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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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NASA NTRS1998

A Geometry Based Infra-Structure for Computational Analysis and Design

The computational steps traditionally taken for most engineering analysis suites (computational fluid dynamics (CFD), structural analysis, heat transfer and etc.) are: (1) Surface Generation -- usually by employing a Computer Assisted Design (CAD) system; (2) Grid Generation -- preparing the volume for the simulation; (3) Flow Solver -- producing the results at the specified operational point; (4) Post-processing Visualization -- interactively attempting to understand the results. For structural analysis, integrated systems can be obtained from a number of commercial vendors. These vendors couple directly to a number of CAD systems and are executed from within the CAD Graphical User Interface (GUI). It should be noted that the structural analysis problem is more tractable than CFD; there are fewer mesh topologies used and the grids are not as fine (this problem space does not have the length scaling issues of fluids). For CFD, these steps have worked well in the past for simple steady-state simulations at the expense of much user interaction. The data was transmitted between phases via files. In most cases, the output from a CAD system could go to Initial Graphics Exchange Specification (IGES) or Standard Exchange Program (STEP) files. The output from Grid Generators and Solvers do not really have standards though there are a couple of file formats that can be used for a subset of the gridding (i.e. PLOT3D data formats). The user would have to patch up the data or translate from one format to another to move to the next step. Sometimes this could take days. Specifically the problems with this procedure are:(1) File based -- Information flows from one step to the next via data files with formats specified for that procedure. File standards, when they exist, are wholly inadequate. For example, geometry from CAD systems (transmitted via IGES files) is defined as disjoint surfaces and curves (as well as masses of other information of no interest for the Grid Generator). This is particularly onerous for modern CAD systems based on solid modeling. The part was a proper solid and in the translation to IGES has lost this important characteristic. STEP is another standard for CAD data that exists and supports the concept of a solid. The problem with STEP is that a solid modeling geometry kernel is required to query and manipulate the data within this type of file. (2) 'Good' Geometry. A bottleneck in getting results from a solver is the construction of proper geometry to be fed to the grid generator. With 'good' geometry a grid can be constructed in tens of minutes (even with a complex configuration) using unstructured techniques. Adroit multi-block methods are not far behind. This means that a million node steady-state solution can be computed on the order of hours (using current high performance computers) starting from this 'good' geometry. Unfortunately, the geometry usually transmitted from the CAD system is not 'good' in the grid generator sense. The grid generator needs smooth closed solid geometry. It can take a week (or more) of interaction with the CAD output (sometimes by hand) before the process can begin. One way Communication. (3) One-way Communication -- All information travels on from one phase to the next. This makes procedures like node adaptation difficult when attempting to add or move nodes that sit on bounding surfaces (when the actual surface data has been lost after the grid generation phase). Until this process can be automated, more complex problems such as multi-disciplinary analysis or using the above procedure for design becomes prohibitive. There is also no way to easily deal with this system in a modular manner. One can only replace the grid generator, for example, if the software reads and writes the same files. Instead of the serial approach to analysis as described above, CAPRI takes a geometry centric approach. This makes the actual geometry (not a discretized version) accessible to all phases of the analysis. The connection to the geometry is made through an Application Programming Interface (API) and NOT a file system. This API isolates the top-level applications (grid generators, solvers and visualization components) from the geometry engine. Also this allows the replacement of one geometry kernel with another, without effecting these top-level applications. For example, if UniGraphics is used as the CAD package then Parasolid (UG's own geometry engine) can be used for all geometric queries so that no solid geometry information is lost in a translation. This is much better than STEP because when the data is queried, the same software is executed as used in the CAD system. Therefore, one analyzes the exact part that is in the CAD system. CAPRI uses the same idea as the commercial structural analysis codes but does not specify control. Software components of the CAD system are used, but the analysis suite, not the CAD operator, specifies the control of the software session. This also means that the license issues (may be) minimized and individuals need not have to know how to operate a CAD system in order to run the suite.

Haimes, Robert↗
NASA NTRS2017

Imaging Systems for Size Measurements of Debrisat Fragments

The overall objective of the DebriSat project is to provide data to update existing standard spacecraft breakup models. One of the key sets of parameters used in these models is the physical dimensions of the fragments (i.e., length, average-cross sectional area, and volume). For the DebriSat project, only fragments with at least one dimension greater than 2 mm are collected and processed. Additionally, a significant portion of the fragments recovered from the impact test are needle-like and/or flat plate-like fragments where their heights are almost negligible in comparison to their other dimensions. As a result, two fragment size categories were defined: 2D objects and 3D objects. While measurement systems are commercially available, factors such as measurement rates, system adaptability, size characterization limitations and equipment costs presented significant challenges to the project and a decision was made to develop our own size characterization systems. The size characterization systems consist of two automated image systems, one referred to as the 3D imaging system and the other as the 2D imaging system. Which imaging system to use depends on the classification of the fragment being measured. Both imaging systems utilize point-and-shoot cameras for object image acquisition and create representative point clouds of the fragments. The 3D imaging system utilizes a space-carving algorithm to generate a 3D point cloud, while the 2D imaging system utilizes an edge detection algorithm to generate a 2D point cloud. From the point clouds, the three largest orthogonal dimensions are determined using a convex hull algorithm. For 3D objects, in addition to the three largest orthogonal dimensions, the volume is computed via an alpha-shape algorithm applied to the point clouds. The average cross-sectional area is also computed for 3D objects. Both imaging systems have automated size measurements (image acquisition and image processing) driven by the need to quickly and accurately measure tens of thousands of debris fragments. Moreover, the automated size measurement reduces potential fragment damage/mishandling and ability for accuracy and repeatability. As the fragment characterization progressed, it became evident that the imaging systems had to be revised. For example, an additional view was added to the 2D imaging system to capture the height of the 2D object. This paper presents the DebriSat project's imaging systems and calculation techniques in detail; from design and development to maturation. The experiences and challenges are also shared.

Shiotani, B.↗
NASA NTRSDate not supplied

Autonomous Ocean World Exploration: Advancement of a Virtual Testbed

The search for life (extinct or extant) and potentially habitable bodies in our solar system and beyond is one of the 12 priority science questions outlined in the National Acadamies’ 2022 decadal survey [5]. Extraterrestrial destinations containing liquid water present an opportunity to search for life as we know it, and in recent years an increasing number of such locations have been discovered within our solar system. Several Jovian moons—Europa, Ganymede, and Callisto [10]—and the Saturnian moons Enceladus [8] and Titan [9] are known or suspected to harbor massive subsurface oceans. Of these "ocean worlds", Europa is the focus of at least one planned NASA orbiter mission, Europa Clipper [4], and an early lander mission concept, the Europa Lander [2, 3]. Whereas most robotic missions to the Moon and Mars (e.g. orbiters, rovers, landers) to date have had ground controllers on Earth tightly involved in mission operations, missions to more distant worlds will require a high degree of onboard autonomy due to long communication lags and blackouts, harsh environments (radiation, cold), and more limited battery and hardware life. The past decade has seen great advances in both AI technologies and computing scalability and performance that offer promising solutions for spacecraft autonomy and motivate the software system and research programs described in this paper. The Ocean Worlds Autonomy Testbed for Exploration, Research, and Simulation (OceanWATERS) [1], which has been in development at the NASA Ames Research Center since 2018, is a virtual environment for testing lander autonomy solutions. It is built on the Robot Operating System (ROS), runs on consumer-grade Linux workstations, and was released as open source in 2020. OceanWATERS provides a physical and visual simulation of a prototypical lander in a Europa-like environment (Figure 1). The lander was modeled after requirements and specifications made in JPL’s Europa Lander Study of 2016 [3]. Simulated lander systems include stereo cameras and spotlights mounted on an antenna mast that pans and tilts, a 6 degrees of freedom (DoF) robotic arm with a force-torque sensor and two interchangeable end effectors, and a battery pack power system. The environment consists of multiple terrain models including a highly detailed model sourced from the FROST dataset [11], simulation of surrounding planetary bodies based on an ephemeris model, and lighting from the sun with associated surface illumination, reflectance, and shadows. Operations supported by OceanWATERS include panoramic and directed imaging of the environment and lander workspace, Cartesian and joint-level arm commanding, grinding of the terrain surface (e.g. digging a trench), and scooping of ground material (Figure 2) which can be discarded or collected as science samples in a receptacle that can be emptied (science operations themselves are not simulated). These operations are realized as ROS Actions and are complimented by a wide selection of telemetry that is continually produced by each lander subsystem. The power system model is driven by the open-source Generic Software Architecture for Prognostics (GSAP) [11] that predicts the battery’s remaining useful life and other characteristics. As a testbed for high-level autonomy, OceanWATERS provides an execution framework based on PLEXIL [12], an open-source plan specification language and execution engine developed largely at Ames. NASA's initial development of OceanWATERS, as well the Ocean Worlds Lander Autonomy Testbed (OWLAT) [6], a complimentary physical testbed developed at JPL, was the first step in a plan for realizing candidate onboard autonomy solutions for such planetary landers. In 2020 NASA solicited applications for its Autonomous Robotics Research for Ocean Worlds (ARROW) program, and in 2021 the similar Concepts for Ocean worlds Life Detection Technology (COLDTech) program. Collectively six research teams, based in universities and companies across the United States, were awarded grants to develop and demonstrate autonomy solutions on OceanWATERS and OWLAT. These 1–2-year projects have now finished or are nearing completion, and a wide variety of autonomy challenges in ocean world surface missions were addressed. Prototyped and demonstrated solutions have included autonomous discovery, response and adaptation to system faults and unexpected environmental events, world model synthesis through perception, plan synthesis using learned models, methods to optimize sample target selection and prioritize science data transmission, extension of PLEXIL for stochastic decision-making, and an integration of a model of JPL’s mission-ready COLDArm [7]. Technologies used in these projects include many forms of machine learning, causal reasoning, automated planning, Markov decision processes, formal methods, and other advanced techniques. A more detailed summary of the ARROW and COLDTech projects is given herein. OceanWATERS has had significant enhancements since its open-source release in 2020. Many of its new features were driven or shaped by feedback from the ARROW and COLDTech teams and requirements of their projects. In support of enabling autonomous adaptation to spacecraft faults (a specific capability solicited by both programs), a fault injection and detection framework was developed that supports a wide and growing range of fault types such as locked joints, image loss, and battery failures. The power system model was completed and integrated into the simulator, starting as a single-cell battery model and later upgraded to a multi-cell model with associated faults such as cell disconnection. Arm/terrain interaction was improved by adding a force-torque sensor and associated faults, and an analytic dig force model based on the Balovnev bucket force equations. Environment fidelity was increased by modeling terrain deformation resulting from digging and scooping; visual improvements were made in textures, lighting, and shadows. To facilitate interoperation with OWLAT, a unified command and telemetry interface between the testbeds was developed at the ROS level, along with a PLEXIL interface. The number of lander operations was greatly expanded (e.g. with Cartesian-based arm and antenna movement), and a framework was designed for users to build their own lander actions. A GUI for PLEXIL plan selection was created (Figure 3), and an expansive set of plans were added, such as those that illustrate patterns for fault handling. This paper provides a self-contained high-level description of OceanWATERS, focusing on more detailed coverage of the aforementioned enhancements. It provides a high-level summary of the projects undertaken by participants in the ARROW and COLDTech programs and how these efforts have helped shape OceanWATERS. Finally, potential future work and directions for the testbed are listed, as likely informed by the recent planetary science decadal survey [5].

K Michael Dalal↗
NASA NTRS1994

Automated revision of CLIPS rule-bases

This paper describes CLIPS-R, a theory revision system for the revision of CLIPS rule-bases. CLIPS-R may be used for a variety of knowledge-base revision tasks, such as refining a prototype system, adapting an existing system to slightly different operating conditions, or improving an operational system that makes occasional errors. We present a description of how CLIPS-R revises rule-bases, and an evaluation of the system on three rule-bases.

Murphy, Patrick M.↗
NASA NTRS1999

In-Flight Technique for Acquiring Mid- And Far-Field Sonic Boom Signatures

Flight test experiments have been conducted to establish the feasibility of obtaining sonic boom signature measurements below a supersonic aircraft using the NASA Portable Automatic Triggering System (PATS) mounted in the USMC Pioneer Unmanned Aerial Vehicle (UAV). This study forms a part of the NASA sonic boom minimization activities, specifically the demonstration of persistence of modified boom signatures to very large distances in a real atmosphere. The basic objective of the measurement effort was to obtain a qualitative view of the sonic boom signature in terms of its shape, number of shocks, their locations, and their relative strength. Results suggest that the technique may very well provide quantitative information relative to mid-field and far-field boom signatures. The purpose of this presentation is to describe the arrangement and operation of this in-flight system and to present the resulting sonic boom measurements. Adaption and modification of two PATS to the UAV payload section are described and include transducer location, mounting arrangement and recording system isolation. Ground static runup, takeoff and landing, and cruise flight checkouts regarding UAV propeller and flow noise on the PATS automated triggering system and recording mode are discussed. For the proof-of-concept tests, the PATS instrumented UAV was flown under radar control in steady-level flight at the altitude of 8700 feet MSL and at a cruise speed of about 60 knots. The USN F-4N sonic boom generating aircraft was vectored over the UAV on reciprocal headings at altitudes of about 1 1,000 feet MSL and 13,000 feet MSL at about Mach 1. 15. Sonic boom signatures were acquired on both PATS for all six supersonic passes. Although the UAV propeller noise is clearly evident in all the measurements, the F-4 boom signature is clearly distinguishable and is typically N-wave in character with sharply rising shock fronts and with a mid-shock associated with the inlet-wing juncture. Consideration is being given to adapting the PATS/TJAV measurements technique to the NASA Learjet to determine feasibility of acquiring in-flight boom signatures in the altitude range of 10,000 feet to 40,000 feet.

Stansbery, Eugene G.↗
NASA NTRS1986

Architectures and algorithms for digital image processing; Proceedings of the Meeting, Cannes, France, December 5, 6, 1985

The conference presents papers on the architectures, algorithms, and applications of image processing. Particular attention is given to a very large scale integration system for image reconstruction from projections, a prebuffer algorithm for instant display of volume data, and an adaptive image sequence filtering scheme based on motion detection. Papers are also presented on a simple, direct practical method of sensing local motion and analyzing local optical flow, image matching techniques, and an automated biological dosimetry system.

Duff, Michael J. B.↗
NASA NTRS2001

ICEG2D (v2.0) - An Integrated Software Package for Automated Prediction of Flow Fields for Single-Element Airfoils With Ice Accretion

An integrated geometry/grid/simulation software package, ICEG2D, is being developed to automate computational fluid dynamics (CFD) simulations for single- and multi-element airfoils with ice accretions. The current version, ICEG213 (v2.0), was designed to automatically perform four primary functions: (1) generate a grid-ready surface definition based on the geometrical characteristics of the iced airfoil surface, (2) generate high-quality structured and generalized grids starting from a defined surface definition, (3) generate the input and restart files needed to run the structured grid CFD solver NPARC or the generalized grid CFD solver HYBFL2D, and (4) using the flow solutions, generate solution-adaptive grids. ICEG2D (v2.0) can be operated in either a batch mode using a script file or in an interactive mode by entering directives from a command line within a Unix shell. This report summarizes activities completed in the first two years of a three-year research and development program to address automation issues related to CFD simulations for airfoils with ice accretions. As well as describing the technology employed in the software, this document serves as a users manual providing installation and operating instructions. An evaluation of the software is also presented.

Thompson David S.↗
NASA NTRS1994

Paint Analysis

Lewis Research Center (LEW) has assisted The Cleveland Museum of Art (CMA) in analyzing the museum's paintings. Because of the many layers of paint that are often involved, this is a complex process. The cross-section of a paint chip must be scanned with a microscope to determine whether a paint layer is original or a restoration. The paint samples, however, are rarely flat enough for high magnification viewing and are frequently scratched. LEW devised an automated method that produces intact, flat, polished paint cross-sections. A sophisticated microprocessor-controlled grinding and polishing machine was manually employed in preparation of exotic samples for aerospace research was a readily adaptable technique. It produced perfectly flat samples with clearly defined layers. The process has been used successfully on a number of paintings, and LEW and CMA are considering additional applications.

Source record↗
NASA NTRS1973

Automated attendance accounting system

An automated accounting system useful for applying data to a computer from any or all of a multiplicity of data terminals is disclosed. The system essentially includes a preselected number of data terminals which are each adapted to convert data words of decimal form to another form, i.e., binary, usable with the computer. Each data terminal may take the form of a keyboard unit having a number of depressable buttons or switches corresponding to selected data digits and/or function digits. A bank of data buffers, one of which is associated with each data terminal, is provided as a temporary storage. Data from the terminals is applied to the data buffers on a digit by digit basis for transfer via a multiplexer to the computer.

Chapman, C. P.↗
NASA NTRS2024

A Multi‐Probe Automated Classification of Ice Crystal Habits During the IMPACTS Campaign

Although all ice crystals are unique, many can be grouped together by shape or habit, with members of a habit class sharing similar representations of properties such as fall velocity and growth rate. A decision tree algorithm designed to be adaptable to any particle imaging probe, thus enabling the creation of habit size distributions over a size range larger than that of any probe on its own, is used to classify ice crystals imaged by three airborne cloud probes in mid-latitude winter cyclones during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. Crystals are sorted into seven habit classes based on their morphological properties: sphere, column/needle, plate, graupel, dendrite, aggregate, and irregular. Although adaptability was its primary goal, the algorithm was found to be moderately skillful for identifying idealized habit images. Quantitative tests of the algorithm’s adaptability displayed mixed results, as Two-Dimensional Stereo Probe (2DS) classifications showed moderate correlation with Particle Habit Imaging and Polar Scattering Probe (PHIPS) classifications, but only weak correlation with High Volume Precipitation Spectrometer (HVPS) classifications. The algorithm was applied to random sets of images from each probe in a case study of a mesoscale snow band sampled on 7 February 2020. In the case study, qualitative analysis of particle images revealed general agreement on classifications among the probes, supporting the algorithm’s applicability to multiple cloud probes. Most classifications appeared correct upon manual inspection, suggesting that in practical use, the algorithm is reasonably able to classify non-idealized images.

Julian Schima↗
NASA NTRS1985

A.I.-based real-time support for high performance aircraft operations

Artificial intelligence (AI) based software and hardware concepts are applied to the handling system malfunctions during flight tests. A representation of malfunction procedure logic using Boolean normal forms are presented. The representation facilitates the automation of malfunction procedures and provides easy testing for the embedded rules. It also forms a potential basis for a parallel implementation in logic hardware. The extraction of logic control rules, from dynamic simulation and their adaptive revision after partial failure are examined. It uses a simplified 2-dimensional aircraft model with a controller that adaptively extracts control rules for directional thrust that satisfies a navigational goal without exceeding pre-established position and velocity limits. Failure recovery (rule adjusting) is examined after partial actuator failure. While this experiment was performed with primitive aircraft and mission models, it illustrates an important paradigm and provided complexity extrapolations for the proposed extraction of expertise from simulation, as discussed. The use of relaxation and inexact reasoning in expert systems was also investigated.

Vidal, J. J.↗
NASA NTRS1994

Galileo mission planning for Low Gain Antenna based operations

The Galileo mission operations concept is undergoing substantial redesign, necessitated by the deployment failure of the High Gain Antenna, while the spacecraft is on its way to Jupiter. The new design applies state-of-the-art technology and processes to increase the telemetry rate available through the Low Gain Antenna and to increase the information density of the telemetry. This paper describes the mission planning process being developed as part of this redesign. Principal topics include a brief description of the new mission concept and anticipated science return (these have been covered more extensively in earlier papers), identification of key drivers on the mission planning process, a description of the process and its implementation schedule, a discussion of the application of automated mission planning tool to the process, and a status report on mission planning work to date. Galileo enhancements include extensive reprogramming of on-board computers and substantial hard ware and software upgrades for the Deep Space Network (DSN). The principal mode of operation will be onboard recording of science data followed by extended playback periods. A variety of techniques will be used to compress and edit the data both before recording and during playback. A highly-compressed real-time science data stream will also be important. The telemetry rate will be increased using advanced coding techniques and advanced receivers. Galileo mission planning for orbital operations now involves partitioning of several scarce resources. Particularly difficult are division of the telemetry among the many users (eleven instruments, radio science, engineering monitoring, and navigation) and allocation of space on the tape recorder at each of the ten satellite encounters. The planning process is complicated by uncertainty in forecast performance of the DSN modifications and the non-deterministic nature of the new data compression schemes. Key mission planning steps include quantifying resource or capabilities to be allocated, prioritizing science observations and estimating resource needs for each, working inter-and intra-orbit trades of these resources among the Project elements, and planning real-time science activity. The first major mission planning activity, a high level, orbit-by-orbit allocation of resources among science objectives, has already been completed; and results are illustrated in the paper. To make efficient use of limited resources, Galileo mission planning will rely on automated mission planning tools capable of dealing with interactions among time-varying downlink capability, real-time science and engineering data transmission, and playback of recorded data. A new generic mission planning tool is being adapted for this purpose.

Gershman, R.↗
NASA NTRS1995

Automatic releveling of calibration stands for wind tunnel force and moment measurement instrumentation

Wind tunnel force and moment measuring instrumentation at NASA LaRC are calibrated by applying NIST certified dead loads, to generate forces and moments in three orthogonal axes, in twenty five percent increments, up to full-load and down to no-load in a nine point continuous procedure, using specially built calibration stands. The measuring instruments deflect upon the application of loads. These deflections must be cancelled to minimize loading interactions. The older calibration stands, integrated with AC induction motors, or DC motors, have provisions for manually releveling the loading point after each load. A newer stand, with integrated DC stepper motors, relevels automatically under software control. It was proposed to extend automation to the old stands. An algorithm to control the unwieldy induction motors to relevel a calibration stand, to within two arcseconds of horizontal direction automatically, was developed. The system developed uses coasting prediction and load adaptation in a general type robust stability control configuration with noncoincidental feedback. Automatic releveling cuts the releveling time to approximately half of the time required to do it manually. The principal constraint for shorter times is the time constant of the angular position feedback transducer.

Perazza, Manuel Rodriguez↗
NASA NTRS1987

Attention theory and training research

This study used elements of attention theory as a methodological basis to decompose a complex training task in order to improve training efficiency. The complex task was a microcomputer flight simulation where subjects were required to control the stability of their own helicopter while acquiring and engaging enemy helicopers in a threat enviroment. Subjects were divided into whole-task, part-task, and part/open loop adaptive task groups in a transfer of training paradigm. The effect of reducing mental workload at the early stages of learning was examined with respect to the degree that subordinate elements of the complex task could be automated through practice of consistent, learnable stimulus-response relationships. Results revealed trends suggesting the benefit of isolating consistently mapped sub-tasks for part-task training and the presence of a time-sharing skill over and above the skill required for the separate subtasks.

Connelly, James G., Jr.↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.

space biology↗
NASA NTRS2006

Enhancing Science and Automating Operations using Onboard Autonomy

In this paper, we will describe the evolution of the software from prototype to full time operation onboard Earth Observing One (EO-1). We will quantify the increase in science, decrease in operations cost, and streamlining of operations procedures. Included will be a description of how this software was adapted post-launch to the EO-1 mission, which had very limited computing resources which constrained the autonomy flight software. We will discuss ongoing deployments of this software to the Mars Exploration Rovers and Mars Odyssey Missions as well as a discussion of lessons learned during this project. Finally, we will discuss how the onboard autonomy has been used in conjunction with other satellites and ground sensors to form an autonomous sensor-web to study volcanoes, floods, sea-ice topography, and wild fires. As demonstrated on EO-1, onboard autonomy is a revolutionary advance that will change the operations approach on future NASA missions...

Earth Observing One (EO-1)↗