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At least 433 records · Page 24

All sources NASA NTRS DOE OSTI

NASA NTRS2017

Quantifying Uncertainties in Navigation and Orbit Propagation Analyses

A tool used to calculate dilution of precision (DOP) was created in order to assist the Space Communications and Navigation (SCaN) program to analyze current and future user missions. The SCaN Center for Engineering, Networks, Integration, and Communication (SCENIC) is developing a new user interface (UI) to augment and replace the capabilities of currently used commercial software, such as Systems Tool Kit (STK). The DOP tool will be integrated in the SCENIC UI and will be used to analyze the accuracy of navigation solutions. This tool was developed using MATLAB and free and open-source tools to save cost and to use already existing orbital software libraries. GPS DOP data was collected and used for validation purposes. The similarities between the DOP tool results and GPS data show that the DOP tool is performing correctly. Additional improvements can be made in the DOP tool to improve its accuracy and performance in analyzing navigation solutions.

dilution of precision↗

NASA NTRS2018

On-Orbit Verification of GLM Navigation on GOES-16

The GOES-R flight project has developed the Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) to perform independent INR evaluations of the optical instruments on the GOES-R series spacecraft. In this presentation, we document the development of navigation (NAV) evaluation capabilities within IPATS for the Geostationary Lightning Mapper (GLM). We also discuss the post-processing quality filtering developed for GLM NAV, and present example results for several GLM background image datasets. Initial results suggest that GOES-16 GLM is compliant with navigation requirements.

GLM↗

NASA NTRS2018

A Window in the Future of Planetary Surface Navigation

The presentation focuses on surface navigation and mapping challenges in planetary environments including Lunar and Martian surface. Imagery from precursor orbital missions are processed to provide a medium resolution, large coverage 2D and 3D maps used by the science and navigation teams. During the surface mission these mapping products together with the images captured from the on-board camera systems are used in rover localization and navigation.

planetary exploration↗

NASA NTRS2016

Challenges in Planetary Mapping and Surface Navigation

planetary mapping↗

NASA NTRS2021

Architecture and Operations of the OSIRIS-REx Independent Navigation Team

The Origins, Spectral Interpretation, Resource Identification, Security-Regolith Explorer (OSIRIS-REx) GoddardSpace Flight Center (GSFC) Independent Navigation Team (INT) performs center-finding and landmark-basedOptical Navigation (OpNav), Orbit Determination (OD), maneuver verification, and additional analyses in support ofnavigation operations motivated by a stringent set of science requirements. The INT has adopted a streamlined andagile approach to navigation operations support via a virtual operations environment, known as "OREX-NAV",which leverages existing capabilities of the Space Science Mission Operations (SSMO) virtual Multi-MissionOperations Center (vMMOC). The virtual environment architecture of OREX-NAV enables the INT to perform dailyoperational tasks and seamlessly interface with external mission networks, regardless of physical location. Throughthe automation and process adopted, the INT is able to keep pace with the rapid cadence of required deliverables.

OSIRIS-REx↗

NASA NTRS2020

Navigation Filter Design Best Practices

Onboard navigation and attitude estimation systems are at the heart of almost all of NASA’s missions, either on launch vehicles, robotic science spacecraft, or on crewed human exploration vehicles. Best practices for attitude estimation systems/filters are scattered throughout open literature, however, even within NASA there has been no previous attempt to codify this knowledge into a readily available design handbook. Without such a document, it is possible for isolated practitioners to lack understanding and appreciation of many tried and true approaches to successful and robust filter design and the implied cost/benefit trades associated with them. To aid designers of current and future missions, a handbook of navigation filter best practices has been developed and is introduced here. The development of this document is also an outgrowth of a recommendation made in an NESC summary of lessons learned from the DARPA Orbital Express mission to utilize best practices for rendezvous navigation filter design. With this handbook, future designers have a reference that establishes NASA’s best practices.

Navigation↗

NASA NTRSDate not supplied

Lunar Navigation Performance Using the Deep Space Network and Alternate Solutions to Support Precision Landing

As human exploration once again targets the surface of the Moon, questions continue to emerge regarding the necessity of Earth-based tracking systems, such as the Deep Space Network, for spacecraft navigation in support of lunar descent and landing. This paper will derive an extensive Deep Space Network sensor model for use in linear covariance analysis and demonstrate the resulting trajectory dispersions and navigation performance in comparison with alternate solutions, such as terrain relative navigation. An in-depth trade study with considerations for various trajectory profiles, time allocated to ground tracking, number of active ground stations, and interaction with other sensors will be conducted to shed significant insight into sensor suite requirements to ensure safe and precise landing on the Moon.

Navigation↗

NASA NTRS1997

ARTSN: An Automated Real-Time Spacecraft Navigation System

ARTSN, the Automated Real-Time Spacecraft Navigation system is a real time system for the automated navigation of interplanetary spacecraft. The system processes the real time DSN radio metric data flow and generates updated spacecraft solutions and the associated covariance. ARTSN is designed modularly with the user interface input and output separated from the data analysis modules. Designed for distributed application, the system provided, for the first time, an automated method of processing radio metric navigation data for interplanetary spacecraft.

ARTSN↗

NASA NTRS1995

Mission to Pluto: A Navigation Assessment

The navigation feasibility of directing a lightweight flyby/probe package to Pluto is discussed, including the subtopics of optical navigation, autonomous navigation, and interfacing with the Russian launcher.

spacecraft↗

NASA NTRS2021

Cislunar Autonomous Positioning System Technology Operations and Navigation Experiment (CAPSTONE)

NASA has partnered with Advanced Space to develop and build the Cislunar Autonomous Positioning System Technology Operations and Navigation Experiment (CAPSTONE) mission which will serve as a pathfinder for Near Rectilinear Halo Orbit (NHRO) operations around the Moon. The NHRO, (Perilune = 3,200 km; Apolune = 70,000 km) will be the intended orbit for the NASA’s Artemis Gateway lunar orbital platform. The CAPSTONE mission will validate simulations and confirm operational planning for Gateway while also validating performance of navigation and station-keeping requirements for the Power and Propulsion Element. Thus, this mission will provide operational experience to NASA, commercial, and international missions for operations in a demanding orbital regime. The baseline for CAPSTONE is to fly a 12U cubesat developed, integrated, and tested by Tyvak Nanosatellite Systems carrying a payload communications system capable of cross-link ranging with the Lunar Reconnaissance Orbiter (LRO), a dedicated payload flight computer for software demonstration, and a camera. The launch, coordinated by NASA Launch Services Program, will be provided by a Rocket Lab launch vehicle utilizing their new Proton upper stage to deploy the CAPSTONE spacecraft into the lunar orbit. The CAPSTONE mission is targeting a launch no earlier than September 23, 2021. Upon launch, the spacecraft will traverse a highly efficient transfer taking approximately three months to enter a primary demonstration phase in an NRHO for six months followed by a twelve month technology enhancement operations phase. The CAPSTONE Project is lead by Advanced Space, LLC of Boulder Colorado. Spacecraft development and mission operations will be conducted by Tyvak Nanosatellite Systems of Irvine, California. Noted objectives for the CAPSTONE mission will be to demonstrate the accessibility of NHROs, validate key operational concepts in the NHRO environment, lay a foundation for commercial support of future lunar operations and accelerate the availability of peer-to-peer navigation capabilities provided by the Cislunar Autonomous Positioning System (CAPS). The CAPSTONE mission is funded through NASA's Small Spacecraft Technology Program (SSTP), which is one of several programs in NASA’s Space Technology Mission Directorate. SSTP is chartered to develop and demonstrate technologies to enhance and expand the capabilities of small spacecraft with a particular focus on enabling new mission architectures through the use of small spacecraft, expanding the reach of small spacecraft to new destinations, and augmenting future missions with supporting small spacecraft. The launch for the CAPSTONE Mission is provided by Human Exploration & Operations Missions Directorate Advanced Exploration Systems Division. Coordination and Acquisition of the Launch is managed by NASA’s Launch Services Program. The CAPSTONE Mission and project status will be presented.

CAPSTONE↗

NASA NTRSDate not supplied

Volume Raycasting of GNSS Signals through Ground Structure Lidar for UAV Navigational Guidance and Safety Estimation

Autonomous UAS navigation at low altitudes is often hindered by degradation of GNSS position estimates. The line of sight from the UAS to orbital satellites may be intersected by foliage (which attenuates the received signal) and by buildings (which block the signal). Since the geometric ray from the presumed UAS position to each GNSS satellite orbital location is predictable, if a 3D survey of ground structures is available, the degree of blockage of each GNSS signal can be estimated. In this study we show raycasting from a UAS location to GNSS satellites at two flight locations: one with overlying structures and bordered by tall trees, and another in an arboreal canyon bordered by tall trees. We confirm the intermittent blockage of satellites in the first location sufficient to lose GNSS position fix. We demonstrate low-altitude GNSS fidelity forecasting via the raycasting method at the second location that can be used to plan navigable flight locations and altitudes. Finally, we match the GNSS signal strength with raycast-derived foliage obstruction depth at hundreds of observation times from 55 recordings collected over 14 days from November 2018 to February 2021 at the second location. This matching confirms that signal attenuation varies with the depth of foliage blockage along a saturating exponential curve, as found in prior continuous-wave RF studies. The exponent and saturation value are species dependent and therefore vary from site to site; once determined empirically, they can be used to characterize foliage along a particular flight path, and refine GNSS fidelity forecasts of flights along that path. The techniques described in this study show the feasibility of a survey method to construct low-altitude navigation safety maps and forecasts.

Navigation↗

NASA NTRSDate not supplied

Building Lunar Maps for Terrain Relative Navigation and Hazard Detection Applications

Terrain Relative Navigation (TRN) systems localize a spacecraft with respect to a map of the surface by comparing descent imagery to that reference map. The spacecraft position estimates can only be as accurate as the reference map itself. Accurate map products that are based on orbital reconnaissance data must be validated for navigation applications to ensure that all relevant error sources are minimized. Currently available map products have been generated for scientific applications, so the need for accurate TRN maps remains a gap to be filled for upcoming lunar lander missions, in particular missions to the South Pole region. Additionally, representative high-resolution maps that contain lander-scale features are needed for successful development and testing of Hazard Detection (HD) systems. This paper describes one of NASA’s current efforts to develop benchmark data sets that can be used for developing and testing TRN and HD algorithms as well as suggested processes and metrics for generating and validating lunar maps that can be used for navigation and hazard detection.

Lunar Maps↗

NASA NTRSDate not supplied

The Kinematic Navigation and Cartography Knapsack (KNaCK): Demonstrating SlLAM (Simultaneous Localization and Mapping) LiDAR as a Tool for Exploration and Mapping of Lunar Pits and Caves

KNaCK (Kinematic Navigation and Cartography Knapsack) is a backpack-mounted mobile LiDAR (Light Detection and Ranging) system. It can map its surroundings in 3 dimensions and localize itself in space. The project is exploring how LiDAR can advance terrain mapping and navigation at the lunar south pole. KNaCK is lead by Dr. Michael Zanetti of NASA MSFC’s Heliophysics and Planetary Science Branch. - KNaCK serves as - A test article for GPS denied mapping and navigation. - A test bed for SLAM (Simultaneous Localization and Mapping) algorithms. - A test bed for commercial LiDAR units. - A tool for terrestrial science.

LiDAR↗

NASA NTRSDate not supplied

Lidar-Based Safe Site Relative Navigation

There has been a renewed focus in exploration of the lunar surface and maximizing scientific potential of such missions is made possible in part by minimizing the time required to set up operations; that is, reducing transit time on the surface by increasing the landing precision with respect to the intended target. Established SPLICE project precision landing requirements necessitate a navigation filter architecture and underlying models developed specifically with these needs in mind. To date, test flights to characterize SPLICE GNC system performance have not provided a means to divert from the a priori selected landing site due to hazardous conditions. With the inclusion of a new sensor, the HDL, coupled with safe landing site selection algorithms, GNC can divert from the originally planned trajectory and navigate relative to the new targeted landing site. This work presents a novel hazard relative measurement model and covariance transformation methodology that enable the navigation system to inform a safe-site relative guidance profile to meet project precision landing goals.

Navigation↗

NASA NTRSDate not supplied

Perturbation Modeling and Navigation Tuning for a Crewed Station in Near Rectilinear Halo Orbit

NASA's Gateway program will build a crew-tended station in an Earth-Moon Near Rectilinear Halo Orbit (NRHO) to support deep space missions to the lunar surface and heliocentric space. The station in the NRHO will be visited by crewed vehicles including lunar landers that are high mass relative to Gateway. Docking events with large vehicles induce significant thruster plume and docking contact velocity perturbations. Crewed vehicles induce significant venting perturbations that can stress the navigation filter's ability to remain converged about an estimated state. The torques that arise from venting and lunar gravity gradient require reaction control system (RCS) thruster intervention, which themselves induce a velocity perturbation. The best method to model RCS thruster perturbations is currently in development. This paper simulates different RCS perturbation models, schedules, and an increasing magnitude to investigate the navigation sensitivity to different RCS perturbation models. From the RCS perturbation models simulated, a worst-case scenario is chosen for further scrutiny. The navigation filter is further tuned to handle the most disruptive perturbation modeling and the resulting performance is analyzed.

Navigation↗

NASA NTRS2024

Demonstration of the Orion Optical Navigation System on Artemis I

The Orion Optical Navigation (OpNav) System is a first-of-its-kind navigation capability that was demonstrated in space on the Artemis I mission. The OpNav System was tested under a variety of conditions, resulting in over one thousand images of Earth, Moon, and starfields. Pairing the images with ground tracking information, not only did Artemis I provide a basis to evaluate the performance of the Orion OpNav system, but produced a valuable set of imagery/data that can be used to further development and testing of other optical navigation systems.

optical navigation↗

NASA NTRS2024

Demonstration of the Orion Optical Navigation System on Artemis I

Optical Navigation↗

NASA NTRSDate not supplied

Improving Sim-to-Real Transfer in Vision-Based Robot Navigation Via Instance-Level GAN-Based Data Augmentation

Achieving robust vision-based robotic tasks requires large amounts of data, which are often difficult to obtain in real-world scenarios. Simulators and synthetic data offer a cost-effective alternative, but the visual gap between simulation and reality hinders the performance of models when deployed in real-world environments. In this paper, we present a data augmentation pipeline that integrates a foundation model (Segment Anything Model) with an unsupervised image-to-image translation model (CycleGAN) for instance-level domain transfer from simulation to reality. This pipeline enables the generation of realistic labeled data from synthetic images for training supervised machine learning models in vision-based navigation tasks. We evaluate our approach on real-world data for ego-vehicle pose estimation, a critical autonomous navigation task involving the prediction of cross-track position and heading angle relative to road center line markings. The results of our tests show that our GAN-based data augmentation pipeline significantly outperforms models trained solely on simulation data or on data processed with standard image augmentation methods for sim-to-real transfer, enhancing model robustness and generalizability in real-world scenarios. Our method provides a scalable and flexible data augmentation tool for leveraging large synthetic datasets to enhance vision-based robotic navigation tasks.

artificial intelligence↗