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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Standalone Hazard Evaluation and Refinement From Instrument Findings (S.H.E.R.I.F.)

The Standalone Hazard Evaluation From Instrument Findings or SHERIF is a set of novel algorithms and associated framework designed to support the generation of Digital Elevation Maps (DEMs) from multiple LiDAR scans and perform Hazard Detection (HD) and Safe Site Selection with minimal input from other onboard systems. SHERIF can employ several techniques to perform Point Cloud registration(PCR) on disparate LiDAR scans of a planetary surface to generate a DEM which evolves over the course of a trajectory, with a flow designed to enable robustness. The framework also supports a variety of Hazard Detection and Safe Site Selection algorithms which can be run on the evolving DEM being produced. SHERIF features a robust and modular construction, allowing the user a high degree of flexibility in selecting and implementing whichever PCR and HD/SSL algorithms they may prefer, while maintaining the data products and sensor independence of the core SHERIF framework. SHERIF was recently tested in a hardware in the loop simulation at NASA JSC.

Entry Descent and Landing Guidance Navigation Cont↗
NASA NTRS2013

Doppler Lidar Sensor for Precision Navigation in GPS-Deprived Environment

Landing mission concepts that are being developed for exploration of solar system bodies are increasingly ambitious in their implementations and objectives. Most of these missions require accurate position and velocity data during their descent phase in order to ensure safe, soft landing at the pre-designated sites. Data from the vehicle's Inertial Measurement Unit will not be sufficient due to significant drift error after extended travel time in space. Therefore, an onboard sensor is required to provide the necessary data for landing in the GPS-deprived environment of space. For this reason, NASA Langley Research Center has been developing an advanced Doppler lidar sensor capable of providing accurate and reliable data suitable for operation in the highly constrained environment of space. The Doppler lidar transmits three laser beams in different directions toward the ground. The signal from each beam provides the platform velocity and range to the ground along the laser line-of-sight (LOS). The six LOS measurements are then combined in order to determine the three components of the vehicle velocity vector, and to accurately measure altitude and attitude angles relative to the local ground. These measurements are used by an autonomous Guidance, Navigation, and Control system to accurately navigate the vehicle from a few kilometers above the ground to the designated location and to execute a gentle touchdown. A prototype version of our lidar sensor has been completed for a closed-loop demonstration onboard a rocket-powered terrestrial free-flyer vehicle.

Amzajerdian, F.↗
NASA NTRS2021

Mars Reconnaissance Orbiter Navigation Strategy for Support of Mars 2020 Mission's Entry, Descent and Landing Sequence

The Mars Reconnaissance Orbiter provided primary relay support during the Mars 2020 mission’s entry, descent, and landing sequence on February 18, 2021. To position the orbiter for relay support during this sequence, two propulsive maneuvers were performed: the first on September 2, 2020 and the second on January 20, 2021. This paper documents the maneuver strategy developed by the navigation team for positioning the Mars Reconnaissance Orbiter to within phasing requirements to support the Mars 2020 mission during landing and provides details on how well those phasing requirements were met.

Woollands, Robyn M.↗
NASA NTRS1973

Flight test results from the CV990 simulated space shuttle during unpowered automatic approaches and landings

Unpowered automatic approaches and landings with a CV990 aircraft were conducted to study navigation, guidance, and control problems associated with terminal area approach and landing for the space shuttle. The flight tests were designed to study from 11,300 m to touchdown the performance of a navigation and guidance concept which utilized blended radio/inertial navigation using VOR, DME, and ILS as the ground navigation aids. In excess of fifty automatic approaches and landings were conducted. Preliminary results indicate that this concept may provide sufficient accuracy to accomplish automatic landing of the shuttle orbiter without air-breathing engines on a conventional size runway.

Edwards, F. G.↗
NASA NTRS1990

System Predicts Critical Runway Performance Parameters

Runway-navigation-monitor (RNM) and critical-distances-process electronic equipment designed to provide pilot with timely and reliable predictive navigation information relating to takeoff, landing and runway-turnoff operations. Enables pilot to make critical decisions about runway maneuvers with high confidence during emergencies. Utilizes ground-referenced position data only to drive purely navigational monitor system independent of statuses of systems in aircraft.

Millen, Ernest W.↗
NASA NTRS2015

Mars Reconnaissance Orbiter Navigation Strategy for Dual Support of Insight and ExoMars Entry, Descent and Landing Demonstrator Module in 2016

Mars Reconnaissance Orbiter (MRO) will support NASA's InSight Mission and ESA's ExoMars Entry, Descent and Landing Demonstrator Module (EDM) in the fall of 2016 when both landers arrive at Mars. MRO provided relay support during the Entry, Descent and Landing (EDL) sequences of Mars Phoenix Lander in 2008 and the Mars Science Laboratory in 2012. Unlike these missions, MRO will coordinate between two EDL events separated by only three weeks: InSight on September 28, 2016 and EDM on October 19, 2016. This paper describes MRO Navigation's maneuver strategy to move MRO's ascending node to meet the In- Sight EDL phasing requirement and support EDM.

EDM↗
NASA NTRSDate not supplied

Topographic Slant Range Modeling and Fault Detection for Precision Planetary Landing

This work presents a novel landing site relative topographic measurement model for aslant range sensor being utilized for precision planetary landing operations. The measurement model accounts for the local terrain the slant range sensor captures and leverages knowledge of the estimated landing site provided by the navigation filter. Notably, in contrast to previous works, the new model does not rely on surface normal approximation, reducing the model’s sensitivity to noisy digital elevation maps which represent the local topography. In addition to the measurement model, this work introduces a novel fault detection method, denoted the probabilistic inspection of topographic filter altitude likelihood (PITFAL) algorithm, that implements a statistical outlier rejection algorithm. PITFAL is designed for multi-beam slant range sensors, such as the Navigation Doppler LIDAR (NDL), and identifies statistically inconsistent range estimates through a consensus check on the set of apparent altitudes computed for each individual beam. These models are numerically validated by the Safe and Precise Landing Capability Evolution (SPLICE) project’s high-fidelity terrestrial and lunar lander simulations.

Davis W Adams↗
NASA NTRS2025

Topographic Slant Range Modeling and Fault Detection for Precision Planetary Landing

This work presents a novel landing site relative topographic measurement model for aslant range sensor being utilized for precision planetary landing operations. The measurement model accounts for the local terrain the slant range sensor captures and leverages knowledge of the estimated landing site provided by the navigation filter. Notably, in contrast to previous works, the new model does not rely on surface normal approximation, reducing the model’s sensitivity to noisy digital elevation maps which represent the local topography. In addition to the measurement model, this work introduces a novel fault detection method, denoted the probabilistic inspection of topographic filter altitude likelihood (PITFAL) algorithm, that implements a statistical outlier rejection algorithm. PITFAL is designed for multi-beam slant range sensors, such as the Navigation Doppler LIDAR (NDL), and identifies statistically inconsistent range estimates through a consensus check on the set of apparent altitudes computed for each individual beam. These models are numerically validated by the Safe and Precise Landing Capability Evolution (SPLICE) project’s high-fidelity terrestrial and lunar lander simulations.

Davis W Adams↗
NASA NTRS2019

Defining Navigation Requirements for Future Precision Lander Missions

Human mission concepts for both the Moon and Mars require landing precision capabilities beyond the current state of art. The Safe and Precise Landing Integrated Capabilities Evolution (SPLICE) project leverages previous work at NASA to develop multimission precision landing and hazard avoidance (PL&HA) technologies to meet the advanced landing requirements. SPLICE aims to develop guidance, navigation and control (GN&C) capabilities for both Moon and Mars lander missions. The approach the project is using to identify a standard suite begins with developing a PL&HA requirements matrix to inform technology investments. The approach to develop a multi-mission navigation requirements matrix includes identifying representative concepts of operations for both robotic and human missions. It also identifies navigation sensor options with estimated performance parameters. This paper summarizes the concepts of operations and sensors considered to develop the SPLICE PL&HA requirements matrix. A description of the analysis used to determine candidate optimal sensor suites and initial results are provided.

Dwyer Cianciolo, Alicia↗
NASA NTRS1982

Space Shuttle Orbiter descent navigation

The entry operational sequence (OPS 3) begins approximately 2 hours prior to the deorbit maneuver and continues through atmospheric entry, terminal area energy management (TAEM), approach and landing, and rollout. During this flight phase, the navigation state vector is estimated by the Space Shuttle Orbiter onboard navigation system. This estimate is computed using a six-element sequential Kalman filter, which blends inertial measurement unit (IMU) delta-velocity data with external navaid data. The external navaids available to the filter are tactical air navigation (TACAN), barometric altimeter, and microwave scan beam landing system (MSBLS). Attention is given to the functional design of the Orbiter navigation system, the descent navigation sensors and measurement processing, predicted Kalman gains, correlation coefficients, and current flights navigation performance.

Montez, M. N.↗