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Johnson, Andrew E.

Publications and source records attributed to Johnson, Andrew E..

35 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Compute Element and Interface Box for the Hazard Detection System

The Autonomous Landing and Hazard Avoidance Technology (ALHAT) program is building a sensor that enables a spacecraft to evaluate autonomously a potential landing area to generate a list of hazardous and safe landing sites. It will also provide navigation inputs relative to those safe sites. The Hazard Detection System Compute Element (HDS-CE) box combines a field-programmable gate array (FPGA) board for sensor integration and timing, with a multicore computer board for processing. The FPGA does system-level timing and data aggregation, and acts as a go-between, removing the real-time requirements from the processor and labeling events with a high resolution time. The processor manages the behavior of the system, controls the instruments connected to the HDS-CE, and services the "heavy lifting" computational requirements for analyzing the potential landing spots.

Villalpando, Carlos Y.↗
NASA NTRS2011

Lidar-Based Navigation Algorithm for Safe Lunar Landing

The purpose of Hazard Relative Navigation (HRN) is to provide measurements to the Navigation Filter so that it can limit errors on the position estimate after hazards have been detected. The hazards are detected by processing a hazard digital elevation map (HDEM). The HRN process takes lidar images as the spacecraft descends to the surface and matches these to the HDEM to compute relative position measurements. Since the HDEM has the hazards embedded in it, the position measurements are relative to the hazards, hence the name Hazard Relative Navigation.

Myers, David M.↗
NASA NTRS2011

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↗
NASA NTRS2011

Hazard Detection Software for Lunar Landing

The Autonomous Landing and Hazard Avoidance Technology (ALHAT) Project is developing a system for safe and precise manned lunar landing that involves novel sensors, but also specific algorithms. ALHAT has selected imaging LIDAR (light detection and ranging) as the sensing modality for onboard hazard detection because imaging LIDARs can rapidly generate direct measurements of the lunar surface elevation from high altitude. Then, starting with the LIDAR-based Hazard Detection and Avoidance (HDA) algorithm developed for Mars Landing, JPL has developed a mature set of HDA software for the manned lunar landing problem. Landing hazards exist everywhere on the Moon, and many of the more desirable landing sites are near the most hazardous terrain, so HDA is needed to autonomously and safely land payloads over much of the lunar surface. The HDA requirements used in the ALHAT project are to detect hazards that are 0.3 m tall or higher and slopes that are 5 or greater. Steep slopes, rocks, cliffs, and gullies are all hazards for landing and, by computing the local slope and roughness in an elevation map, all of these hazards can be detected. The algorithm in this innovation is used to measure slope and roughness hazards. In addition to detecting these hazards, the HDA capability also is able to find a safe landing site free of these hazards for a lunar lander with diameter .15 m over most of the lunar surface. This software includes an implementation of the HDA algorithm, software for generating simulated lunar terrain maps for testing, hazard detection performance analysis tools, and associated documentation. The HDA software has been deployed to Langley Research Center and integrated into the POST II Monte Carlo simulation environment. The high-fidelity Monte Carlo simulations determine the required ground spacing between LIDAR samples (ground sample distances) and the noise on the LIDAR range measurement. This simulation has also been used to determine the effect of viewing on hazard detection performance. The software has also been deployed to Johnson Space Center and integrated into the ALHAT real-time Hardware-in-the-Loop testbed.

Huertas, Andres↗
NASA NTRS2010

Terrain Hazard Detection and Avoidance During the Descent and Landing Phase of the Altair Mission

This paper describes some of the environmental challenges associated with landing a crewed or robotic vehicle at any certified location on the lunar surface (i.e. not a mountain peak, permanently dark crater floor or overly steep terrain), with a specific focus on how hazard detection technology may be incorporated to mitigate these challenges. For this discussion, the vehicle of interest is the Altair Lunar Lander, being the vehicle element of the NASA Constellation Program aimed at returning humans to the moon. Lunar environmental challenges for such global lunar access primarily involve terrain and lighting. These would include sizable rocks and slopes, which are more concentrated in highland areas; small craters, which are essentially everywhere independent of terrain type; and for polar regions, low-angle sunlight, which leaves significant terrain in shadow. To address these issues, as well as to provide for precision landing, the Autonomous Landing and Hazard Avoidance Technology (ALHAT) Project was charted by NASA Headquarters, and has since been making significant progress. The ALHAT team considered several sensors for real-time hazard detection, settling on the use of a Flash Lidar mounted to a high-speed gimbal, with computationally intense image processing and elevation interpretation software. The Altair Project has been working with the ALHAT team to understand the capabilities and limitations of their concept, and has incorporated much of the ALHAT hazard detection system into the Altair baseline design. This integration, along with open issues relating to computational performance, the need for system redundancy, and potential pilot interaction, will be explored further in this paper.

Strhan, Alan L.↗
NASA NTRS2010

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↗
NASA NTRS2010

Post2 End-to-End Descent and Landing Simulation for ALHAT Design Analysis Cycle 2

The ALHAT project is an agency-level program involving NASA centers, academia, and industry, with a primary goal to develop a safe, autonomous, precision-landing system for robotic and crew-piloted lunar and planetary descent vehicles. POST2 is used as the 6DOF descent and landing trajectory simulation for determining integrated system performance of ALHAT landing-system models and lunar environment models. This paper presents updates in the development of the ALHAT POST2 simulation, as well as preliminary system performance analysis for ALDAC-2 used for the testing and assessment of ALHAT system models. The ALDAC-2 POST2 Monte Carlo simulation results have been generated and focus on HRN model performance with the fully integrated system, as well performance improvements of AGNC and TSAR model since the previous design analysis cycle

Davis, Jody L.↗
NASA NTRS2010

Analysis of Flash Lidar Field Test Data for Safe Lunar Landing

In May 2008, the Autonomous Landing and Hazard Avoidance Technology (ALHAT) Project conducted a helicopter field test of a commercial flash lidar to assess its applicability to safe lunar landing. The helicopter flew several flights, which covered a variety of slant ranges and viewing angles, over man-made and natural lunar-like terrains. The collected data were analyzed to assess the performance of the sensor and the performance of two algorithms: Hazard Detection (HD) and Hazard Relative Navigation (HRN). The collected flash lidar data were also used to validate a high fidelity flash lidar software model used in ALHAT Monte Carlo simulations. The field test results, combined with prior simulation results, advanced the technology readiness level of the HD algorithm to TRL 5 and the HRN algorithm to TRL 4.

hazard relative navigation↗
NASA NTRS2009

Processing Images of Craters for Spacecraft Navigation

A crater-detection algorithm has been conceived to enable automation of what, heretofore, have been manual processes for utilizing images of craters on a celestial body as landmarks for navigating a spacecraft flying near or landing on that body. The images are acquired by an electronic camera aboard the spacecraft, then digitized, then processed by the algorithm, which consists mainly of the following steps: 1. Edges in an image detected and placed in a database. 2. Crater rim edges are selected from the edge database. 3. Edges that belong to the same crater are grouped together. 4. An ellipse is fitted to each group of crater edges. 5. Ellipses are refined directly in the image domain to reduce errors introduced in the detection of edges and fitting of ellipses. 6. The quality of each detected crater is evaluated. It is planned to utilize this algorithm as the basis of a computer program for automated, real-time, onboard processing of crater-image data. Experimental studies have led to the conclusion that this algorithm is capable of a detection rate >93 percent, a false-alarm rate <5 percent, a geometric error <0.5 pixel, and a position error <0.3 pixel.

Cheng, Yang↗
NASA NTRS2008

Advances in POST2 End-to-End Descent and Landing Simulation for the ALHAT Project

Program to Optimize Simulated Trajectories II (POST2) is used as a basis for an end-to-end descent and landing trajectory simulation that is essential in determining design and integration capability and system performance of the lunar descent and landing system and environment models for the Autonomous Landing and Hazard Avoidance Technology (ALHAT) project. The POST2 simulation provides a six degree-of-freedom capability necessary to test, design and operate a descent and landing system for successful lunar landing. This paper presents advances in the development and model-implementation of the POST2 simulation, as well as preliminary system performance analysis, used for the testing and evaluation of ALHAT project system models.

Davis, Jody L.↗
NASA NTRS2008

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)↗
NASA NTRS2008

Analysis of On-board Hazard Detection and Avoidance for Safe Lunar Landing

Landing hazard detection and avoidance technology is being pursued within NASA to improve landing safety and increase access to sites of interest on the lunar surface. The performance of a hazard detection and avoidance system depends on properties of the terrain, sensor performance, algorithm design, vehicle characteristics and the overall all guidance navigation and control architecture. This paper analyzes the size of the region that must be imaged, sensor performance parameters and the impact of trajectory angle on hazard detection performance. The analysis shows that vehicle hazard tolerance is the driving parameter for hazard detection system design.

hazard detection↗
NASA NTRS2008

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.↗
NASA NTRS2005

Vision guided landing of an an autonomous helicopter in hazardous terrain

Future robotic space missions will employ a precision soft-landing capability that will enable exploration of previously inaccessible sites that have strong scientific significance. To enable this capability, a fully autonomous onboard system that identifies and avoids hazardous features such as steep slopes and large rocks is required. Such a system will also provide greater functionality in unstructured terrain to unmanned aerial vehicles. This paper describes an algorithm for landing hazard avoidance based on images from a single moving camera. The core of the algorithm is an efficient application of structure from motion to generate a dense elevation map of the landing area. Hazards are then detected in this map and a safe landing site is selected. The algorithm has been implemented on an autonomous helicopter testbed and demonstrated four times resulting in the first autonomous landing of an unmanned helicopter in unknown and hazardous terrain.

Mars Landing↗
NASA NTRS1998

Precise Image-Based Motion Estimation for Autonomous Small Body Exploration

Space science and solar system exploration are driving NASA to develop an array of small body missions ranging in scope from near body flybys to complete sample return. This paper presents an algorithm for onboard motion estimation that will enable the precision guidance necessary for autonomous small body landing. Our techniques are based on automatic feature tracking between a pair of descent camera images followed by two frame motion estimation and scale recovery using laser altimetry data. The output of our algorithm is an estimate of rigid motion (attitude and position) and motion covariance between frames. This motion estimate can be passed directly to the spacecraft guidance and control system to enable rapid execution of safe and precise trajectories.

Johnson, Andrew E.↗