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DOE OSTI2025

Aerial and Processed Model Data Representing As-built Conditions in Coastal Port Arthur, Texas in May 2025

This dataset was collected by the Co-Design Team of the Southeast Texas Urban Integrated Field Lab, a research initiative led by the University of Texas at Austin and funded by the U.S. Department of Energy. The broader project focuses on developing climate-resilient design solutions for the Beaumont–Port Arthur region, with more information available at www.setx-uifl.org. Our team conducted aerial surveys of the Port Arthur coastal neighborhood in May 2025, before the start of construction scheduled for Summer 2026. These pre-construction datasets are designed to facilitate comparative analyses, including pre- and post-construction assessments and simulated inundation scenario evaluations. Aerial images were captured using DroneDeploy autonomous flight systems, with imagery processed through the DroneDeploy engine. All original aerial photographs are provided in JPG format and organized in zipped folders by area. The processed data package includes: 3D surface models Orthomosaics Geospatial and topographic mappings Point clouds For guidance on file contents, structure, and recommended usage, please refer to the included README file.

2D mapping↗

NASA NTRS2024

Distributed Sensor Fusion of Ground and Air Nodes Using Vision and Radar Modalities for Tracking Multirotor Small Uncrewed Air Systems and Birds

High-density airspace operations with multiple aircraft type and potentially noncooperative aircraft require distributed sensor detection and tracking systems to monitor airspace for safe, autonomous flight operations for advanced air mobility, urban air mobility, and high density small uncrewed air systems (SUAS) flight concepts. This work collected data using a distributed radar and camera sensor framework during the NASA Advanced Air Mobility High Density Vertiplex project SUAS flight operation. Node locations include on an onboard SUAS, affixed to the Landing and Impact Research Facility with approximate 200ft elevation, on a tripod on first-floor roof with an approximate elevation of 20 ft, and on a tripod on a concrete pad that is approximately 4 ft above sea level. Each node includes camera, radar, and GPS.

Beyond Visual Line of Sight↗

NASA NTRS2021

FRESCO: A Framework for Spacecraft Systems Autonomy

Achieving the science exploration and defense goals of the following decades will require flight systems capable of operations with limited operator contact, system mode changes and retasking based on sensor data, and complex robotic operations. To support these capabilities, increasingly autonomous flight systems are required that can perform dedicated mission functions, e.g. payload targeting and communications, and system-level functions, e.g. planning and goal monitoring. Architecting an autonomous system requires a well-reasoned, self-consistent framework to avoid \textit{ad hoc} design choices that will introduce complexity and risk. The Framework for Robust Execution and Scheduling of Commands On-Board, FRESCO, is the result of lessons learned in developing a software architecture to enable autonomous solar system exploration. FRESCO generalizes this work to offer a modular, software-agnostic approach to developing verifiable architecture for autonomous space systems. FRESCO specifies guiding principles, functions, interfaces, and interactions from which mission-specific autonomous control architectures can be derived. FRESCO is a principled framework relying on explicit, state-based goal definitions, centralized management of state knowledge, clearly separated control boundaries, and hierarchical reasoning. Using components from FRESCO reference architecture, an autonomous decision-making architecture can be designed for spacecraft which can then be mapped to flight software architecture. FRESCO is flexibly defined to enable autonomous control of flight systems built using extensive software and hardware heritage. Finally, FRESCO-derived architectures support a spectrum of operator/spacecraft interactions, ranging from traditional commanding to goal-driven commanding with the ability to change mission goals autonomously. FRESCO has been used in defining the autonomy architectures for the ASTERIA mission and have been demonstrated in laboratory and software simulation for small body rendezvous and in-space servicing missions.

Kolcio, Ksenia↗

NASA NTRS2006

Simulation to Flight Test for a UAV Controls Testbed

The NASA Flying Controls Testbed (FLiC) is a relatively small and inexpensive unmanned aerial vehicle developed specifically to test highly experimental flight control approaches. The most recent version of the FLiC is configured with 16 independent aileron segments, supports the implementation of C-coded experimental controllers, and is capable of fully autonomous flight from takeoff roll to landing, including flight test maneuvers. The test vehicle is basically a modified Army target drone, AN/FQM-117B, developed as part of a collaboration between the Aviation Applied Technology Directorate (AATD) at Fort Eustis, Virginia and NASA Langley Research Center. Several vehicles have been constructed and collectively have flown over 600 successful test flights, including a fully autonomous demonstration at the Association of Unmanned Vehicle Systems International (AUVSI) UAV Demo 2005. Simulations based on wind tunnel data are being used to further develop advanced controllers for implementation and flight test.

Motter, Mark A.↗

NASA NTRS1999

Bantam: A Systematic Approach to Reusable Launch Vehicle Technology Development

The Bantam technology project is focused on providing a low cost launch capability for very small (100 kilogram) NASA and University science payloads. The cost goal has been set at one million dollars per launch. The Bantam project, however, represents much more than a small payload launch capability. Bantam represents a unique, systematic approach to reusable launch vehicle technology development. This technology maturation approach will enable future highly reusable launch concepts in any payload class. These launch vehicle concepts of the future could deliver payloads for hundreds of dollars per pound, enabling dramatic growth in civil and commercial space enterprise. The National Aeronautics and Space Administration (NASA) has demonstrated a better, faster, and cheaper approach to science discovery in recent years. This approach is exemplified by the successful Mars Exploration Program lead by the Jet Propulsion Laboratory (JPL) for the NASA Space Science Enterprise. The Bantam project represents an approach to space transportation technology maturation that is very similar to the Mars Exploration Program. The NASA Advanced Space Transportation Program (ASTP) and Future X Pathfinder Program will combine to systematically mature reusable space transportation technology from low technology readiness to system level flight demonstration. New reusable space transportation capability will be demonstrated at a small (Bantam) scale approximately every two years. Each flight demonstration will build on the knowledge derived from the previous flight tests. The Bantam scale flight demonstrations will begin with the flights of the X-34. The X-34 will demonstrate reusable launch vehicle technologies including; flight regimes up to Mach 8 and 250,000 feet, autonomous flight operations, all weather operations, twenty-five flights in one year with a surge capability of two flights in less than twenty-four hours and safe abort. The Bantam project will build on this initial capability to expand the capability of a reusable first stage, including ground launch, powered return to the launch site, and a fully reusable rocket propulsion system. A Bantam technology goal is to demonstrate twenty-five flights with no unplanned rocket engine maintenance and only minor planned maintenance or inspections. The design goal of the propulsion system is a mission life of one hundred.

Griner, Carolyn↗

NASA NTRS2006

Case Study: Test Results of a Tool and Method for In-Flight, Adaptive Control System Verification on a NASA F-15 Flight Research Aircraft

Adaptive control technologies that incorporate learning algorithms have been proposed to enable autonomous flight control and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments [1-2]. At the present time, however, it is unknown how adaptive algorithms can be routinely verified, validated, and certified for use in safety-critical applications. Rigorous methods for adaptive software verification end validation must be developed to ensure that. the control software functions as required and is highly safe and reliable. A large gap appears to exist between the point at which control system designers feel the verification process is complete, and when FAA certification officials agree it is complete. Certification of adaptive flight control software verification is complicated by the use of learning algorithms (e.g., neural networks) and degrees of system non-determinism. Of course, analytical efforts must be made in the verification process to place guarantees on learning algorithm stability, rate of convergence, and convergence accuracy. However, to satisfy FAA certification requirements, it must be demonstrated that the adaptive flight control system is also able to fail and still allow the aircraft to be flown safely or to land, while at the same time providing a means of crew notification of the (impending) failure. It was for this purpose that the NASA Ames Confidence Tool was developed [3]. This paper presents the Confidence Tool as a means of providing in-flight software assurance monitoring of an adaptive flight control system. The paper will present the data obtained from flight testing the tool on a specially modified F-15 aircraft designed to simulate loss of flight control faces.

Jacklin, Stephen A.↗

NASA NTRS2013

Development and Execution of Autonomous Procedures Onboard the International Space Station to Support the Next Phase of Human Space Exploration

Now that major assembly of the International Space Station (ISS) is complete, NASA's focus has turned to using this high fidelity in-space research testbed to not only advance fundamental science research, but also demonstrate and mature technologies and develop operational concepts that will enable future human exploration missions beyond low Earth orbit. The ISS as a Testbed for Analog Research (ISTAR) project was established to reduce risks for manned missions to exploration destinations by utilizing ISS as a high fidelity micro-g laboratory to demonstrate technologies, operations concepts, and techniques associated with crew autonomous operations. One of these focus areas is the development and execution of ISS Testbed for Analog Research (ISTAR) autonomous flight crew procedures intended to increase crew autonomy that will be required for long duration human exploration missions. Due to increasing communications delays and reduced logistics resupply, autonomous procedures are expected to help reduce crew reliance on the ground flight control team, increase crew performance, and enable the crew to become more subject-matter experts on both the exploration space vehicle systems and the scientific investigation operations that will be conducted on a long duration human space exploration mission. These tests make use of previous or ongoing projects tested in ground analogs such as Research and Technology Studies (RATS) and NASA Extreme Environment Mission Operations (NEEMO). Since the latter half of 2012, selected non-critical ISS systems crew procedures have been used to develop techniques for building ISTAR autonomous procedures, and ISS flight crews have successfully executed them without flight controller involvement. Although the main focus has been preparing for exploration, the ISS has been a beneficiary of this synergistic effort and is considering modifying additional standard ISS procedures that may increase crew efficiency, reduce operational costs, and raise the amount of crew time available for scientific research. The next phase of autonomous procedure development is expected to include payload science and human research investigations. Additionally, ISS International Partners have expressed interest in participating in this effort. The recently approved one-year crew expedition starting in 2015, consisting of one Russian and one U.S. Operating Segment (USOS) crewmember, will be used not only for long duration human research investigations but also for the testing of exploration operations concepts, including crew autonomy.

beyond low earth orbit (BLEO)↗

NASA NTRS2022

DataSet: Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated

Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this e that it is feasible to classify sensor collected trajectories using a classifier trained flight controller data.

Chester V Dolph↗

NASA NTRSDate not supplied

Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated Tracks

Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this work indicate that it is feasible to classify sensor collected trajectories using a classifier trained on flight controller data.

Henry Holbrook↗

NASA NTRS2003

Fuzzy Logic Trajectory Design and Guidance for Terminal Area Energy Management

The second generation reusable launch vehicle will leverage many new technologies to make flight to low earth orbit safer and more cost effective. One important capability will be completely autonomous flight during reentry and landing, thus making it unnecessary to man the vehicle for cargo missions with stringent weight constraints. Implementation of sophisticated new guidance and control methods will enable the vehicle to return to earth under less than favorable conditions. The return to earth consists of three phases--Entry, Terminal Area Energy Management (TAEM), and Approach and Landing. The Space Shuttle is programmed to fly all three phases of flight automatically, and under normal circumstances the astronaut-pilot takes manual control only during the Approach and Landing phase. The automatic control algorithms used in the Shuttle for TAEM and Approach and Landing have been developed over the past 30 years. They are computationally efficient, and based on careful study of the spacecraft's flight dynamics, and heuristic reasoning. The gliding return trajectory is planned prior to the mission, and only minor adjustments are made during flight for perturbations in the vehicle energy state. With the advent of the X-33 and X-34 technology demonstration vehicles, several authors investigated implementing advanced control methods to provide autonomous real-time design of gliding return trajectories thus enhancing the ability of the vehicle to adjust to unusual energy states. The bulk of work published to date deals primarily with the approach and landing phase of flight where changes in heading angle are small, and range to the runway is monotonically decreasing. These benign flight conditions allow for model simplification and fairly straightforward optimization. This project focuses on the TAEM phase of flight where mathematically precise methods have produced limited results. Fuzzy Logic methods are used to make onboard autonomous gliding return trajectory design robust to a wider energy envelope, and the possibility of control surface failures, thus increasing the flexibility of unmanned gliding recovery and landing.

Burchett, Bradley↗

NASA NTRSDate not supplied

Visual and Inertial Datasets for an eVTOL Aircraft Approach and Landing Scenario

A National Aeronautics and Space Administration (NASA) project developing computer vision algorithms for autonomous flight is producing real-world datasets with cameras mounted on aircraft. In related domains, such as autonomous driving, open datasets are key to innovation and advancement in computer vision and autonomous perception for future Advanced Air Mobility (AAM) operations. Few vision datasets, however, are publicly available in the aviation context. This paper introduces preliminary datasets containing several examples of approach and landing scenarios. The platform aircraft include a multirotor small unmanned aerial system (sUAS) and a crewed helicopter as surrogates for future electric vertical take-off and landing (eVTOL) aircraft. The dataset provides video imagery with associated inertial navigation system-global positioning system (INS-GPS) position and attitude estimates and other sensors. Surveyed locations of the visual features of the landing area are included. This dataset is the first to be released in an ongoing effort to collect and share large, diverse datasets relevant to autonomous aviation; community critique that can inform and improve future flight campaigns is welcome.

Nelson Brown↗

NASA NTRS1985

Vision requirements for Space Station applications

Problems which will be encountered by computer vision systems in Space Station operations are discussed, along with solutions be examined at Johnson Space Station. Lighting cannot be controlled in space, nor can the random presence of reflective surfaces. Task-oriented capabilities are to include docking to moving objects, identification of unexpected objects during autonomous flights to different orbits, and diagnoses of damage and repair requirements for autonomous Space Station inspection robots. The approaches being examined to provide these and other capabilities are television IR sensors, advanced pattern recognition programs feeding on data from laser probes, laser radar for robot eyesight and arrays of SMART sensors for automated location and tracking of target objects. Attention is also being given to liquid crystal light valves for optical processing of images for comparisons with on-board electronic libraries of images.

Crouse, K. R.↗

NASA NTRS2024

Presound: UAV Diagnostic System Enabled by Vibration-Based Machine Learning

A low-weight, inexpensive small unmanned aerial system (sUAS) that takes off, performs a mission, lands, and safely stows and recharges itself has myriad future applications ranging from agricultural imaging to last-mile package delivery. Likewise, Urban Air Mobility (UAM) systems will enable people to take air taxis from point to point in cities, rapidly moving commuters long distances without concern for road traffic and congestion. Fully electric aviation systems will be cleaner and quieter than ground transport. Cities could eliminate cars and buses, and convert roads to higher capacity bike and pedestrian throughways. Yet, for sUAS as well as UAM, system reliability and assurance is a limiting factor to deploying affordable autonomous flight systems. For this bright future of aviation to be realized, aircraft must be able to autonomously and accurately self-diagnose health issues both before takeoff and during flight. The GreenSight PreSound system is designed to identify defects on aircraft through intelligent analysis of vibration. It accomplishes this by measuring structural vibrations induced by the vehicle’s own propellers, and analyzing that data using a machine learning model that determines whether a defect is present. The PreSound system is designed to require no human oversight, and to operate across a wide array of vehicles through re-training of the model for each target aircraft. PreSound has been developed and seen limited early success using data collected from the GreenSight Dreamer sUAS, a 5lb quadrotor vehicle designed for aerial imaging applications. The final detection model, trained on data with props spinning at 50% throttle, achieves excellent performance with over 99% average accuracy in detecting blade damage using a single FFT vector input. It demonstrates the ability to generalize to new types of blade damage, correctly classifying a different type of blade damage with 98% accuracy. Full test pulses were classified with 100% accuracy, and in live testing, all sets of data during blade movement were classified accurately with over 95% confidence. When trained on in-flight data, the same model achieves an average accuracy of 85% in distinguishing between undamaged and blade-damaged states in flight. The authors believe that these accuracies show significant potential of this approach to expand unmanned flight safety, with significant potential benefits in accelerating Advanced Aerial Mobility (AAM) and UAM aviation applications.

UAS↗

NASA NTRS2000

Accelerating Innovation: Turning Goals into Reality

The success of NASA's programs depends upon innovation, which is recognized by several characteristics. All aspects of a program including tools, processes, materials, subsystems, vehicles, and operations should be evaluated to determine possible innovations which might be implemented. Several examples are presented of ways in which innovation has substantially furthered the goals of NASA. The specific fields mentioned include high performance computing, advanced technologies for aerospace system design, advanced materials and manufacturing processes, neural based flight control, linear aerospike engines, advanced space propulsion systems, high altitude and long duration autonomous flights, advanced vehicle concepts, advanced space propulsion systems, as well as advanced weather information. A final list details the perceived ways in which NASA can benefit from continued innovation in such ways as partnering with the private sector.

VanDalsem, William R.↗

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 NTRSDate not supplied

Flight Test Configuration of the Sensor Payload and the Ground Nodes in Distributed Sensing Frameworks

The realization of the Urban Air Mobility (UAM) vision, entailing the deployment of high-density autonomous flights over urban areas, necessitates methodologies transcending contemporary airborne sensing techniques. An instrumental innovation in this realm is the advent of distributed sensing. In this paradigm, environmental sensors function as active agents, engaging in the triad of sensing, processing, and communication either amongst themselves, with ground stations, or both. This concerted effort creates a dynamic and responsive "smart space," facilitating real-time autonomous control. NASA Ames Research Center is actively engaged in a series of comprehensive indoor and outdoor flight tests to garner requisite data and insights essential for the actualization of this ambitious vision. This paper delineates the holistic configuration of the flight payload and the distributed ground nodes pivotal to the NASA flight test campaign. Furthermore, it expounds upon the intricacies of the sensor node communication framework. The narrative extends to provide a detailed overview of the strategic placement of distributed sensors and a comprehensive account of the varied indoor and outdoor flight tests orchestrated in pursuit of UAM objectives.

urban air mobility↗

NASA NTRSDate not supplied

Flight Test Configuration of the Sensor Payload and the Ground Nodes in Distributed Sensing Frameworks

urban air mobility↗

NASA NTRS2004

Development and Evaluation of an Airborne Separation Assurance System for Autonomous Aircraft Operations

NASA Langley Research Center is developing an Autonomous Operations Planner (AOP) that functions as an Airborne Separation Assurance System for autonomous flight operations. This development effort supports NASA s Distributed Air-Ground Traffic Management (DAG-TM) operational concept, designed to significantly increase capacity of the national airspace system, while maintaining safety. Autonomous aircraft pilots use the AOP to maintain traffic separation from other autonomous aircraft and managed aircraft flying under today's Instrument Flight Rules, while maintaining traffic flow management constraints assigned by Air Traffic Service Providers. AOP is designed to facilitate eventual implementation through careful modeling of its operational environment, interfaces with other aircraft systems and data links, and conformance with established flight deck conventions and human factors guidelines. AOP uses currently available or anticipated data exchanged over modeled Arinc 429 data buses and an Automatic Dependent Surveillance Broadcast 1090 MHz link. It provides pilots with conflict detection, prevention, and resolution functions and works with the Flight Management System to maintain assigned traffic flow management constraints. The AOP design has been enhanced over the course of several experiments conducted at NASA Langley and is being prepared for an upcoming Joint Air/Ground Simulation with NASA Ames Research Center.

Barhydt, Richard↗