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At least 523 records · Page 29

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NASA NTRS2018

Initial Developments Toward an Active Noise Control System for Small Unmanned Aerial Systems

Small unmanned aerial systems have the potential to expand commercial markets from package delivery to infrastructure inspection. Many missions, however, require the vehicles to operate in close proximity to people, where community noise concerns could ultimately limit vehicle acceptability. Therefore, noise control technologies may be needed to achieve an acceptable noise signature and enable widespread use of these vehicles. The purpose of this paper is to assess the feasibility of using loudspeakers attached to the vehicle to actively reduce noise. More specifically, this initial study explores the possibility of using a single loudspeaker to reduce the noise from an isolated rotor. Tests performed in an anechoic chamber with a 7.6 cm diameter speaker and 23.9 cm diameter rotor are used to demonstrate the potential benefits and limitations of the concept. Results confirm that active noise control can work in this application with measured tonal reductions of over 30 dB in specific directions. However, since the radiation characteristics of the speaker are different than the rotor, amplification is observed in other directions. This technology has the potential to create a “cone of silence”, which could be steered during flight operations to minimize the impact on noise sensitive areas.

Schiller, Noah H.↗

NASA NTRS2019

An Improved Far-Field Small Unmanned Aerial System Optical Detection Algorithm

Onboard far-field aircraft detection is needed for safe non-cooperative traffic mitigation for autonomous small Unmanned Aerial System (sUAS) operations. This work presents an aircraft detect and track pipeline that fuses image differencing and morphological filtering detections for inputs to a Kalman-based object tracking pipeline. The pipeline is evaluated using two types of flight encounters: 1) motorcopter sUAS vs. fixed-wing sUAS 2) motorcopter sUAS vs general aviation plane.

Chester Dolph↗

NASA NTRSDate not supplied

Distributed Pressure Sensing for Enabling Self-Aware Autonomous Aerial Vehicles

Autonomous aerial transportation will be a fixture of future robotic societies, simultaneously requiring more stringent safety requirements and fewer resources for characterization than current commercial air transportation. More robust, adaptable, self-state estimation will be necessary to create such autonomous systems. We present a modular, scalable, distributed pressure sensing skin for aerodynamic state estimation of a large, flexible aerostructure. This skin used a network of 22 nodes that performed in-situ computation and communication of data collected from 74 pressure sensors, which were embedded into the skin panels of an ultra-lightweight 14-foot wingspan made from commutable, lattice-based subcomponents, and tested at NASA Langley Research Center's 14X22 wind tunnel. The density of the pressure sensors allowed for the use of a novel distributed algorithm to generate estimates of the wing lift contribution that were more accurate than the direct integration of the pressure distribution over the wing surface.

Daniel Cellucci↗

NASA NTRS2020

Fit2Fly: A Proof of Concept for Testing the Commercial Feasibility of Unmanned Aerial System Operations

As autonomous Unmanned Aerial Systems (UAS) become more prevalent, improvements in airworthiness assessments, data security, establishing vehicle trust, and more will become necessary to make fleet operations of these systems routine. Currently, these are verified for every UAS vehicle through a time-intensive manual inspection performed by human individuals. This process becomes infeasible when introduced into large-scale operations, so the Fit2Fly project is attempting to address these concerns. Fit2Fly is a multi-year project with a large scope, so we chose to narrow our focus to building a proof-of-concept feasibility simulation in tandem with physical UAS demonstration. This work resulted in a software simulation capable of running asynchronous commercial operations with persistent data modeling in coordination with several autonomous drones using integrated radio and GPS configurations.

Ryan Bonk↗

NASA NTRS2020

In-Flight Detection of Vibration Anomalies in Unmanned Aerial Vehicles

Owing to the frequency of occurrence and high risk associated with bearings, identification and characterization of bearing faults in motors via nondestructive evaluation (NDE) methods have been studied extensively, amongst which vibration analysis has been found to be a promising technique for early diagnosis of anomalies. However, a majority of the existing techniques rely on vibration sensors attached onto or in close proximity to the motor in order to collect signals with a relatively high SNR. Due to weight and space restrictions, these techniques cannot be used in unmanned aerial vehicles (UAVs), especially during flight operations since accelerometers cannot be attached onto motors in small UAVs. Small UAVs are often subjected to vibrational disturbances caused by multiple factors such as weather turbulence, propeller imbalance or bearing faults. Such anomalies may not only pose risks to UAV's internal circuitry, components or payload, they may also generate undesirable noise level particularly for UAVs expected to fly in low-altitudes or urban canyon. This paper presents a detailed discussion of challenges in in-flight detection of bearing failure in UAVs using existing approaches and offers potential solutions to detect overall vibration anomalies in small UAV operations based on IMU data.

Portia Banerjee↗

NASA NTRSDate not supplied

Bootstrapping Multi-Agent Unmanned Aerial Vehicle (UAV) System Integration Using Ground-Based Assets: Lessons Learned

The highly dynamic nature of UAVs imposes significant challenges when conducting initial testing ranging from safety risks posed by high-capacity lithium batteries and spinning propellers to rigorous timing demands on controllers and the consequences of failures mid-air. Flight testing of a single vehicle is time and labor intensive due to these challenges and more, and the complexity increases exponentially with the number of vehicles. While simulations and hardware-in-the-loop bench testing can provide adequate environments for preliminary validation, differences in system deployment architecture, software interfaces, and hardware infrastructure between simulation and a fleet of real UAVs create a sizable gap that must be navigated carefully during system integration. In support of the Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project, which had the goal of establishing a basis of certification of trust and trustworthiness in multi-agent autonomous systems, this gap was tackled from two directions. First, a novel mixed-reality simulation environment was engineered to blur the transition from simulation to flight hardware. Second, a fleet of Unmanned Surface Vehicles (USVs) was developed as a test and evaluation platform that more closely represented the final aerial fleet while eliminating many of the risks associated with air vehicles. This paper delves into the second element, analyzing the efficacy of the USV platform in performing system integration testing for the UAV system. In this paper we present the USV fleet and its role in reducing the aforementioned gaps in deployment architecture, software interfaces, and hardware infrastructure when moving from simulation to flight. An overview of the hardware and software onboard the vehicles will be provided along with supporting infrastructure. The system integration process will be documented including results in supporting both the overarching design reference mission (DRM) of ATTRACTOR and individual research efforts conducted during the project. Finally, we will discuss some of the practical lessons learned regarding the testing, deployment, and operation of multi-agent autonomous systems.

Matthew P Vaughan↗

NASA NTRSDate not supplied

Monocular Ranging for Small Unmanned Aerial Systems in the Far-Field

Recent proliferation of small Unmanned Aerial Systems (sUAS) applications requires onboard collision avoidance systems to mitigate the risk of collision with non-cooperative aircraft and manned aircraft, which may not see sUAS in time to perform an avoidance maneuver. An attractive avenue for onboard collision avoidance is the utilization of machine vision cameras due to their low size, weight and power (SWaP) requirements. In this paper, we characterize the range performance of a machine vision system developed in-house and mounted onto an sUAS. The technique was designed to estimate the performance of a sense-and-avoid system to ensure that the sensing components meet the well-clear requirements for the chosen platform and avoidance strategy. Experimental flight-test data was acquired from test-flights flown along multiple collision geometries for two intruders: a general Aviation (GA) aircraft and a fixed-wing sUAS. The ownship and both intruders were instrumented with inertial navigation systems (INS) recording position and attitude information. The range at first detection, 𝑹𝟎, was extracted from in-flight imagery of head-on collision course geometry synchronized with INS data from both aircraft and ground-truth values extracted from the raw imagery. This initial detection distance, 𝑹𝟎, scales with atmospheric attenuation. Therefore, under clear sky conditions, the derived 𝑹𝟎 value represents the upper bound on the detection range achievable by the test configuration of the detector. Results indicate that the maximum initial detection distance for a 4k resolution action camera fitted with a 41º Field of View (FOV) lens is 2.763 ± 0.037 km for a GA aircraft and 0.881 ± 0.061 km for a fixed-wing sUAS, respectively. The in results this study suggest that a vision-based detect and track system may be analyzed using the sensor characterization and contextualized within aircraft well-clear volumes.

Chester V. Dolph↗

NASA NTRS2017

Using Small Unmanned Aerial Systems (sUAS) and Helium Aerostats to Perform Far-Field Radiation Pattern Measurements of High-Frequency Antennas

A new methodology is described for performing near-free space far-field radiation pattern measurements of high frequency (HF) antennas utilizing small Unmanned Aerial Systems (sUAS) and helium-filled aerostat balloons for the radar antennas onboard NASA’s planned Europa Clipper mission to Jupiter’s moon Europa. Adapted from land-based measurements, this test methodology involves hoisting the antenna to be tested above the earth to minimize ground interactions while flying a sUAS with onboard measurement package to map the far field radiation pattern. Initial results producing radiation pattern maps are promising with work remaining to fully adapt fixed VHF measurements to the dynamic HF antenna test setup.

Decrossas, Emmanuel↗

NASA NTRS2021

Feasibility Analysis of Unmanned Aerial Vehicle Based on Tensegrity Structure

For future unmanned aerial vehicles to be ubiquitous, they need to be safe to other aircraft and people on the ground even in the event of unintended collisions. Unfortunately, this is difficult for current rigid designs as a crash of all but the smallest aircraft will have a significant impact on its target. As an alternative, this pa-per explores using a soft tensegrity structure (interconnected cables and rods) as a lightweight, robust chassis for a UAS and analyze its ability to achieve stable, controllable flight and mitigate damage during a collision. Tensegrity structures are three dimensional geometric constructions which are able to undergo severe deformations without permanent damage, and can immediately return to their original shape. Experiments and analysis are performed using a simple physics simulation environment, allowing for rapid design iteration and thorough exploration of system behavior. We test multiple configurations of the vehicle to understand impacts of changing various physical characteristics, and take limitations such as modern propeller thrust coefficients and motor speeds into account to ensure our results are inline with those of a real world vehicle. We ultimately find that such a vehicle has considerable real world potential, and that a physical prototype could theoretically be constructed and flown with current technology.

Bjorn Johnson↗

NASA NTRSDate not supplied

Uncertainty Propagation in Pre-Flight Prediction of Unmanned Aerial Vehicle Separation Violation

Current forecasts on the future of aeronautics suggest an in- creasing number of unmanned aerial vehicles entering the low- altitude airspace in the next decades (FAA, 2018; Kopardekar et al., 2016). Small vehicles for package delivery as well as larger vehicles for urban air mobility will change the airspace drastically, increasing density of operations both in time, i.e. high number of take-off and landings per unit time, and in space, operating in dense urban environment. This scenario poses challenges to the current approach to air traffic control, and large efforts from academia, industry and regulatory bodies are dedicated to the development of new traffic management strategies that leverage higher computing and simulating capabilities available today. In this paper, we propose a simple look-ahead approach to predict potential minimum separation violations at the strategic level, that is before vehicles start flying, depending on the predefined 4D trajectories and uncertainty affecting the wind acting along those routes. The wind field is extracted from the NOAA North America Mesoscale Forecast System and interpolated using Gaussian process regression, while uncertainty affecting the expected cruise airspeed is propagated through error intervals. The approach allows the prediction of aircraft separation as a function of time, highlighting potential safety violations that would go undetected if uncertainty affecting the expected 4D trajectories is not considered. The paper will also discuss issues related to accuracy and scalability of the approach to multiple vehicle operations.

Trajectory Prediction↗

NASA NTRSDate not supplied

In-Time Safety Assessment & Risk Prediction for Unmanned Aerial Systems

One of the critical challenges in emerging autonomous systems is timely mitigation of hazards encountered during operation which may not be known or accounted for at the time of design. Efficient execution of unmanned systems therefore demands a paradigm shift from scheduled periodic maintenance to predictive risk analysis that includes condition-based-monitoring, real-time reliability assessment and hazard mitigation. Particularly, the state-of-health parameters needs to be computed at the component level, unit level as well as the integrated system level. While in the former two levels, the physics of health propagation may be based on underlying electro-mechanical properties, system level prognostics often relies on data-driven models. Further, uncertainty from model, measurements and input sources should be accurately quantified to generate meaningful prediction results that can be fed into reliable decision making processes. Finally, the expected risk and time to failure has to be computed based on the current state-of-health of the overall system. This talk presents a conceptual design of such an in-time safety assurance approach for unmanned aerial vehicles (UAV) operating at low altitudes near and over populated areas. Typical in-flight hazard incidents include unplanned detour, proximity to obstacles, mid-flight component faults, limited battery life and poor quality of GPS measurements. Safety assessment therefore comprises trajectory generation and re-plan, battery RUL computation, distributed fault diagnostics and uncertainty management of predicted trajectory based on GPS measurement noise. The entire monitoring framework will be demonstrated on simulated as well as real UAV flight experiments conducted at the NASA Langley Research Center. This tutorial will therefore guide the audience through a step-by-step tracking of an autonomous system with focus on in-time risk prediction in the presence of unforeseen hazards and uncertain environment.

diagnostics↗

NASA NTRSDate not supplied

Define Minimum Safe Operational Volume for Aerial Vehicles in Upper Class E Airspace

This work presents both analytical and simulation studies for defining the minimum safe operational volume needed for aerial vehicles operating in upper Class E airspace. The analytical study is conducted first to investigate factors that affect the minimum safe operational volume, such as position errors, wake/vortex, vehicle maneuverability, communication and surveillance, control/operator response time. A mathematical approach is developed to compute the boundary of the safe operational volume required by the limited aircraft maneuverability and an analytical sensitivity study is then performed with varying parameters. After the analytical study, simulations are then conducted to verify the results from the analytical study. Simulation results will be included in the final paper.

Separation standard, well-clear definition, upper ↗

NASA NTRSDate not supplied

Define Minimum Safe Operational Volume for Aerial Vehicles in Upper Class E Airspace

The variety of vehicle performance in upper Class E airspace requires a method that can efficiently compute the minimum safe operational boundary between aircraft. This work presents a mathematical method to define the minimum safe operational boundary needed for aerial vehicles operating in upper Class E airspace. This method focuses on the extra separation required by vehicle maneuverability, communication delay, and control/operator response time. A sensitivity study is then performed to provide a general understanding of the impact of these factors on the extra separation needed. Experiments with pairwise encounters are conducted to verify the results generated by the proposed methods.

Separation standard↗

NASA NTRSDate not supplied

Aircraft Classification Using Radar from Small Unmanned Aerial Systems for Scalable Traffic Management Emergency Response Operations

This work investigates two machine learning techniques: Support Vector Machine (SVM) and Autoencoders (AE)with SVM layer for classification of radar trajectories as General Aviation (GA), fixed-wing small Unmanned Aerial System (sUAS), or not-an-aircraft using radar data recorded from sUAS. Onboard identification of intruder aircraft type is useful for planning avoidance maneuvers and is necessary to provide autonomous systems to meet or exceed the avoidance capability of a human pilot. Aircraft classification can identify intruder aircraft that are not part of the team and may be violating a Temporary Flight Restriction. Aircraft classification is needed in monitoring an airspace where multiple aircraft are teaming on a shared task. Scalable Traffic Management for Emergency Response Operations (STEReO) is a NASA project aimed at improving disaster response by enabling large scale aircraft operations through the teaming of manned aircraft with sUAS to maximize emergency response resources. To this end, this work uses trajectories and radar derived features to classify aircraft from a multirotor sUAS. The AE + SVM generated the strongest classification overall accuracy of 93.5% using the first 4 seconds of radar track data for tracks that activated the avoidance system. Subsampling the available track data increased the available training data with the maximum aircraft recall of 0.94 achieved using the SVM with 1 second track data.

Chester V. Dolph↗

NASA NTRSDate not supplied

Define Minimum Safe Operational Volume for Aerial Vehicles in Upper Class E Airspace

Separation↗

NASA NTRS2021

Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar

Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.

Eunsang Cho↗

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 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↗