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NASA NTRS2015

Enhanced Flight Vision Systems Operational Feasibility Study Using Radar and Infrared Sensors

Approach and landing operations during periods of reduced visibility have plagued aircraft pilots since the beginning of aviation. Although techniques are currently available to mitigate some of the visibility conditions, these operations are still ultimately limited by the pilot's ability to "see" required visual landing references (e.g., markings and/or lights of threshold and touchdown zone) and require significant and costly ground infrastructure. Certified Enhanced Flight Vision Systems (EFVS) have shown promise to lift the obscuration veil. They allow the pilot to operate with enhanced vision, in lieu of natural vision, in the visual segment to enable equivalent visual operations (EVO). An aviation standards document was developed with industry and government consensus for using an EFVS for approach, landing, and rollout to a safe taxi speed in visibilities as low as 300 feet runway visual range (RVR). These new standards establish performance, integrity, availability, and safety requirements to operate in this regime without reliance on a pilot's or flight crew's natural vision by use of a fail-operational EFVS. A pilot-in-the-loop high-fidelity motion simulation study was conducted at NASA Langley Research Center to evaluate the operational feasibility, pilot workload, and pilot acceptability of conducting straight-in instrument approaches with published vertical guidance to landing, touchdown, and rollout to a safe taxi speed in visibility as low as 300 feet RVR by use of vision system technologies on a head-up display (HUD) without need or reliance on natural vision. Twelve crews flew various landing and departure scenarios in 1800, 1000, 700, and 300 RVR. This paper details the non-normal results of the study including objective and subjective measures of performance and acceptability. The study validated the operational feasibility of approach and departure operations and success was independent of visibility conditions. Failures were handled within the lateral confines of the runway for all conditions tested. The fail-operational concept with pilot in the loop needs further study.

Etherington, Timothy J.↗

NASA NTRSDate not supplied

A New Vision and A More Rational Process Will Advance Space Life Support Through Better Projects

Many space life support assumptions are obviously mistaken but they are generally accepted because they mutually support each other and because they advance a mistaken futuristic vision of closed human ecosystems in space. Many projects have been mistakenly selected to increase life support system closure or reduce launch mass in order to implement this impractical vision. Project members rarely challenge their project’s assumptions and guiding vision. Systems engineering including cost, reliability, risk, and system trade-offs has been very strongly discouraged to prevent rational criticism of these dubious assumptions, the projects, and the ecosystem vision. A more realistic vision can define better near term life support goals and future aspirations that will improve mission support and increase public interest. Given more realistic goals, it will be possible to develop effective technical alternatives, use open rational project selection methods, conduct professional project management, and make swifter progress in the inevitable human advance into space. The current unworkable human space settlement scenario involves moon visits and permanent bases, zero gravity trips to Mars, Mars visits and bases, all leading to extensive human settlements on the moon and Mars. This is unrealistic because the moon and Mars have less than Earth normal gravity and high radiation that can seriously damage human health. A better alternative would use rotating space colonies that produce Earth gravity and are radiation shielded. The attractive fantasy of reproducing terrestrial ecology in space should be replaced by the idea of building a closed system based on advancing technology such as artificial photosynthesis. Past mistaken projects include growing plants for food to replicate terrestrial food sources, recovering small amounts of water from difficult to process waste to achieve high system closure, and using newly designed, lighter, but unreliable hardware to reduce launch mass. The political process supporting the false assumptions, projects, and vision has deliberately prevented the usual systems engineering and cost-benefit analysis. We are going to do much better.

Harry W. Jones↗

DOE OSTI2022

A Combined Computer Vision and Deep Learning Approach for Rapid Drone-Based Optical Characterization of Parabolic Troughs

Optical accuracy is a primary driver of parabolic trough concentrating solar power (CSP) plant performance, but can be damaged by wind loads, gravity, error during installation, and regular plant operation. Collecting and analyzing optical measurements over an entire operating parabolic trough plant is difficult, given the large scale of typical installations. Distant Observer, a software tool developed at the National Renewable Energy Laboratory, uses images of the absorber tube reflected in the collector mirror to measure both surface slope in the parabolic mirror and offset of the absorber tube from the ideal focal point. This technology has been adapted for fast data collection using low-cost commercial drones, but until recently still required substantial human labor to process large amounts of data. A new method leveraging advanced deep learning and computer vision tools can drastically reduce the time required to process images. This new method addresses the primary analysis bottleneck, identifying featureless, reflective mirror corner points to a high degree of accuracy. Recent work has shown promising results using computer vision methods. The combined deep learning and computer vision approach presented here proved highly effective and has the potential to further automate data collection and analysis, making the tool more robust. The method presented in this paper automatically identified 74.3% of mirror corners within 2 pixels of their manually marked counterparts and 91.9% within 3 pixels. This level of accuracy is sufficient for practical Distant Observer analysis within a target uncertainty. A commercial drone collected video of over 100 parabolic trough modules at an operating CSP plant to demonstrate the deep learning and computer vision method's usefulness in processing large amounts of data. These troughs were successfully analyzed using Distant Observer, paired with the new deep learning and computer vision algorithm, and can provide plant operators and trough designers with valuable insight about plant performance, operating strategies, and plant-wide optical error trends.

computer vision↗

NASA NTRS2011

Autonomous Landing and Ingress of Micro-Air-Vehicles in Urban Environments Based on Monocular Vision

Unmanned micro air vehicles (MAVs) will play an important role in future reconnaissance and search and rescue applications. In order to conduct persistent surveillance and to conserve energy, MAVs need the ability to land, and they need the ability to enter (ingress) buildings and other structures to conduct reconnaissance. To be safe and practical under a wide range of environmental conditions, landing and ingress maneuvers must be autonomous, using real-time, onboard sensor feedback. To address these key behaviors, we present a novel method for vision-based autonomous MAV landing and ingress using a single camera for two urban scenarios: landing on an elevated surface, representative of a rooftop, and ingress through a rectangular opening, representative of a door or window. Real-world scenarios will not include special navigation markers, so we rely on tracking arbitrary scene features; however, we do currently exploit planarity of the scene. Our vision system uses a planar homography decomposition to detect navigation targets and to produce approach waypoints as inputs to the vehicle control algorithm. Scene perception, planning, and control run onboard in real-time; at present we obtain aircraft position knowledge from an external motion capture system, but we expect to replace this in the near future with a fully self-contained, onboard, vision-aided state estimation algorithm. We demonstrate autonomous vision-based landing and ingress target detection with two different quadrotor MAV platforms. To our knowledge, this is the first demonstration of onboard, vision-based autonomous landing and ingress algorithms that do not use special purpose scene markers to identify the destination.

micro air vehicles (MAVs)↗

NASA NTRSDate not supplied

Vision-Based Distributed Sensing at Vertiports for Advanced Air Mobility and Urban Air Mobility Approach and Landing

Advanced Air Mobility (AAM) encompasses a broad vision for air transportation, including Urban Air Mobility (UAM) as a subset. AAM aims to create a more connected and efficient transportation network across various geographical settings. However, navigating AAM aircraft in GPS-denied or degraded environments during approach and landing is challenging. Traditional vision aids like glideslopes and localizers are limited in vertiport environments due to narrow beam constraints and reduced landing angle options. This paper addresses the need for accurate navigation solutions at vertiports by proposing a vision-based distributed sensing (VIDIS) system utilizing cameras with bundle adjustment to assist incoming AAM aircraft during approach and landing while monitoring surface movements to enhance safety and efficiency. Key focus areas for current and future vertiport developers include identifying suitable sensor types and infrastructure standards to support AAM operations and including vertiport markings as vision-based navigation aids. The proposed system offers a novel approach to overcoming navigation challenges in AAM operations, particularly in urban settings where traditional aids may be insufficient. Preliminary simulation results with distributed cameras demonstrate promising outcomes for implementing bundle adjustment techniques to enhance vision-based navigation solutions at vertiports. Generating waypoint-based trajectories via waypoint integration using explicit guidance synthesis (WINGS) creates smooth AAM trajectories for landing at vertiports by using the current waypoint's terminal conditions as the initial conditions for the next waypoint. Combining bundle adjustment's ground-based solution of vertiport features with WINGS, Coplanar Pose from Orthography and Scaling with Iterations (COPOSIT), and an extended Kalman filter (EKF) estimates the state of an incoming aircraft during approach and landing at vertiports. Future work includes testing VIDIS in a high-fidelity simulation and with real-world data.

Distributed sensing↗

NASA NTRSDate not supplied

Vision-Based Distributed Sensing at Vertiports for Advanced Air Mobility and Urban Air Mobility Approach and Landing

Distributed sensing↗

DOE OSTI2017

2016-2017 Status Assessment and Update on the Wind Vision Roadmap: Findings from Topical Working Sessions, April 2016 - March 2017

IIn March 2015, the U.S. Department of Energy (DOE) released Wind Vision: A New Era for Wind Power in the United States (DOE 2015), which explores a scenario in which wind provides 10 percent of U.S. electricity in 2020, 20 percent in 2030, and 35 percent in 2050. The Wind Vision report also includes a roadmap of recommended actions aimed at pursuit of the vision and its underlying wind-deployment scenario. The roadmap was compiled by the Wind Vision project team, which included representatives from the industrial, electric-power, government-laboratory, academic, environmental-stewardship, regulatory, and permitting stakeholder groups. The roadmap describes high-level activities suitable for all sectors with a stake in wind power and energy development. It is intended to be a 'living document,' and DOE expects to engage the wind community from time to time to track progress.

17 WIND ENERGY↗

DOE OSTI2018

Data fusion for a vision-aided radiological detection system: Calibration algorithm performance

In order to improve the ability to detect, locate, track and identify nuclear/radiological threats, the University of Florida nuclear detection community has teamed up with the 3D vision community to collaborate on a low cost data fusion system. The key is to develop an algorithm to fuse the data from multiple radiological and 3D vision sensors as one system. The system under development at the University of Florida is being assessed with various types of radiological detectors and widely available visual sensors. A series of experiments were devised utilizing two EJ-309 liquid organic scintillation detectors (one primary and one secondary), a Microsoft Kinect for Windows v2 sensor and a Velodyne HDL-32E High Definition LiDAR Sensor which is a highly sensitive vision sensor primarily used to generate data for self-driving cars. Each experiment consisted of 27 static measurements of a source arranged in a cube with three different distances in each dimension. The source used was Cf-252. The calibration algorithm developed is utilized to calibrate the relative 3D-location of the two different types of sensors without need to measure it by hand; thus, preventing operator manipulation and human errors. The algorithm can also account for the facility dependent deviation from ideal data fusion correlation. Use of the vision sensor to determine the location of a sensor would also limit the possible locations and it does not allow for room dependence (facility dependent deviation) to generate a detector pseudo-location to be used for data analysis later. Using manually measured source location data, our algorithm-predicted the offset detector location within an average of 20 cm calibration-difference to its actual location. Calibration-difference is the Euclidean distance from the algorithm predicted detector location to the measured detector location. The Kinect vision sensor data produced an average calibration-difference of 35 cm and the HDL-32E produced an average calibration-difference of 22 cm. Using NaI and He-3 detectors in place of the EJ-309, the calibration-difference was 52 cm for NaI and 75 cm for He-3. In conclusion, the algorithm is not detector dependent; however, from these results it was determined that detector dependent adjustments are required.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2018

Wind Vision Detailed Roadmap Actions: 2017 Update

In March 2015, the U.S. Department of Energy (DOE) released Wind Vision: A New Era for Wind Power in the United States, which explores and quantifies the economic and environmental costs and benefits of a scenario in which wind provides 10% of U.S. electrical demand in 2020, 20% in 2030, and 35% in 2050. The Wind Vision report also includes a Roadmap that describes actions stakeholders can pursue to achieve the vision and its underlying wind deployment scenario. The Wind Vision and its Roadmap were compiled by representatives from DOE and its national laboratories in consultation with the wind industry, trade associations, academia, environmental organizations, and other stakeholders. When the Wind Vision was released in 2015, DOE announced its intention to engage the wind community from time to time to track progress and update the Roadmap periodically.

17 WIND ENERGY↗

DOE OSTI2023

High Dose Rate Irradiations of Enduray Vision System for Nuclear Inspections

Higher radiation-hardened video cameras are needed in the operation and remote handling of equipment in nuclear reactor inspection and refueling applications. Vega Wave Systems, Inc. has developed a radiation-hardened vision system for nuclear energy applications. This vision system has been developed under several small business innovative research programs (SBIRs) from the U.S. Department of Energy, and the previous GAIN voucher program at Argonne National Laboratory (ANL) was successful in demonstrating that a prototype of the camera is radiation-hard up to at least 525 kGy (5.25 × 106 rad, Si equivalent) at dose rates of 10 kGy/hr with no measurable degradation in image quality and no measurable radiation-induced noise (RIN). Vega Wave Systems has developed a new version of the camera with more than 3X the resolution using a new design and new components, and this new design requires radiation-hardness qualification for marketplace acceptance. The program described in this report provided high radiation-hardness testing of this redesigned high-resolution vision system using the Argonne Low-Energy Accelerator Facility (LEAF). This report presents the results of irradiation tests performed on Vega Wave System’s redesigned high resolution vision system at a dose rate of 9.3 kGy/hr and up to total doses of 1823 kGy. This was an accumulation of 197 hours of irradiation. The results were excellent, providing proof of the redesigned high-resolution vision system’s immunity to high levels of radiation.

42 ENGINEERING↗

NASA NTRS1972

Development of a model of machine hand eye coordination and program specifications for a topological machine vision system

A unified approach to computer vision and manipulation is developed which is called choreographic vision. In the model, objects to be viewed by a projected robot in the Viking missions to Mars are seen as objects to be manipulated within choreographic contexts controlled by a multimoded remote, supervisory control system on Earth. A new theory of context relations is introduced as a basis for choreographic programming languages. A topological vision model is developed for recognizing objects by shape and contour. This model is integrated with a projected vision system consisting of a multiaperture image dissector TV camera and a ranging laser system. System program specifications integrate eye-hand coordination and topological vision functions and an aerospace multiprocessor implementation is described.

Source record↗

NASA NTRS1984

Theory underlying the peripheral vision horizon device

Peripheral Vision Horizon Device (PVHD) theory states that the likelihood of pilot disorientation in flight is reduced by providing an artificial horizon that provides orientation information to peripheral vision. In considering the validity of the theory, three areas are explored: the use of an artificial horizon device over some other flight instrument; the use of peripheral vision over foveal vision; and the evidence that peripheral vision is well suited to the processing of orientation information.

Money, K. E.↗

NASA NTRS1986

Computational approaches to vision

Vision is examined in terms of a computational process, and the competence, structure, and control of computer vision systems are analyzed. Theoretical and experimental data on the formation of a computer vision system are discussed. Consideration is given to early vision, the recovery of intrinsic surface characteristics, higher levels of interpretation, and system integration and control. A computational visual processing model is proposed and its architecture and operation are described. Examples of state-of-the-art vision systems, which include some of the levels of representation and processing mechanisms, are presented.

Barrow, H. G.↗

NASA NTRS1991

A vision-based end-point control for a two-link flexible manipulator

The measurement and control of the end-effector position of a large two-link flexible manipulator are investigated. The system implementation is described and an initial algorithm for static end-point positioning is discussed. Most existing robots are controlled through independent joint controllers, while the end-effector position is estimated from the joint positions using a kinematic relation. End-point position feedback can be used to compensate for uncertainty and structural deflections. Such feedback is especially important for flexible robots. Computer vision is utilized to obtain end-point position measurements. A look-and-move control structure alleviates the disadvantages of the slow and variable computer vision sampling frequency. This control structure consists of an inner joint-based loop and an outer vision-based loop. A static positioning algorithm was implemented and experimentally verified. This algorithm utilizes the manipulator Jacobian to transform a tip position error to a joint error. The joint error is then used to give a new reference input to the joint controller. The convergence of the algorithm is demonstrated experimentally under payload variation. A Landmark Tracking System (Dickerson, et al 1990) is used for vision-based end-point measurements. This system was modified and tested. A real-time control system was implemented on a PC and interfaced with the vision system and the robot.

Obergfell, Klaus↗

NASA NTRS1992

Image enhancement filters significantly improve reading performance for low vision observers

As people age, so do their photoreceptors; many photoreceptors in central vision stop functioning when a person reaches their late sixties or early seventies. Low vision observers with losses in central vision, those with age-related maculopathies, were studied. Low vision observers no longer see high spatial frequencies, being unable to resolve fine edge detail. We developed image enhancement filters to compensate for the low vision observer's losses in contrast sensitivity to intermediate and high spatial frequencies. The filters work by boosting the amplitude of the less visible intermediate spatial frequencies. The lower spatial frequencies. These image enhancement filters not only reduce the magnification needed for reading by up to 70 percent, but they also increase the observer's reading speed by 2-4 times. A summary of this research is presented.

Lawton, T. B.↗

NASA NTRS1994

Microwave vision for robots

Microwave Vision (MV), a concept originally developed in 1985, could play a significant role in the solution to robotic vision problems. Originally our Microwave Vision concept was based on a pattern matching approach employing computer based stored replica correlation processing. Artificial Neural Network (ANN) processor technology offers an attractive alternative to the correlation processing approach, namely the ability to learn and to adapt to changing environments. This paper describes the Microwave Vision concept, some initial ANN-MV experiments, and the design of an ANN-MV system that has led to a second patent disclosure in the robotic vision field.

Lewandowski, Leon↗

NASA NTRS2002

Flexible Wing Base Micro Aerial Vehicles: Vision-Guided Flight Stability and Autonomy for Micro Air Vehicles

Substantial progress has been made recently towards design building and test-flying remotely piloted Micro Air Vehicle's (MAVs). We seek to complement this progress in overcoming the aerodynamic obstacles to.flight at very small scales with a vision stability and autonomy system. The developed system based on a robust horizon detection algorithm which we discuss in greater detail in a companion paper. In this paper, we first motivate the use of computer vision for MAV autonomy arguing that given current sensor technology, vision may he the only practical approach to the problem. We then briefly review our statistical vision-based horizon detection algorithm, which has been demonstrated at 30Hz with over 99.9% correct horizon identification. Next we develop robust schemes for the detection of extreme MAV attitudes, where no horizon is visible, and for the detection of horizon estimation errors, due to external factors such as video transmission noise. Finally, we discuss our feed-back controller for self-stabilized flight, and report results on vision autonomous flights of duration exceeding ten minutes.

Ettinger, Scott M.↗

NASA NTRS2003

Sonoluminescence Bubble Measurements using Vision-Based Algorithms

Vision-based measurement methods were used to measure bubble sizes in this sonoluminescence experiment. Bubble imaging was accomplished by placing the bubble between a bright light source and a microscope-CCD camera system. A collimated light-emitting diode was operated in a pulsed model with an adjustable time delay with respect to the piezo-electric transducer drive signal. The light-emitting diode produced a bubble shadowgraph consisting of a multiple exposure made by numerous light pulses imaged onto a charge-couple device camera. Each image was transferred from the camera to a computer-controlled machine vision system via a frame grabber. The frame grabber was equipped with on-board memory to accomodate sequential image buffering while images were transferred to the host processor and analyzed. This configuration allowed the host computer to perform diameter measurements, centroid position measurements and shape estimation in "real-time" as the next image was being acquired. Bubble size measurement accuracy with an uncertainty of 3 microns was achieved using standard lenses and machine vision algorithms. Bubble centroid position accuracy was also within the 3 micron tolerance of the vision system. This uncertainty estimation accounted for the optical spatial resolution, digitization errors and the edge detection algorithm accuracy. The vision algorithms include camera calibration, thresholding, edge detection, edge position determination, distance between two edges computations and centroid position computations.

Hall, Nancy R.↗