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At least 19 records

Analysis and Testing of a LIDAR-Based Approach to Terrain Relative Navigation for Precise Lunar Landing

To increase safety and land near pre-deployed resources, future NASA missions to the moon will require precision landing. A LIDAR-based terrain relative navigation (TRN) approach can achieve precision landing under any lighting conditions. This paper presents results from processing flash lidar and laser altimeter field test data that show LIDAR TRN can obtain position estimates less than 90m while automatically detecting and eliminating incorrect measurements using internal metrics on terrain relief and data correlation. Sensitivity studies show that the algorithm has no degradation in matching performance with initial position uncertainties up to 1.6 km

ion propulsion↗

Analysis and Testing of a LIDAR-Based Approach to Terrain Relative Navigation for Precise Lunar Landing

Capability for precise lunar landing is the goal for future NASA missions. A LIDAR-based terrain relative navigation (TRN) approach lets us achieve this goal and also land under any illumination conditions. Results from field test data showed that the LIDAR TRN algorithm obtained position estimates with mean error of about 20 meters and standard deviations of about 10 meters. Moreover, the algorithm was capable of providing 99 percent correct estimates by assessing the local terrain relief in the data. Also, the algorithm was able to handle initial position uncertainty of up to 1.6 kilometers without performance degradation.

laser altimeter↗

On-Orbit Autonomous Assembly from Nanosatellites

The On-Orbit Autonomous Assembly from Nanosatellites (OAAN) project will demonstrate autonomous control algorithms for rendezvous and docking maneuvers; low-power reconfigurable magnetic docking technology; and compact, lightweight and inexpensive precision relative navigation using carrier-phase differential (CD) GPS with a three-degree of freedom ground demonstration. CDGPS is a specific relative position determination method that measures the phase of the GPS carrier wave to yield relative position data accurate to.4 inch (1 centimeter). CDGPS is a technology commonly found in the surveying industry. The development and demonstration of these technologies will fill a current gap in the availability of proven autonomous rendezvous and docking systems for small satellites.

Precision relative navigation↗

Overview of Terrain Relative Navigation Approaches for Precise Lunar Landing

The driving precision landing requirement for the Autonomous Landing and Hazard Avoidance Technology project is to autonomously land within 100m of a predetermined location on the lunar surface. Traditional lunar landing approaches based on inertial sensing do not have the navigational precision to meet this requirement. The purpose of Terrain Relative Navigation (TRN) is to augment inertial navigation by providing position or bearing measurements relative to known surface landmarks. From these measurements, the navigational precision can be reduced to a level that meets the 100m requirement. There are three different TRN functions: global position estimation, local position estimation and velocity estimation. These functions can be achieved with active range sensing or passive imaging. This paper gives a survey of many TRN approaches and then presents some high fidelity simulation results for contour matching and area correlation approaches to TRN using active sensors. Since TRN requires an a-priori reference map, the paper concludes by describing past and future lunar imaging and digital elevation map data sets available for this purpose.

Terrain Relative Navigation (TRN)↗

Overview of terrain relative navigation approaches for Precise Lunar Landing

The driving precision landing requirement for the Autonomous Landing and Hazard Avoidance Technology project is to autonomously land within 100m of a predetermined location on the lunar surface. Traditional lunar landing approaches based on inertial sensing do not have the navigational precision to meet this requirement. The purpose of Terrain Relative Navigation (TRN) is to augment inertial navigation by providing position or bearing measurements relative to known surface landmarks. From these measurements, the navigational precision can be reduced to a level that meets the 100m requirement. There are three different TRN functions: global position estimation, local position estimation and velocity estimation. These functions can be achieved with active range sensing or passive imaging. This paper gives a survey of many TRN approaches and then presents some high fidelity simulation results for contour matching and area correlation approaches to TRN using active sensors. Since TRN requires an a-priori reference map, the paper concludes by describing past and future lunar imaging and digital elevation map data sets available for this purpose.

Montgomery, James F.↗

On-Orbit Cross Calibration Between the Osiris-Rex Orbiting Laser Altimeter and Navigation Camera

Accurate shape models of small bodies provide both science value and resources for precision relative navigation. One instrument commonly used for generating shape models is the laser altimeter. Laser altimeters require accurate intrinsic and extrinsic calibration knowledge to produce accurate shape models. Here, we describe a method used for updating the intrinsic and extrinsic calibration parameters of the laser altimeter onboard the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) spacecraft, OLA (OSIRISREx Laser Altimeter). We perform the calibration update by comparing the OLA scans with monocular camera images of the asteroid surface captured by the primary navigation camera.

Andrew J. Liounis↗

Evaluation of Precision Landing Performance Using A Generalized Aerospace Simulation in Simulink Framework

NASA’s science and exploration goals to return to the Moon and beyond will need to perform precision landings to place humans and cargo supplies near places of scientific interest, surface resources, or pre-established basecamps. With the maturation of new navigation technology, such as terrain relative navigation, precision landing is now feasible, enabling new exploration sites, such as the lunar poles. However, verification of precision landing performance becomes crucial since not reaching the designated landing site would have a high risk of loss of mission. Therefore, having a high-fidelity simulation platform to evaluate six degrees of freedom vehicle performance during high-risk phases of flight such landing is a fundamental part of the system verification and risk reduction. The NASA Marshall Space Flight Center has developed the GeneraLized Aerospace Simulation in Simulink® (GLASS) tool which incorporates guidance, navigation, and control algorithms, as well as vehicle and environmental models, such as gravity, vehicle mass properties, navigation sensors, propulsion, and terrain models. GLASS uses the MathWorks® Simulink® environment which provides a model-based design framework that allows the incorporation of vehicle models in a modular architecture. The Simulink® environment provides seamless integration with all the MathWorks® capabilities and toolboxes, such as control design toolboxes and Simscape™ Multibody™ dynamics toolbox. The MathWorks® environment also allows for guidance, navigation, and control algorithms to be auto coded in C language, enabling quick software and hardware in the loop testing. This paper provides an overview of GLASS capabilities for analyzing precision landing performance, including navigation trades applied to a NASA human lander reference design architecture.

Guidance↗

4D Dynamic RNP Annual Interim Report-Year 1

This Annual Interim Report summarizes the activities led by Raytheon, in collaboration with GE Aviation and SAIC, and presents the results obtained during the first year of this research effort to expand the RNP concept to 4 dimensions relative to a dynamic frame of reference. Joint Program Development Office (JPDO)Concepts of Operations for the Next Generation Air Transportation System (NextGen) considers 4 Dimension Trajectory (4DT) procedures a key enabler to Trajectory Based Operations (TBO). The JPDO defines 4DT as a precise description of an aircraft path in space and time . While NextGen assumes that this path is defined within an Earth-reference frame, many 4DT procedure implementations will require an aircraft to precisely navigate relative to a moving reference such as another aircraft to form aggregate flows or a weather cell to allow for flows to shift. Current methods of implementing routes and flight paths rely on aircraft meeting a Required Navigation Performance (RNP) specification and being equipped with a monitoring and alerting capability to annunciate when the aircraft system is unable to meet the performance specification required for the operation. Since all aircraft today operate within the NAS relative to fixed reference points, the current RNP definition is deemed satisfactory. However, it is not well understood how the current RNP construct will support NextGen 4DT procedures where aircraft operate relative to each other or to other dynamic frames of reference. The objective of this research effort is to analyze candidate 4DT procedures from both an Air Navigation Service Provider (ANSP) and aircraft perspective, to identify their specific navigational requirements, assess the shortcomings of the current RNP construct to meet these requirements, to propose an extended 4 Dimensional Dynamic RNP (4D Dynamic RNP) construct that accounts for the dynamic spatial and temporal nature of the selected 4DT procedures, and finally, to design an experiment using the Airspace and Traffic Operations Simulation (ATOS) system to validate the 4D Dynamic RNP construct. This Annual Interim Report summarizes the activities led by Raytheon, in collaboration with GE Aviation and SAIC, and presents the results obtained during the first year of this research effort to expand the RNP concept to 4 dimensions relative to a dynamic frame of reference. A comprehensive assessment of the state-of-the-art international implementation of current RNP was completed and presented in the Contractor Report RNP State-of-the-Art Assessment, Version 4, 17 December 2008 . The team defined in detail two 4DT operations, Airborne Precision Spacing and Self-Separation, that are ideally suited to be supported by 4D Dynamic RNP and developed their respective conceptual frameworks, Required Interval Management Performance (RIMP) Version 1.1, 13 April 2009 and Required Self Separation Performance (RSSP) Version 1.1, 13 April 2009 . Finally, the team started the development of a mathematical model and simulation tool for RIMP and RSSP scheduled to be delivered during the second year of this research effort.

Finkelsztein, Daniel M.↗

NASA Johnson Space Center: Mini AERCam Testing with GSS6560

This slide presentation reviews the testing of the Miniature Autonomous Extravehicular Robotic Camera (Mini AERCam) with the GPS/SBAS simulation system, GSS6560. There is a listing of several GPS based programs at NASA Johnson, including the testing of Shuttle testing of the GPS system. Including information about Space Integrated GPS/INS (SIGI) testing. There is also information about the standalone ISS SIGI test,and testing of the SIGI for the Crew Return Vehicle. The Mini AERCam is a small, free-flying camera for remote inspections of the ISS, it uses precise relative navigation with differential carrier phase GPS to provide situational awareness to operators. The closed loop orbital testing with and without the use of the GSS6550 system of the Mini AERCam system is reviewed.

Cryant, Scott P.↗

4D Dynamic Required Navigation Performance Final Report

New advanced four dimensional trajectory (4DT) procedures under consideration for the Next Generation Air Transportation System (NextGen) require an aircraft to precisely navigate relative to a moving reference such as another aircraft. Examples are Self-Separation for enroute operations and Interval Management for in-trail and merging operations. The current construct of Required Navigation Performance (RNP), defined for fixed-reference-frame navigation, is not sufficiently specified to be applicable to defining performance levels of such air-to-air procedures. An extension of RNP to air-to-air navigation would enable these advanced procedures to be implemented with a specified level of performance. The objective of this research effort was to propose new 4D Dynamic RNP constructs that account for the dynamic spatial and temporal nature of Interval Management and Self-Separation, develop mathematical models of the Dynamic RNP constructs, "Required Self-Separation Performance" and "Required Interval Management Performance," and to analyze the performance characteristics of these air-to-air procedures using the newly developed models. This final report summarizes the activities led by Raytheon, in collaboration with GE Aviation and SAIC, and presents the results from this research effort to expand the RNP concept to a dynamic 4D frame of reference.

Finkelsztein, Daniel M.↗

Digital Elevation Map Parametric Error Analysis Using Corresponding NAC Images

Future lunar landing systems, particularly those used to land humans on the lunar surface aspart of the ARTEMIS program, will require precision navigation relative to the lunar surface. The most common way to meet these stringent navigation require-ments is through terrain relative navigation (TRN), which localizes a spacecraft by comparing descent im-agery with a predefined map of the surface. The accu-racy achievable using TRN is limited by the accuracy of the reference Digital Elevation Map (DEM). It is therefore critical for future lunar missions that potential errors in DEMs be quantified. This paper describes one of NASA’s current efforts to develop a process for evaluating lunar DEM quality.

Chris R Gnam↗

Digital Elevation Map Parametric Error Analysis Pipeline using Corresponding NAC Images

Future lunar landing systems, particularly those used to land humans on the lunar surface as part of the ARTEMIS program, will require precision navigation relative to the lunar surface. The most common way to meet these stringent navigation requirements is through terrain relative navigation (TRN), which localizes a spacecraft by comparing descent imagery with a predefined map of the surface. The accuracy achievable using TRN is limited by the accuracy of the reference Digital Elevation Map (DEM). It is therefore critical for future lunar missions that potential errors in DEMs be quantified. This paper describes one of NASA’s current efforts to develop a process for evaluating lunar DEM quality.

Chris Gnam↗

Machine Learning Based Crater Detection for Terrain Relative Navigation

As Lunar exploration continues to become more commonplace, reliable methods of precise Terrain Relative Navigation (TRN) are needed. While there are many TRN techniques available, one that has received increased interest in the past few years is that of crater based navigation. Crater based navigation has numerous benefits, including being a human recognizable feature (important for crewed missions), as well as the fact that craters are often possible hazards that need to be detected and avoided. The use of crater based navigation has been limited however. This has been due to the difficulty of running such algorithms on board a spacecraft, as well as the difficulty in procuring large amounts of the required training data. This paper presents a new rendering tool for generating large amounts of high quality training data. It then looks at two recently developed machine learning techniques for crater detection and crater identification in real-time on near-future space hardware.

computer vision↗

Approach-Phase Precision Landing with Hazard Relative Navigation: Terrestrial Test Campaign Results of the Morpheus/ALHAT Project

The Morpheus Project began in late 2009 as an ambitious e ort code-named Project M to integrate three ongoing multi-center NASA technology developments: humanoid robotics, liquid oxygen/liquid methane (LOX/LCH4) propulsion and Autonomous Precision Landing and Hazard Avoidance Technology (ALHAT) into a single engineering demonstration mission to be own to the Moon by 2013. The humanoid robot e ort was redirected to a deploy- ment of Robonaut 2 on the International Space Station in February of 2011 while Morpheus continued as a terrestrial eld test project integrating the existing ALHAT Project's tech- nologies into a sub-orbital ight system using the world's rst LOX/LCH4 main propulsion and reaction control system fed from the same blowdown tanks. A series of 33 tethered tests with the Morpheus 1.0 vehicle and Morpheus 1.5 vehicle were conducted from April 2011 - December 2013 before successful, sustained free ights with the primary Vertical Testbed (VTB) navigation con guration began with Free Flight 3 on December 10, 2013. Over the course of the following 12 free ights and 3 tethered ights, components of the ALHAT navigation system were integrated into the Morpheus vehicle, operations, and ight control loop. The ALHAT navigation system was integrated and run concurrently with the VTB navigation system as a reference and fail-safe option in ight (see touchdown position esti- mate comparisons in Fig. 1). Flight testing completed with Free Flight 15 on December 15, 2014 with a completely autonomous Hazard Detection and Avoidance (HDA), integration of surface relative and Hazard Relative Navigation (HRN) measurements into the onboard dual-state inertial estimator Kalman lter software, and landing within 2 meters of the VTB GPS-based navigation solution at the safe landing site target. This paper describes the Mor- pheus joint VTB/ALHAT navigation architecture, the sensors utilized during the terrestrial ight campaign, issues resolved during testing, and the navigation results from the ight tests.

Crain, Timothy P.↗

Precision Landing and Hazard Avoidance Doman

The Precision Landing and Hazard Avoidance (PL&HA) domain addresses the development, integration, testing, and spaceflight infusion of sensing, processing, and GN&C functions critical to the success and safety of future human and robotic exploration missions. PL&HA sensors also have applications to other mission events, such as rendezvous and docking. Autonomous PL&HA builds upon the core GN&C capabilities developed to enable soft, controlled landings on the Moon, Mars, and other solar system bodies. Through the addition of a Terrain Relative Navigation (TRN) function, precision landing within tens of meters of a map-based target is possible. The addition of a 3-D terrain mapping lidar sensor improves the probability of a safe landing via autonomous, real-time Hazard Detection and Avoidance (HDA). PL&HA significantly improves the probability of mission success and enhances access to sites of scientific interest located in challenging terrain. PL&HA can also utilize external navigation aids, such as navigation satellites and surface beacons. Advanced Lidar Sensors High precision ranging, velocimetry, and 3-D terrain mapping Terrain Relative Navigation (TRN) TRN compares onboard reconnaissance data with real-time terrain imaging data to update the S/C position estimate Hazard Detection and Avoidance (HDA) Generates a high-resolution, 3-D terrain map in real-time during the approach trajectory to identify safe landing targets Inertial Navigation During Terminal Descent High precision surface relative sensors enable accurate inertial navigation during terminal descent and a tightly controlled touchdown within meters of the selected safe landing target.

Robertson, Edward A.↗

Toward Improved Landing Precision on Mars

Mars landers to date have flown ballistic entry trajectories with no trajectory control after the final maneuver before entry. Improvements in landing accuracies (from approx.150 km from the target for Mars Pathfinder to approx.30-40 km for MER and Phoenix) have been driven by approach navigation improvements. MSL will fly the first guided-entry trajectory to Mars, further improving accuracy to approx.10-12 km from the target.

powered descent guidance↗

Advancement of Deep Learning and Geometric Methods for Active Terrain Relative Navigation

To enhance NASA’s precision landing capabilities, in conjunction with the development of a novel active terrain relative navigation (ATRN) and terrain mapping system, denoted SHERIF, this work performed a comparative analysis between both deep-learning (DL) based and geometric approaches to hazard detection (HD) and safe-site-identification (SSI) through hardware-in-the loop testing on the Six degree-of-freedom Tendon Actuated Robot (STAR). The Standalone Hazard Evaluation and Refinement using Instrument Findings (SHERIF) system is capable of ingesting sensor data at an asynchronous rate, stitching successive terrain scans together to yield a high-resolution digital elevation map (DEM), performing absolute and relative localization using novel 3D feature extraction and matching methods, and HD/SSI activities. The DL-based HD/SSI algorithm provides a modular alternative to classical geometric approaches which have performance times that scale with map resolution. As the adoption of AI solutions become more prevalent for autonomous system decision making, it is prudent to explore the utility of such solutions in applications where they traditionally excel, such as image classification. Along with the development of a DL-based HD system, this work performed the first comparative analysis between DL and geometric approaches to HD/SSI using real sensor data from real-time testing in a relevant environment.

Davis Adams↗