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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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7 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Unmanned Ground Vehicle Perception Using Thermal Infrared Cameras

The ability to perform off-road autonomous navigation at any time of day or night is a requirement for some unmanned ground vehicle (UGV) programs. Because there are times when it is desirable for military UGVs to operate without emitting strong, detectable electromagnetic signals, a passive only terrain perception mode of operation is also often a requirement. Thermal infrared (TIR) cameras can be used to provide day and night passive terrain perception. TIR cameras have a detector sensitive to either mid-wave infrared (MWIR) radiation (3-5?m) or long-wave infrared (LWIR) radiation (8-12?m). With the recent emergence of high-quality uncooled LWIR cameras, TIR cameras have become viable passive perception options for some UGV programs. The Jet Propulsion Laboratory (JPL) has used a stereo pair of TIR cameras under several UGV programs to perform stereo ranging, terrain mapping, tree-trunk detection, pedestrian detection, negative obstacle detection, and water detection based on object reflections. In addition, we have evaluated stereo range data at a variety of UGV speeds, evaluated dual-band TIR classification of soil, vegetation, and rock terrain types, analyzed 24 hour water and 12 hour mud TIR imagery, and analyzed TIR imagery for hazard detection through smoke. Since TIR cameras do not currently provide the resolution available from megapixel color cameras, a UGV's daytime safe speed is often reduced when using TIR instead of color cameras. In this paper, we summarize the UGV terrain perception work JPL has performed with TIR cameras over the last decade and describe a calibration target developed by General Dynamics Robotic Systems (GDRS) for TIR cameras and other sensors.

terrain classification↗
NASA NTRS2011

Unmanned Ground Vehicle Perception Using Thermal Infrared Cameras

TIR cameras can be used for day/night Unmanned Ground Vehicle (UGV) autonomous navigation when stealth is required. The quality of uncooled TIR cameras has significantly improved over the last decade, making them a viable option at low speed Limiting factors for stereo ranging with uncooled LWIR cameras are image blur and low texture scenes TIR perception capabilities JPL has explored includes: (1) single and dual band TIR terrain classification (2) obstacle detection (pedestrian, vehicle, tree trunks, ditches, and water) (3) perception thru obscurants

calibration↗
NASA NTRS2009

Head Pose Estimation Using Multilinear Subspace Analysis for Robot Human Awareness

Mobile robots, operating in unconstrained indoor and outdoor environments, would benefit in many ways from perception of the human awareness around them. Knowledge of people's head pose and gaze directions would enable the robot to deduce which people are aware of the its presence, and to predict future motions of the people for better path planning. To make such inferences, requires estimating head pose on facial images that are combination of multiple varying factors, such as identity, appearance, head pose, and illumination. By applying multilinear algebra, the algebra of higher-order tensors, we can separate these factors and estimate head pose regardless of subject's identity or image conditions. Furthermore, we can automatically handle uncertainty in the size of the face and its location. We demonstrate a pipeline of on-the-move detection of pedestrians with a robot stereo vision system, segmentation of the head, and head pose estimation in cluttered urban street scenes.

Ivanov, Tonislav↗
NASA NTRS1980

AMTV headway sensor and safety design

A headway sensing system for an automated mixed traffic vehicle (AMTV) employing an array of optical proximity sensor elements is described, and its performance is presented in terms of object detection profiles. The problem of sensing in turns is explored experimentally and requirements for future turn sensors are discussed. A recommended headway sensor configuration, employing multiple source elements in the focal plane of one lens operating together with a similar detector unit, is described. Alternative concepts including laser radar, ultrasonic sensing, imaging techniques, and radar are compared to the present proximity sensor approach. Design concepts for an AMTV body which will minimize the probability of injury to pedestrians or passengers in the event of a collision are presented.

Johnston, A. R.↗
NASA NTRS2020

Detecting differential ground displacements of civil structures in fast-subsiding metropolises with interferometric SAR and band-pass filtering

Ground displacements due to changes in soil conditions represent a threat to the stability of civil structures in many urban areas, worldwide. In fast-subsiding areas, regional subsidence (wavelength ~ 1,000’s m) can be dominantly high and, consequently, mask other signals at local scales (wavelength ~ 10–100’s m). Still, engineering and construction applications require a comprehensive knowledge of local-scale signals, which can threaten the stability of buildings and infrastructure. Here we present a new technique based on band-pass filters for uncovering local-scale signals hidden by regional subsidence as detected by interferometric SAR measurements. We apply our technique to a velocity field calculated from 21 high-resolution COSMO-SkyMed scenes acquired over Mexico City and obtain components of long (> 478 m), intermediate (42–478 m) and short (< 42 m) spatial wavelengths. Our results reveal that long-wavelength velocities exceed − 400 mm/year, whereas intermediate- and short-wavelength velocities are in the order of ± 15 mm/year. We show that intermediate-wavelength velocities are useful for retrieving signals such as uplift along elevated viaducts of Metro lines 4 and B, as well as differential displacements in Pantitlán station’s pedestrian overpass system and across sharp geotechnical boundaries in the piedmont of Sierra de Santa Catarina—where surface faulting occurs.

Darío Solano-Rojas↗
NASA NTRSDate not supplied

Structural Health Monitoring of Vertical Lift Structures Operating in an Urban Environment

Verifying the structural integrity of aeronautical vehicles is of critical importance for the safety of passengers and crew. When operating in a densely populated urban area, pedestrian safety and collateral damage to buildings and other infrastructure also become important concerns. Damage modes, including fatigue from an increased frequency of take-off and landing cycles and a higher probability of impacts from bird strikes, also pose different failure risks as compared to commercial long-haul aircraft and thus require a change in structural health monitoring philosophy. This presentation will review the state of the art in structural health monitoring with an eye toward applications for vertical lift systems operated in the urban environment. Structural health monitoring methods including eddy current, fiber optic, ultrasonic plate wave, surface acoustic wave and electrical impedance tomography will be explored for use in on-board monitoring of impact and overstrain events as well as damage detection and characterization. Infusion of the data into an early warning system to help ensure damage will not propagate to failure during flight will also be discussed.

Russell A Wincheski↗
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↗