NDL’s performance during the Intuitive Machines Odysseus lunar lander mission. A description of how the data acquired by NDL is then used to perform trajectory reconstruction and animation is then provided. Finally, this paper concludes with potential uses of NDL’s acquired mission data to future applications.
Landing on the Moon safely, softly, and precisely is still an extremely difficult task to achieve. Navigation Doppler Lidar (NDL) was designed to provide a solution to spaceflight landing vehicles in achieving this task. NDL is a high-performance, compact, and cost-effective velocity and altitude measurement instrument that has been developed, tested and improved over many years. Testing has now culminated with NDL’s successful operation on two spaceflight vehicles this year: Astrobotic’s Peregrine lunar lander and Intuitive Machines’ Odysseus lunar lander. Odysseus became the first U.S. vehicle to land successfully on the Moon since Apollo 17. The performance data obtained by NDL during the Intuitive Machines mission is exceptional, and this mission fully demonstrated NDL’s ability to produce critical velocity and altitude data for future robotic and human exploration missions to the Moon and other planetary bodies. This paper will give an overview of NDL briefly describing its history, architecture, and theory of operation. NDL’s system checkout on Astrobotic’s Peregrine lunar lander is then discussed followed by NDL’s performance during the Intuitive Machines Odysseus lunar lander mission. A description of how the data acquired by NDL is then used to perform trajectory reconstruction and animation is then provided. Finally, this paper concludes with potential uses of NDL’s acquired mission data to future applications.
NASA Goddard Space Flight Center (GSFC) is supporting two large-scale Lagrange-Point Orbiting missions set to launch in the next two years; Space Weather Follow On (SWFO-L1) in 2025 and Roman Space Telescope (RST) in 2026. SWFO-L1 will operate in a Sun-Earth L1 Lissajous orbit and RST will operate in a Sun-Earth L2 quasi-halo orbit. As with many NASA missions, the engineering challenges are as unique as the scientific objectives. The navigation systems for these missions must meet the specific, demanding, and evolving needs of both spacecraft.
Nine years after the Paris Agreement was adopted, it is clear that negative-emission technologies are required to keep 1.5°C, or even 2°C, of global warming in reach. Yet carbon dioxide removal (CDR) strategies remain rife with political, technical, economic, environmental, and geographic challenges. Here, this Voices surveys the pitfalls of incorporating carbon-negative technologies into climate mitigation plans and asks: how can we navigate around the challenges to make CDR a reliable component of climate mitigation?
Synthetic polymers are highly customizable with tailored structures and functionality, yet this versatility generates challenges in the design of advanced materials due to the size and complexity of the design space. Thus, exploration and optimization of polymer properties using combinatorial libraries has become increasingly common, which requires careful selection of synthetic strategies, characterization techniques, and rapid processing workflows to obtain fundamental principles from these large data sets. Herein, we provide guidelines for strategic design of macromolecule libraries and workflows to efficiently navigate these high-dimensional design spaces. We describe synthetic methods for multiple library sizes and structures as well as characterization methods to rapidly generate data sets, including tools that can be adapted from biological workflows. We further highlight relevant insights from statistics and machine learning to aid in data featurization, representation, and analysis. This Perspective acts as a “user guide” for researchers interested in leveraging high-throughput screening toward the design of multifunctional polymers and predictive modeling of structure–property relationships in soft materials.
Abstract We investigate the potential of using Global Navigation Satellite System (GNSS) observations to directly forecast full tsunami waveforms in real time. We train convolutional neural networks to use less than 9 min of GNSS data to forecast the full tsunami waveforms over 6 hr at select locations, and obtain accurate forecasts on a test data set. Our training and test data consists of synthetic earthquakes and associated GNSS data generated for the Cascadia Subduction Zone using the MudPy software, and corresponding tsunami waveforms in Puget Sound computed using GeoClaw. We use the same suite of synthetic earthquakes and waveforms as in earlier work where tsunami waveforms were used for forecasting, and provide a comparison. We also explore varying the number of GNSS stations, their locations, and their observation durations.
Pollen and nectar resources are unevenly distributed over space and bees must make routing decisions when navigating patchy resources. Determining the patch selection process used by bees is crucial to understanding bee foraging over discontinuous landscapes. To elucidate this process, we developed four distinct probability models of bee movement where the size and the distance to the patch determined the attractiveness of a patch. A field experiment with a center patch and four peripheral patches of two distinct sizes and distances from the center was set up in two configurations. Empirical transition probabilities from the center to each peripheral patch were obtained at two sites and two years. The best model was identified by comparing observed and predicted transition probabilities, where predicted values were obtained by incorporating the spatial dimensions of the field experiment into each model’s mathematical expression. Bumble bees used both patch size and isolation distance when selecting a patch and could assess the total amount of resources available in a patch. Bumble bees prefer large, nearby patches. This information will facilitate the development of a predictive framework to the study of bee movement and of models that predict the movement of genetically engineered pollen in bee-pollinated crops.
Abstract Efficient synthesis recipes are needed to streamline the manufacturing of complex materials and to accelerate the realization of theoretically predicted materials. Often, the solid-state synthesis of multicomponent oxides is impeded by undesired by-product phases, which can kinetically trap reactions in an incomplete non-equilibrium state. Here we report a thermodynamic strategy to navigate high-dimensional phase diagrams in search of precursors that circumvent low-energy, competing by-products, while maximizing the reaction energy to drive fast phase transformation kinetics. Using a robotic inorganic materials synthesis laboratory, we perform a large-scale experimental validation of our precursor selection principles. For a set of 35 target quaternary oxides, with chemistries representative of intercalation battery cathodes and solid-state electrolytes, our robot performs 224 reactions spanning 27 elements with 28 unique precursors, operated by 1 human experimentalist. Our predicted precursors frequently yield target materials with higher phase purity than traditional precursors. Robotic laboratories offer an exciting platform for data-driven experimental synthesis science, from which we can develop fundamental insights to guide both human and robotic chemists.