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By NASA
3 min read
2023 Entrepreneurs Challenge Winner Skyline Nav AI: Revolutionizing GPS-Independent Navigation with Computer Vision
NASA sponsored Entrepreneurs Challenge events in 2020, 2021, and 2023 to identify innovative ideas and technologies from small business start-ups with the potential to advance the agency’s science goals. To help leverage external funding sources for the development of innovative technologies of interest to NASA, SMD involved the venture capital community in Entrepreneurs Challenge events. Challenge winners were awarded prize money, and in 2023 the total Entrepreneurs Challenge prize value was $1M. Numerous challenge winners have subsequently received funding from both NASA and external sources (e.g., other government agencies or the venture capital community) to further develop their technologies.
Skyline Nav AI, a winner of the 2023 NASA Entrepreneurs Challenge, is pioneering GPS-independent navigation by leveraging cutting-edge computer vision models, artificial intelligence (AI), and edge computing.
Skyline Nav AI’s flagship technology offers precise, real-time geolocation without the need for GPS, Wi-Fi, or cellular networks. The system utilizes machine learning algorithms to analyze terrain and skyline features and match them with preloaded reference datasets, providing up to centimeter-level accuracy in GPS-denied environments. This capability could enable operations in areas where GPS signals are absent, blocked, degraded, spoofed, or jammed, including urban canyons, mountainous regions, and the Moon.
Skyline Nav AI’s flagship technology at work in New York to provide precise location by matching the detected skyline with a reference data set. The red line shows detection by Skyline Nav AI technology, the green line marks the true location in the reference satellite dataset, and the orange line represents the matched location (i.e., the location extracted from the satellite dataset using Skyline Nav AI algorithms). Skyline Nav’s visual navigation technology can deliver accuracy up to five meters, 95% of the time. The AI-powered visual positioning models continuously improve geolocation precision through pixel-level analysis and semantic segmentation of real-time images, offering high reliability without the need for GPS.
In addition to its visual-based AI, Skyline Nav AI’s software is optimized for edge computing, ensuring that all processing occurs locally on the user’s device. This design enables low-latency, real-time decision-making without constant satellite or cloud-based connectivity, making it ideal for disconnected environments such as combat zones or space missions.
Furthermore, Skyline Nav AI’s technology can be integrated with various sensors, including inertial measurement units (IMUs), lidar, and radar, to further enhance positioning accuracy. The combination of visual navigation and sensor fusion can enable centimeter-level accuracy, making the technology potentially useful for autonomous vehicles, drones, and robotics operating in environments where GPS is unreliable.
“Skyline Nav AI aims to provide the world with an accurate, resilient alternative to GPS,” says Kanwar Singh, CEO of Skyline Nav AI. “Our technology empowers users to navigate confidently in even the most challenging environments, and our recent recognition by NASA and other partners demonstrates the value of our innovative approach to autonomous navigation.”
Skyline Nav AI continues to expand its influence through partnerships with organizations such as NASA, the U.S. Department of Defense, and the commercial market. Recent collaborations include projects with MIT, Draper Labs, and AFRL (Air Force Research Laboratory), as well as winning the MOVE America 2024 Pitch competition and being a finalist in SXSW 2024.
Sponsoring Organization: The NASA Science Mission Directorate sponsored the Entrepreneurs Challenge events.
Project Leads: Kanwar Singh, Founder & CEO of Skyline Nav AI
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Last Updated Jan 07, 2025 Related Terms
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By Space Force
A prototype F-16 Fight Falcon cockpit collapsible ladder for agile combat employment and contingency operations emerged as the 2024 Spark Tank winner at the Pentagon.
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By NASA
The NASA Science Mission Directorate (SMD) instituted the Entrepreneurs Challenge to identify innovative ideas and technologies from small business start-ups with the potential to advance the agency’s science goals. Geolabe—a prize winner in the latest Entrepreneurs Challenge—has developed a way to use artificial intelligence to identify global methane emissions. Methane is a greenhouse gas that significantly contributes to global warming, and this promising new technology could provide data to help decision makers develop strategies to mitigate climate change.
SMD sponsored Entrepreneurs Challenge events in 2020, 2021, and 2023. Challenge winners were awarded prize money—in 2023 the total Entrepreneurs Challenge prize value was $1M. To help leverage external funding sources for the development of innovative technologies of interest to NASA, SMD involved the venture capital community in Entrepreneurs Challenge events. Numerous challenge winners have subsequently received funding from both NASA and external sources (e.g., other government agencies or the venture capital community) to further develop their technologies.
Each Entrepreneurs Challenge solicited submissions in specific focus areas such as mass spectrometry technology, quantum sensors, metamaterials-based sensor technologies, and more. The focus areas of the latest 2023 challenge included lunar surface payloads and climate science.
A recent Entrepreneurs Challenge success story involves 2023 challenge winner Geolabe—a startup founded by Dr. Claudia Hulbert and Dr. Bertrand Rouet-Leduc in 2020 in Los Alamos, New Mexico. The Geolabe team developed a method that uses artificial intelligence (AI) to automatically detect methane emissions on a global scale.
This image taken from a NASA visualization shows the complex patterns of methane emissions around the globe in 2018, based on data from satellites, inventories of human activities, and NASA global computer models. Credit: NASA’s Scientific Visualization Studio As global temperatures rise to record highs, the pressure to curb greenhouse gas emissions has intensified. Limiting methane emissions is particularly important since methane is the second largest contributor to global warming, and is estimated to account for approximately a third of global warming to date. Moreover, because methane stays in the atmosphere for a shorter amount of time compared to CO2, curbing methane emissions is widely considered to be one of the fastest ways to slow down the rate of global warming.
However, monitoring methane emissions and determining their quantities has been challenging due to the limitations of existing detection methods. Methane plumes are invisible and odorless, so they are typically detected with specialized equipment such as infrared cameras. The difficulty in finding these leaks from space is akin to finding a needle in a haystack. Leaks are distributed around the globe, and most of the methane plumes are relatively small, making them easy to miss in satellite data.
Multispectral satellite imagery has emerged as a viable methane detection tool in recent years, enabling routine measurements of methane plumes at a global scale every few days. However, with respect to methane, these measurements suffer from very poor signal to noise ratio, which has thus far allowed detection of only very large emissions (2-3 tons/hour) using manual methods.
This landscape of “mountains” and “valleys” speckled with glittering stars is actually the edge of a nearby, young, star-forming region called NGC 3324 in the Carina Nebula. Captured in infrared light by NASA’s new James Webb Space Telescope, this image reveals for the first time previously invisible areas of star birth. Credit: NASA, ESA, CSA, and STScI The Geolabe team has developed a deep learning architecture that automatically identifies methane signatures in existing open-source spectral satellite data and deconvolves the signal from the noise. This AI method enables automatic detection of methane leaks at 200kg/hour and above, which account for over 85% of the methane emissions in well-studied, large oil and gas basins. Information gained using this new technique could help inform efforts to mitigate methane emissions on Earth and automatically validate their effects. This Geolabe project was featured in Nature Communications on May 14, 2024.
SPONSORING ORGANIZATION
NASA Science Mission Directorate
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Last Updated Aug 20, 2024 Related Terms
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By NASA
3 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
NASA Johnson Space Center: ORDEM represents the state of the art in orbital debris models intended for engineering analysis. It is a data-driven model, relying on large quantities of radar, optical, in situ, and laboratory measurement data. When released, it was the first software code to include a model for different orbital debris material densities, population models from low Earth orbit (LEO) all the way to Geosynchronous orbit (GEO), and uncertainties in each debris population.
ORDEM allows users to compute the orbital debris flux on any satellite in Earth orbit. This allows satellite designers to mitigate possible orbital debris damage to a spacecraft and its instruments using shielding and design choices, thereby extending the useful life of the mission and its experiments. The model also has a mode that simulates debris telescope/radar observations from the ground. Both it and the spacecraft flux mode can be used to design experiments to measure the meteoroid and orbital debris environments.
ORDEM is used heavily in the hypervelocity protection community, those that design, build, and test shielding for spacecraft and rocket upper stages. The fidelity of the ORDEM model allows for the optimization of shielding to balance mission success criteria, risk posture, and cost considerations.
As both government and civilian actors continue to exploit the space environment for security, science, and the economy, it is important that we track the debris risks in increasingly crowded orbits, in order to minimize damage to these space assets to make sure these missions continue to operate safely. ORDEM is NASA’s primary tool for computing and mitigating these risks.
ORDEM is used by NASA, the Department of Defense, and other U.S. government agencies, directly or indirectly (via the Debris Assessment Software, MSC-26690-1) to evaluate collision risk for large trackable objects, as well as other mission-ending risks associated with small debris (such as tank ruptures or wiring cuts). In addition to the use as an engineering tool, ORDEM has been used by NASA and other missions in the conceptual design phase to analyze the frequency of orbital debris impacts on potential in situ sensors that could detect debris too small to be detected from ground-based assets.
Commercial and academic users of ORDEM include Boeing, SpaceX, Northrop Grumman, the University of Colorado, California Polytechnic State University, among many others. These end users, similar to the government users discussed above, use the software to (1) directly determine potential hazards to spaceflight resulting from flying through the debris environment, and (2) research how the debris environment varies over time to better understand what behaviors may be able to mitigate the growth of the environment.
The quality and quantity of data available to the NASA Orbital Debris Program Office (ODPO) for the building, verification, and validation of the ORDEM model is greater than for any other entity that performs similar research. Many of the models used by other research and engineering organizations are derived from the models that ODPO has published after developing them for use in ORDEM.
ORDEM Team
Alyssa Manis Andrew B, Vavrin Brent A. Buckalew Christopher L. Ostrom Heather Cowardin Jer-chyi Liou John H, Seago John Nicolaus Opiela Mark J. Matney, Ph.D. Matthew Horstman Phillip D. Anz-Meador, Ph.D. Quanette Juarez Paula H. Krisko, Ph.D. Yu-Lin Xu, Ph.D. Share
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Last Updated Jul 31, 2024 EditorBill Keeter Related Terms
Office of Technology, Policy and Strategy (OTPS) View the full article
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By NASA
4 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
NASA Ames Research Center: ProgPy is an open-source Python package supporting research and development of prognostics, health management, and predictive maintenance tools.
Prognostics is the science of prediction, and the field of Prognostics and Health Management (PHM) aims at estimating the current physical health of a system (e.g., motor, battery, etc.) and predicting how the system will degrade with use. The results of prognostics are used across industries to prevent failure, preserve safety, and reduce maintenance costs.
Prognostics, and prediction in general, is a very difficult and complex undertaking. Accurate prediction requires a model of the performance and degradation of complex systems as a function of time and use, estimation and management of uncertainty, representation of system use profiles, and ability to represent impact of neighboring systems and the environment. Any small discrepancy between the model and the actual system is compounded repeatedly, resulting in a large variation in the resulting prediction. For this reason, prognostics requires complex and capable algorithms, models, and software systems.
The ProgPy architecture can be thought of as three innovations: the Prognostic Models, the Prognostic Engine, Prognostic Support Tools.
The first part of the ProgPy innovation is the Prognostic Models. The model describes the prognostic behavior of the specific system of interest. ProgPy’s architecture includes a spectrum of modeling methodologies, ranging from physics-based models to entirely data-driven or hybrid techniques. Most users develop their own physics-based model, train one of the ProgPy data-driven models (e.g., Neural-Network models), or some hybrid of the two. A set of mature models for systems like batteries, electric motors, pumps, and valves are distributed in ProgPy. For these parameterized models, users tune the model to their specific system using the model tuning tools. The Prognostics Engine and Support Tools are built on top of these models, meaning a user that creates a new model will immediately be able to take advantage of the other features of ProgPy.
The Prognostic Engine is the most important part of ProgPy and forms the backbone of the software. The Prognostics Engine uses a Prognostics Model to perform the key functions of prognostics and health state estimation. The value in this design is that the Prognostics Engine can use any ProgPy model, whether it be a model distributed with ProgPy or a custom model created by users, to perform health state estimation and prognostics in a configurable way. The components of the Prognostics Engine are extendable, allowing users to implement their own state estimation or prediction algorithm for use with ProgPy models or use one distributed with ProgPy. Given the Prognostics Engine and a model, users can start performing prognostics for their application. This flexible and extendable framework for performing prognostics is truly novel and enables the widespread impact of ProgPy in the prognostic community.
The Prognostic Support Tools are a set of features that aid with the development, tuning, benchmarking, evaluation, and visualization of prognostic models and Prognostics Engine results (i.e., predictions). Like the Prognostic Engine, the support tools work equally with models distributed with ProgPy or custom models created by users. A user creating a model immediately has access to a wide array of tools to help them with their task.
Detailed documentation, examples, and tutorials of all these features are available to help users learn and use the software tools.
These three innovations of ProgPy implement architectures and widely used prognostics and health management functionality, supporting both researchers and practitioners. ProgPy combines technologies from across NASA projects and mission directorates, and external partners into a single package to support NASA missions and U.S. industries. Its innovative framework makes it applicable to a wide range of applications, providing enhanced capabilities not available in other, more limited, state-of-the-art software packages.
ProgPy offers unique features and a breadth and depth of unmatched capabilities when compared to other software in the field. It is novel in that it equips users with the tools necessary to do prognostics in their applications as-is, eliminating the need to adapt their use case to comply with the software available. This feature of ProgPy is an improvement upon the current state-of-the-art, as other prognostics software are often developed for specific use cases or based on a singular modeling method (Dadfarina and Drozdov, 2013; Davidson-Pilon, 2022; Schreiber, 2017). ProgPy’s unique approach opens a world of possibilities for researchers, practitioners, and developers in the field of prognostics and health management, as well as NASA missions and U.S. industries.
ProgPy Team:
Adam J Sweet, Aditya Tummala, Chetan Shrikant Kulkarni Christopher Allen Teubert Jason Watkins Kateyn Jarvis Griffith Matteo Corbetta Matthew John Daigle Miryam Stautkalns Portia Banerjee Share
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Last Updated Jul 31, 2024 EditorBill Keeter Related Terms
Office of Technology, Policy and Strategy (OTPS) View the full article
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