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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 289 records · Page 16

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NASA NTRS2001

Highly Protable Airborne Multispectral Imaging System

A portable instrumentation system is described that includes and airborne and a ground-based subsytem. It can acquire multispectral image data over swaths of terrain ranging in width from about 1.5 to 1 km. The system was developed especially for use in coastal environments and is well suited for performing remote sensing and general environmental monitoring. It includes a small,munpilotaed, remotely controlled airplance that carries a forward-looking camera for navigation, three downward-looking monochrome video cameras for imaging terrain in three spectral bands, a video transmitter, and a Global Positioning System (GPS) reciever.

Lehnemann, Robert↗

NASA NTRS2001

Multi-resolution mapping using surface, descent and orbit images

Our objective is to produce high-accuracy maps of the terrain elevation at landing sites on planetary bodies through the use of all available image data. These technologies are important for performing rover navigation in future space missions and the maps provide a tool for coordinating rovers in a robotic colony.

localization↗

NASA NTRSDate not supplied

Post-Flight Performance Analysis of Navigation and Advanced Guidance Algorithms on a Terrestrial Suborbital Rocket Flight

There is currently renewed interest in robotic and crewed landers for a return to the lunar surface. Advanced guidance and navigation algorithms are essential to accurately delivering cargo and crew safely to the moon successfully. This paper reports the overall performance of an integrated set of navigation and guidance algorithms flown on a terrestrial suborbital rocket up to an altitude of approximately 100km. The navigation algorithm consists of an onboard extended Kalman Filter (EKF) that ingests multiple sensor measurements, one of which is the output from a terrain relative navigation (TRN) algorithm that cross-references camera images to on-board satellite imagery to perform feature correlation within the camera image. The guidance algorithm solves for a 6-degree-of-freedom (DoF) optimal trajectory using a successive convexification method during powered descent. The altitude range as well as the landing dynamics experienced during this test flight are realistic for an extraterrestrial landing and provide an invaluable data set to gauge the current development of these landing algorithms in an effort to advance the overall software readiness levels (SRL). This paper will delve into different aspects of each algorithm and present an analysis of the in-flight performance of the algorithms. This flight was conducted under the National Aeronautics and Space Administration (NASA) Safe and Precise Landing Integrated Capabilities Evolution (SPLICE) project focused on technology advancement for landing applications.

Guidance↗

NASA NTRS2011

Fast Image Texture Classification Using Decision Trees

Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

Thompson, David R.↗

NASA NTRS2007

Improved Path Planning Onboard the Mars Exploration Rovers

A revised version of the AutoNav (autonomous navigation with hazard avoidance) software running onboard each Mars Exploration Rover (MER) affords better obstacle avoidance than does the previous version. Both versions include GESTALT (Grid-based Estimation of Surface Traversability Applied to Local Terrain), a navigation program that generates local-terrain models from stereoscopic image pairs captured by onboard rover cameras; uses this information to evaluate candidate arcs that extend across the terrain from the current rover location; ranks the arcs with respect to hazard avoidance, minimization of steering time, and the direction towards the goal; and combines the rankings in a weighted vote to select an arc, along which the rover is then driven. GESTALT works well in navigating around small isolated obstacles, but tends to fail when the goal is on the other side of a large obstacle or multiple closely spaced small obstacles. When that occurs, the goal seeking votes and hazard avoidance votes conflict severely. The hazard avoidance votes will not allow the rover to drive through the unsafe area, and the waypoint votes will not allow enough deviation from the straight-line path for the rover to get around the hazard. The rover becomes stuck and is unable to reach the goal. The revised version of AutoNav utilizes a global path-planning program, Field D*, to evaluate the cost of traveling from the end of each GESTALT arc to the goal. In the voting process, Field D* arc votes supplant GESTALT goal-seeking arc votes. Hazard avoidance, steering bias, and Field D* votes are merged and the rover is driven a preset distance along the arc with the highest vote. Then new images are acquired and the process as described is repeated until the goal is reached. This new technology allows the rovers to autonomously navigate around much more complex obstacle arrangements than was previously possible. In addition, this improved autonomy enables longer traverses per Sol (a day on Mars), and can make planning drives easier for operators on Earth.

Stentz, Anthony↗

NASA NTRS1994

Small image laser range finder for planetary rover

A variety of technical subjects need to be solved before planetary rover navigation could be a part of future missions. The sensors which will perceive terrain environment around the rover will require critical development efforts. The image laser range finder (ILRF) discussed here is one of the candidate sensors because of its advantage in providing range data required for its navigation. The authors developed a new compact-sized ILRF which is a quarter of the size of conventional ones. Instead of the current two directional scanning system which is comprised of nodding and polygon mirrors, the new ILRF is equipped with the new concept of a direct polygon mirror driving system, which successfully made its size compact to accommodate the design requirements. The paper reports on the design concept and preliminary technical specifications established in the current development phase.

Wakabayashi, Yasufumi↗

NASA NTRS1979

Autonomy in space navigation

The potential development of autonomous onboard navigation systems for future space missions is discussed. Motivations for autonomy in the near-earth satellite program and in deep-space navigation are examined, emphasizing lower mission-operation costs for the first, and reduction of ground-system complexity as well as encounter accuracy for the latter. Uses of navigation parameters by the Information Adaptive System, including sensor-pointing computation and image correction, are noted, as are the accuracy parameters of the NAVSTAR Global Positioning System. The role of autonomous onboard navigation in contact exploration of Neptune's and Uranus's satellites as well as in such extreme-high-velocity encounters as those with retrograde comets is investigated. The Automated Optical Navigation program is described

Jordan, J. F.↗

NASA NTRS1973

Digital processing of stereoscopic image pairs.

The problem under consideration is concerned with scene analysis during robot navigation on the surface of Mars. In this mode, the world model of the robot must be continuously updated to include sightings of new obstacles and scientific samples. In order to describe the content of a particular scene, it is first necessary to segment it into known objects. One technique for accomplishing this segmentation is by analyzing the pair of images produced by the stereoscopic cameras mounted on the robot. A heuristic method is presented for determining the range for each point in the two-dimensional scene under consideration. The method is conceptually based on a comparison of corresponding points in the left and right images of the stereo pair. However, various heuristics which are adaptive in nature are used to make the algorithm both efficient and accurate. Examples are given of the use of this so-called range picture for the purpose of scene segmentation.

Levine, M. D.↗

NASA NTRS1981

Deep space navigation systems and operations

The history of the deep space navigation system developed by NASA is outlined. Its application to Mariner, Viking and Pioneer missions is reviewed. Voyager navigation results for Jupiter and Saturn are commented on and velocity correction in relation to fuel expenditure and computer time are discussed. The navigation requirements of the Gahleo and Venus orbiting imaging radar (VOIR) missions are assessed. The measurement and data processing systems are described.

Jordan, J. F.↗

NASA NTRS1999

STS-96 FD Highlights and Crew Activities Report: Flight Day 09

On this ninth day of the STS-96 Discovery mission, the flight crew, Commander Kent V. Rominger, Pilot Rick D. Husband, and Mission Specialists Ellen Ochoa, Tamara E. Jernigan, Daniel T. Barry, Julie Payette, and Valery Ivanovich Tokarev are seen as they prepare to depart from the International Space Station. After the undocking of the spacecraft, Husband navigated the spacecraft around the International Space Station. Images of the crew removing centerline cameras, tracking the solar arrays and beautiful panoramic views of the Station above the Earth are seen.

Source record↗

NASA NTRS2002

Bioinspired engineering of exploration systems for NASA and DoD

A new approach called bioinspired engineering of exploration systems (BEES) and its value for solving pressing NASA and DoD needs are described. Insects (for example honeybees and dragonflies) cope remarkably well with their world, despite possessing a brain containing less than 0.01% as many neurons as the human brain. Although most insects have immobile eyes with fixed focus optics and lack stereo vision, they use a number of ingenious, computationally simple strategies for perceiving their world in three dimensions and navigating successfully within it. We are distilling selected insect-inspired strategies to obtain novel solutions for navigation, hazard avoidance, altitude hold, stable flight, terrain following, and gentle deployment of payload. Such functionality provides potential solutions for future autonomous robotic space and planetary explorers. A BEES approach to developing lightweight low-power autonomous flight systems should be useful for flight control of such biomorphic flyers for both NASA and DoD needs. Recent biological studies of mammalian retinas confirm that representations of multiple features of the visual world are systematically parsed and processed in parallel. Features are mapped to a stack of cellular strata within the retina. Each of these representations can be efficiently modeled in semiconductor cellular nonlinear network (CNN) chips. We describe recent breakthroughs in exploring the feasibility of the unique blending of insect strategies of navigation with mammalian visual search, pattern recognition, and image understanding into hybrid biomorphic flyers for future planetary and terrestrial applications. We describe a few future mission scenarios for Mars exploration, uniquely enabled by these newly developed biomorphic flyers.

Space Flight↗

NASA NTRS2010

Crater Identification Algorithm for the Lost in Low Lunar Orbit Scenario

Recent emphasis by NASA on returning astronauts to the Moon has placed attention on the subject of lunar surface feature tracking. Although many algorithms have been proposed for lunar surface feature tracking navigation, much less attention has been paid to the issue of navigational state initialization from lunar craters in a lost in low lunar orbit (LLO) scenario. That is, a scenario in which lunar surface feature tracking must begin, but current navigation state knowledge is either unavailable or too poor to initiate a tracking algorithm. The situation is analogous to the lost in space scenario for star trackers. A new crater identification algorithm is developed herein that allows for navigation state initialization from as few as one image of the lunar surface with no a priori state knowledge. The algorithm takes as inputs the locations and diameters of craters that have been detected in an image, and uses the information to match the craters to entries in the USGS lunar crater catalog via non-dimensional crater triangle parameters. Due to the large number of uncataloged craters that exist on the lunar surface, a probability-based check was developed to reject false identifications. The algorithm was tested on craters detected in four revolutions of Apollo 16 LLO images, and shown to perform well.

Hanak, Chad↗

NASA NTRS2016

Characterization of Stereo Vision Performance for Roving at the Lunar Poles

Surface rover operations at the polar regions of airless bodies, particularly the Moon, are of particular interest to future NASA science missions such as Resource Prospector (RP). Polar optical conditions present challenges to conventional imaging techniques, with repercussions to driving, safeguarding and science. High dynamic range, long cast shadows, opposition and white out conditions are all significant factors in appearance. RP is currently undertaking an effort to characterize stereo vision performance in polar conditions through physical laboratory experimentation with regolith simulants, obstacle distributions and oblique lighting.

Sensor Characterization↗

NASA NTRSDate not supplied

The Comet Astrobiology Exploration Sample Return (CAESAR) Mission

The Comet Astrobiology Exploration Sample Return (CAESAR) mission will acquire and return to Earth for laboratory analysis a minimum of 80 grams of surface material from the nucleus of comet 67P/Chur-yumov-Gerasimenko (67P). CAESAR will characterize the surface region sampled, preserve the collected sample in a pristine state, and return evolved volatiles by capturing them in a separate gas reservoir. NASA Goddard Space Flight Center provides project management, systems engineering, safety and mission assurance, contamination control, mission operations, and many other important functions. Northrop Grumman Space Systems will build the spacecraft, based on Dawn mission heritage, which like CAESAR, uses solar electric propulsion. CAESAR was selected by for Phase A study in the New Frontiers 4 Competition and will be proposed to New Frontiers 5.Collection of a sample from the surface of comet 67P is facilitated by a set of cameras that together provide images to support sample site selection, perform optical navigation, and document the sample before, during, and after col-lection. The sample is collected at the end of an arm during a 5-second touch-and-go (TAG) maneuver with the Sample Acquisition System (SAS)designed by Honeybee Robotics for the surface properties of comet 67P observed by the Rosetta mission. After sample collection, and while the sample is still cold (< -80°C), the TAG Arm inserts the sample container into the Sample Containment System (SCS) mounted inside the Sample Return Capsule (SRC). The SCS is sealed, preventing the sample from escaping into space. The sample is slowly warmed inside the SCS to enable sublimation of volatiles, which are collected in the Gas Containment System (GCS), a passively cooled gas reservoir. Separating the volatiles from the solid sample protects the solid sample from alteration. Once all sublimated H2O is transferred to the GCS, the GCS is sealed to capture the volatile sit contains, and the SCS is vented to space to maintain the solid sample under vacuum. The SCS vent is closed before Earth entry to prevent atmospheric contamination. Detailed laboratory analyses of the sample from 67P will trace the history of volatile reservoirs, delineate the chemical pathways that led from simple interstellar species to complex molecules, constrain the evolution of the comet, and evaluate the role of comets in delivering water and prebiotic organics to the early Earth. CAESAR will achieve these goals by carrying out coordinated sample analyses that will link macroscopic properties of the comet with microscale mineralogy, chemistry, and isotopic studies of volatiles and solids. Most of the sample (≥75%) will be set aside for analyses by generations of scientists using continually advancing tools and methods, yielding an enduring scientific treasure that only sample return can provide. This presentation will review development conducted during NF4 Phase A and discuss the NF5 mission concept.

A G Hayes↗

NASA NTRSDate not supplied

An Integrated Software Architecture for Solar Cruiser Mission Design and Navigation

Solar Cruiser is a solar sailing mission, riding as a secondary payload to the Interstellar Mapping and Acceleration Probe (IMAP) mission, slated to launch in February of 2025. The Solar Cruiser vehicle will generate thrust via a complex, low-thrust solar sail. The extreme low-thrust nature of the solar sail will leave Solar Cruiser highly sensitive to external environmental effects (such as solar radiation pressure and high-order gravitational perturbations) throughout the entirety of flight. Because of this, preliminary operational optimization routines must be intricately tied to high-order predictive propagation models to ensure the greatest possible confidence in mission success. The Solar Cruiser Mission Design and Navigation (MDNav) team has designed a software tool suite, employing the latest in software containerization technology, to accomplish this task; allowing for seamless development across several users and operating systems. Combining JPL’s Monte toolkit with high-performance optimizers written by the University of Alabama, the proposed architecture allows for instant verification of optimized trajectories within the same development environment that the optimization takes place, removing the need for mission designers and navigators to switch between tools. The MDNav suite image deployment process is separated from the development and operational scripts to be used in flight, which allows for maintaining a low-footprint version control profile without unnecessary file bloating. This paper discusses the historical differences between previous iterations of the Solar Cruiser MDNav tool suite and the current iteration, planned operational interfaces of the tool with other software and subsystems, and the planned path forward in maintaining containerization services for the software throughout the lifetime of Solar Cruiser.

optimization↗

NASA NTRSDate not supplied

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↗

NASA NTRS2012

Mars Science Laboratory Engineering Cameras

NASA's Mars Science Laboratory (MSL) Rover, which launched to Mars in 2011, is equipped with a set of 12 engineering cameras. These cameras are build-to-print copies of the Mars Exploration Rover (MER) cameras, which were sent to Mars in 2003. The engineering cameras weigh less than 300 grams each and use less than 3 W of power. Images returned from the engineering cameras are used to navigate the rover on the Martian surface, deploy the rover robotic arm, and ingest samples into the rover sample processing system. The navigation cameras (Navcams) are mounted to a pan/tilt mast and have a 45-degree square field of view (FOV) with a pixel scale of 0.82 mrad/pixel. The hazard avoidance cameras (Haz - cams) are body-mounted to the rover chassis in the front and rear of the vehicle and have a 124-degree square FOV with a pixel scale of 2.1 mrad/pixel. All of the cameras utilize a frame-transfer CCD (charge-coupled device) with a 1024x1024 imaging region and red/near IR bandpass filters centered at 650 nm. The MSL engineering cameras are grouped into two sets of six: one set of cameras is connected to rover computer A and the other set is connected to rover computer B. The MSL rover carries 8 Hazcams and 4 Navcams.

Maki, Justin N.↗

NASA NTRS1997

Neural Network Based Sensory Fusion for Landmark Detection

NASA is planning to send numerous unmanned planetary missions to explore the space. This requires autonomous robotic vehicles which can navigate in an unstructured, unknown, and uncertain environment. Landmark based navigation is a new area of research which differs from the traditional goal-oriented navigation, where a mobile robot starts from an initial point and reaches a destination in accordance with a pre-planned path. The landmark based navigation has the advantage of allowing the robot to find its way without communication with the mission control station and without exact knowledge of its coordinates. Current algorithms based on landmark navigation however pose several constraints. First, they require large memories to store the images. Second, the task of comparing the images using traditional methods is computationally intensive and consequently real-time implementation is difficult. The method proposed here consists of three stages, First stage utilizes a heuristic-based algorithm to identify significant objects. The second stage utilizes a neural network (NN) to efficiently classify images of the identified objects. The third stage combines distance information with the classification results of neural networks for efficient and intelligent navigation.

Kumbla, Kishan -K.↗