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At least 307 records · Page 17

Initial Study of an Effective Fast-Time Simulation Platform for Unmanned Aircraft System Traffic Management

Small Unmanned Aerial Vehicles (sUAVs), typically 55 lbs and below, are envisioned to play a major role in surveilling critical assets, collecting important information, and delivering goods. Large scale small UAV operations are expected to happen in low altitude airspace in the near future. Many static and dynamic constraints exist in low altitude airspace because of manned aircraft or helicopter activities, various wind conditions, restricted airspace, terrain and man-made buildings, and conflict-avoidance among sUAVs. High sensitivity and high maneuverability are unique characteristics of sUAVs that bring challenges to effective system evaluations and mandate such a simulation platform different from existing simulations that were built for manned air traffic system and large unmanned fixed aircraft. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative focuses on enabling safe and efficient sUAV operations in the future. In order to help define requirements and policies for a safe and efficient UTM system to accommodate a large amount of sUAV operations, it is necessary to develop a fast-time simulation platform that can effectively evaluate requirements, policies, and concepts in a close-to-reality environment. This work analyzed the impacts of some key factors including aforementioned sUAV's characteristics and demonstrated the importance of these factors in a successful UTM fast-time simulation platform.

UTM↗

Initial Study of An Effective Fast-Time Simulation Platform for Unmanned Aircraft System Traffic Management

Small Unmanned Aerial Vehicles (sUAVs), typically 55 lbs and below, are envisioned to play a major role in surveilling critical assets, collecting important information, and delivering goods. Large scale small UAV operations are expected to happen in low altitude airspace in the near future. Many static and dynamic constraints exist in low altitude airspace because of manned aircraft or helicopter activities, various wind conditions, restricted airspace, terrain and man-made buildings, and conflict-avoidance among sUAVs. High sensitivity and high maneuverability are unique characteristics of sUAVs that bring challenges to effective system evaluations and mandate such a simulation platform different from existing simulations that were built for manned air traffic system and large unmanned fixed aircraft. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative focuses on enabling safe and efficient sUAV operations in the future. In order to help define requirements and policies for a safe and efficient UTM system to accommodate a large amount of sUAV operations, it is necessary to develop a fast-time simulation platform that can effectively evaluate requirements, policies, and concepts in a close-to-reality environment. This work analyzed the impacts of some key factors including aforementioned sUAV's characteristics and demonstrated the importance of these factors in a successful UTM fast-time simulation platform.

UTM↗

Legacy surface change analyses of the 1993 Rock Valley earthquake sequence for direct comparison to planned NA-22 underground conventional high-explosive experiments (Source Physics Experiment 3 (SPE3) - RV/DC Task 1.3 FY22 Final Report)

Recent work under two previous phases of the NNSA NA-22 Source Physics Experiment (SPE) have shown that underground chemical high-explosive experiments can produce detectable surface changes that differ in spatial extent and vertical magnitude depending on the geologic media at the site (Schultz-Fellenz et al., 2018; 2020; Crawford et al., 2021). Neither of these two prior phases of SPE identified natural earthquake-related surface effects in the same region that occurred at a similar depth as the explosive experiments for direct comparison. While earthquakes can also produce surface changes that are detectable using remote sensing data analyses, it is expected that the pattern and spatial extent of surface changes would vary between earthquakes and explosions. However, no direct-observed surface-change signature comparison between earthquakes and explosions has ever been performed. The SPE Phase 3 Rock Valley/Direct Comparison (RV/DC) program presents a unique opportunity to investigate and characterize co-occurrence of both earthquakes and explosions. In this project, we worked to address five tasks in a workflow, as follows: 1. Identify and obtain existing high-resolution legacy satellite and aerial imagery as close in time before and after the 1993 Rock Valley earthquake sequence to temporally constrain the analyses. 2. Transform these pre-earthquake and post-earthquake datasets into digital elevation models (DEMs) using geospatial analysis software packages (e.g., ArcGIS, Agisoft Metashape, and Google Earth Engine). 3. Perform DEM differencing analyses to assess and quantify earthquake-related changes from the 1993 sequence, and develop map products that visualize these analyses. 4. Use the analyses from (3) to: (a) assess spatial distribution and magnitude of surface changes due to the 1993 Rock Valley earthquake sequence, and (b) determine parameters of forthcoming, planned explosion-related surface change data collection from sensors mounted on unmanned aerial vehicles (UAVs) (e.g., spatial extent of collection, design and density of survey control, sensors to deploy, forward speed and line spacing of UAV flight lines, flight altitude). 5. Develop a summary report on the analyses, including how the analyses define parameters and identify focus areas for any future surface change analytical field campaigns related to the explosive experiment. Analyzing these legacy data and identifying whether they can detect any surface changes related to the earthquake sequence facilitates opportunities for direct signature comparison of surface change from explosions at one location, which has never previously been performed. Comparing the surface change signatures from a co-located and depth-equivalent earthquake and an explosion could help to advance remote sensing event discrimination techniques. This report summarizes the work completed toward this ambitious goal.

42 ENGINEERING↗

Flight Testing of Novel Compliant Spines for Passive Wing Morphing on Ornithopters

Unmanned Aerial Vehicles (UAVs) are proliferating in both the civil and military markets. Flapping wing UAVs, or ornithopters, have the potential to combine the agility and maneuverability of rotary wing aircraft with excellent performance in low Reynolds number flight regimes. The purpose of this paper is to present new free flight experimental results for an ornithopter equipped with one degree of freedom (1DOF) compliant spines that were designed and optimized in terms of mass, maximum von-Mises stress, and desired wing bending deflections. The spines were inserted in an experimental ornithopter wing spar in order to achieve a set of desired kinematics during the up and down strokes of a flapping cycle. The ornithopter was flown at Wright Patterson Air Force Base in the Air Force Research Laboratory Small Unmanned Air Systems (SUAS) indoor flight facility. Vicon motion tracking cameras were used to track the motion of the vehicle for five different wing configurations. The effect of the presence of the compliant spine on wing kinematics and leading edge spar deflection during flight is presented. Results show that the ornithopter with the compliant spine inserted in its wing reduced the body acceleration during the upstroke which translates into overall lift gains.

Wissa, Aimy↗

The High Density Vertiplex Advanced Onboard Automation Overview

While many studies have been performed examining Urban Air Mobility (UAM) operations from UAM Maturity Level (UML) UML-1 to UML-4, [1, 2] some uncertainty exists regarding the integration and role of onboard autonomous systems, airspace management systems, ground control and fleet management systems, and how they integrate with vertiport automation systems to ensure safe high-density future operations. One thrust of the Advanced Air Mobility (AAM) High Density Vertiplex (HDV) sub-project is to perform rapid prototyping and assessment of an Urban Air Mobility (UAM) Ecosystem within the terminal operational area to help inform future research investments and technology development. Another thrust within HDV is to perform integration, testing, and safety risk assessments required to acquire operational credit for several NASA small Unmanned Aerial Systems (sUAS) beyond visual line of sight (BVLOS) enabling technologies to expand test capabilities and to expedite technology transfer and ultimate effective usage. Both thrusts leverage sUAS to serve as surrogates for the highly-technologically-similar envisioned UAM aircraft as well as to provide significant contributions to sUAS Part-135 operators. This report provides an overview of the activities accomplished within the Advanced Onboard Automation (AOA) schedule work package of HDV.

Human Factors, Simulation↗

L1 Adaptive Control with Switched Reference Models: Application to Learn-to-Fly

Learn-to-Fly (L2F) is a new framework that aims to replace the traditional iterative development paradigm for aerial vehicles with a combination of real-time aerodynamic modeling, guidance, and learning control. To ensure safe learning of the vehicle dynamics on the fly, this paper presents an L1 adaptive control (L1AC) based scheme, which actively estimates and compensates for the discrepancy between the intermediately learned dynamics and the actual dynamics. First, to incorporate the periodic update of the learned model within the L2F framework, this paper extends the L1AC architecture to handle a switched reference system subject to unknown time-varying parameters and disturbances. The paper also includes analysis of both transient and steady-state performance of the L1AC architecture in the presence of non-zero initialization error for the state predictor. Second, the paper presents how the proposed L1AC scheme is integrated into the L2F framework, including its interaction with the baseline controller and the real-time modeling module. Finally, flight tests on an unmanned aerial vehicle (UAV) validate the efficacy of the proposed control and learning scheme.

Steven Snyder↗

Decentralized modular hybrid supervisory control for the formation of unmanned helicopters

Abstract Formation control of Unmanned Aerial Vehicles (UAVs) requires them to tightly cooperate to reach and keep the formation, while avoiding collision. This paper proposes a novel decentralized hybrid supervisory control approach for the formation control of multiple UAVs. This is achieved by developing a symbolic motion planning technique to polarly partition the motion space resulting in a finite state discrete event model for the motion dynamics of each UAV. Then, a modular discrete supervisor is designed for different components of the formation mission including reaching the formation, keeping the formation, and collision avoidance. Further, for the collision avoidance mechanism, a novel top‐down decomposition‐based approach is developed to design local supervisors decentralizedly. It is formally proved that with the proposed top‐down decomposition‐based approach, the (locally) supervised UAVs, as a whole, can cooperatively satisfy the desired (global) collision avoidance specification. The proposed decentralized supervisory control algorithm is also verified through a hardware‐in‐the‐loop simulator for the formation control of unmanned helicopters.

Karimoddini, Ali↗

UAS Conflict-Avoidance Using Multiagent RL with Abstract Strategy Type Communication

The use of unmanned aerial systems (UAS) in the national airspace is of growing interest to the research community. Safety and scalability of control algorithms are key to the successful integration of autonomous system into a human-populated airspace. In order to ensure safety while still maintaining efficient paths of travel, these algorithms must also accommodate heterogeneity of path strategies of its neighbors. We show that, using multiagent RL, we can improve the speed with which conflicts are resolved in cases with up to 80 aircraft within a section of the airspace. In addition, we show that the introduction of abstract agent strategy types to partition the state space is helpful in resolving conflicts, particularly in high congestion.

Unmanned Autonomous Systems↗

Announced Strategy Types in Multiagent RL for Conflict-Avoidance in the National Airspace

The use of unmanned aerial systems (UAS) in the national airspace is of growing interest to the research community. Safety and scalability of control algorithms are key to the successful integration of autonomous system into a human-populated airspace. In order to ensure safety while still maintaining efficient paths of travel, these algorithms must also accommodate heterogeneity of path strategies of its neighbors. We show that, using multiagent RL, we can improve the speed with which conflicts are resolved in cases with up to 80 aircraft within a section of the airspace. In addition, we show that the introduction of abstract agent strategy types to partition the state space is helpful in resolving conflicts, particularly in high congestion.

National Airspace↗

Vibration Anomaly Indicator in UAVs in presence of Wind

One of the critical factors affecting flight safety of unmanned aerial vehicles (UAVs) is the amount of vibration they are exposed during a flight. For UAVs under remote operation, vehicle stabilization and navigation is typically achieved by estimating its attitude and position using onboard miniature sensors such as accelerometers, gyroscopes, and GPS via an onboard autopilot. Since precise control of the UAV relies heavily on the attitude sensing, the vibration levels need to be as low as possible in order to minimize the signal noise. Incorrect sensor data can lead to uncertain state estimation causing the multirotor to drift from its desired position. Moreover, high vibrations can induce faults in the safety-critical components of the UAV such as its on-board sensors, motors and propellers. Hence, it is important to monitor the vibration levels during a UAV flight. This paper specifically looks into effect of wind on the vibrations recorded by the autopilot system in an octocopter. Using data from experimental flights under varying wind conditions, we aim to classify between a nominal and anomalous vibration level and define a safety metric known as the Vibrational Anomaly Indicator (VAI) for small UAV systems. Further, we will study effect of high vibrations on the inertial measurement unit (IMU) of an octocopter under laboratory set-up and compute the VAI from IMU measurements. Results would demonstrate the utility of VAI as an health indicator for unmanned flights either in presence of winds or from degraded on-board IMU sensor.

Vibration↗

Tensor Decomposition Analysis for UAV Anomaly Detection

Vibrational anomalies can provide valuable insights into the health status of an unmanned aerial vehicle, potentially indicating system degradation including propeller, motor, or sensor damage, as well as environmental anomalies such as strong wind gusts and turbulence. However, many causes for vibrational anomalies are not related to vehicle health, such as sharp shifts in velocity or direction of flight. Thus, depending strictly on vibration signals to detect anomalies can result in false positives for failures. Hence, it is important to include additional telemetries in detecting and diagnosing in-flight anomalies. This paper considers an approach to anomaly detection based on tensor decompositions that incorporates information from vibration signals, as well as additional flight data such as velocity, current draw, voltage drop, and attitude. Using experimental flight data collected by the University of Notre Dame, we construct third-order tensors then apply the CANDECOMP/PARAFAC decomposition to identify trends within each flight and classify flights as nominal or anomalous.

unmanned aviation↗

Assessing the Impact of Operational Constraints on the Near-Term Unmanned Aircraft System Traffic Management Supported Market

An economic impact market analysis was conducted for 16 leading sectors of commercial Unmanned Aerial System (UAS) applications predicted to be enabled by 2020 through the NASA UAS Traffic Management (UTM) program. Subject matter experts from seven industries were interviewed to validate concept of operations (ConOps) and market adoption assumptions for each sector. The market analysis was used to estimate direct economic impacts for each sector including serviceable addressable market, capital investment, revenue recovery potential, and operations cost savings. The resultant economic picture distinguishes the agricultural, pipeline and railroad inspection, construction, and maritime sectors of the nascent commercial UAS industry as providing the highest potential economic value in the United States. Sensitivity studies characterized the variability of select UAS sectors economic value to key regulatory or UTM ConOps requirements such as weight, altitude, and flight over populated area constraints. Takeaways from the analysis inform the validation of UTM requirements, technologies and timetables from a commercial market need and value viewpoint. This work concluded in August 2015 and reflects the state of the UAS industry and market projections at that time.

Air Traffic Management↗

Spatial and Temporal Deconfliction of Trajectories in the Presence of Uncertainties

Demonstration of conflict-free movement for multi-agent teams in challenging scenarios is crucial in developing trust and trustworthiness in an autonomous transport system. Tolerance verification queries are explored as a mechanism to enforce spatial and temporal deconfliction for a cooperating team of Unmanned Aerial Systems (UAS) with prescribed heterogeneous path-following performance. Obstacles in the environment are modelled as set of polyhedra, whereas each vehicle’s trajectory is represented as a sequence of polynomial curves with C2 continuity, expressed in a Bernstein basis. Each vehicle is modelled as a point mass and a safety distance, informed by the geometry of the UAS and the worst-case path-following error. This defines a tube around the trajectories where each UAS is most likely to fly through. In addition, obstacles in the environment have an associated safety buffer around them to account for the uncertainty in their location and geometric description. The tolerance verification queries explored in this paper combine the safety distance information from each UAS and environmental hazard to compute trajectories that are contained within the safe configuration space. Tolerance verification is also compared with other proximity queries to determine the suitability of each method along the different steps of the trajectory generation algorithm. This paper analyzes the fitness and performance of three proximity queries – collision, tolerance verification, and distance computations – between polyhedral and polynomial curves to ensure deconfliction between obstacles and vehicles, but also between polynomial curves to guarantee safe separation among cooperating UAS.

trajectory generation↗

Performance of Two Battery Prognostic Applications used by Two Octocopters for Safe Low Altitude Autonomous Flight Operations

This paper addresses the problem of building trust in online predictions of the remaining available flying time for two different electric Unmanned Aerial Vehicles (eUAVs) powered by lithium-ion-polymer batteries. Flight tests for various automation research missions for the two vehicles were monitored using two on-board battery health management applications to make predictions of the remaining flying time (RFT) for each eUAV and to predict the state of the battery. Playback of the voltage, current and temperature profiles of the battery discharge were used to assess the accuracy of the estimation of the voltage and the charge states of the models as well as the estimate of the RFT. The reference ground truth values were the observed landing time and the measured battery pack resting pack voltage 20 minutes after the flight. The predicted RFT, state of charge (SoC), and state of energy (SoE) were compared with the observed results. Noise values of one standard deviation from the mean values of the internal charge states of the battery model during a reference run were used to vary the states during simulation. One application used an equivalent circuit model of the electrical dynamics of the battery pack, and the other application used a reduced-order electrochemistry model. The variation of the model state components was compared to the variation in the estimate of the RFT and the variation in the SoE to estimate a confidence factor. Variation in the estimates caused by factors affecting the off-line laboratory parameter identification experiments is considered. Variation in the estimates due to environmental factors are discussed.

Assurance↗

Detect and Avoid: Efforts from NASA's UAS Integration into the NAS Project

NASA's Unmanned Aerial Systems (UAS) integration into the National Air Space (NAS) project has been working closely with the FAA and RTCA Special Committee 228 to identify and break down barriers to UAS integration. A focus of this work is on detect and avoid (DAA) technologies. A pilot has responsibility to see and avoid other aircraft and to remain "well clear," using their best judgment (Federal Aviation Regulations (FAR) Sec. 91.113). For UAS to perform this function, the see function is replaced by sensors to detect the other aircraft. Secondly, the pilot judgment of well clear has to be replaced by a mathematical expression. For Phase 1 of this effort, a well clear violation was defined if all three of these conditions are true: a) the horizontal clearance is less than 4000 ft., and b) the vertical clearance is less than 450 ft., and c) the time to loss of well clear is less than 35 seconds. This definition was developed with a great deal of community input and testing to ensure interoperability with Air Traffic Control (ATC) and pilots of manned aircraft. Appropriate guidance, alerting and displays were developed to allow UAS, with the appropriate sensors, to effectively maintain well clear. This work contributed to FAA Technical Standard Orders: TSO-C211, Detect and Avoid and TSO-C212, ATAR for Traffic Surveillance. Phase 2 of this work extends the operational environment to include the terminal area and lesser capable aircraft that might not have the payload capability to carry the RADAR defined in Phase 1. This session reports on work from Phase 1 and initial work in Phase 2.

Detect and Avoid (DAA)↗

In Situ Observations and Sampling of Volcanic Emissions with Unmanned Aircraft: A NASA/UCR Case Study at Turrialba Volcano, Costa Rica

Burgeoning new technology in the design and development of robotic aircraft-unmanned aerial vehicles (UAVs)-presents unprecedented opportunities for the volcanology community to observe, measure, and sample eruption plumes and drifting volcanic clouds in situ. While manned aircraft can sample dilute parts of such emissions, demonstrated hazards to air breathing, and most particularly turbine, engines preclude penetration of the zones of highest ash concentrations. Such areas within plumes are often of highest interest with respect to boundary conditions of applicable mass-loading retrieval models, as well as Lagrangian, Eulerian, and hybrid transport models used by hazard responders to predict plume trajectories, particularly in the context of airborne hazards. Before the 2010 Ejyafyallajokull eruption in Iceland, ICAO zero-ash-tolerance rules were typically followed, particularly for relatively uncrowded Pacific Rim airspace, and over North and South America, where often diversion of aircraft around ash plumes and clouds was practical. The 2010 eruption in Iceland radically changed the paradigm, in that critical airspace over continental Europe and the United Kingdom were summarily shut by local civil aviation authorities and EURO CONTROL. A strong desire emerged for better real-time knowledge of ash cloud characteristics, particularly ash concentrations, and especially for validation of orbital multispectral imaging. UAV platforms appear to provide a viable adjunct, if not a primary source, of such in situ data for volcanic plumes and drifting volcanic clouds from explosive eruptions, with prompt and comprehensive application to aviation safety and to the basic science of volcanology. Current work is underway in Costa Rica at Turrialba volcano by the authors, with the goal of developing and testing new small, economical UAV platforms, with miniaturized instrument payloads, within a volcanic plume. We are underway with bi-monthly deployments of tethered SO2-sondes and are in the planning stages for the deployment of the SIERRA UAV to our site in March 2013. We will be conducting in situ observations simultaneously with ASTER orbital multispectral TIR data acquisitions, in order to compare in situ measurements with estimates of SO2 mass loading and dispersion derived from ASTER data. Though small UAVs are now being considered for use in active volcanic areas for in situ sampling of emissions (e.g., efforts by our group, and by our colleagues at the INGV in Italy and the Applied Science University in Dusseldorf, Germany, and others in the United Kingdom and Iceland), and also for remote sensing, much more needs to be done in the way of instrument development, and in developing small UAVs for both low altitude (tropospheric) and high altitude (stratospheric) applications. In particular, the development of all weather and day/night operational flight capabilities in close proximity to hazardous topography is crucial to a truly responsive volcano in situ measurement system. Finally, it is imperative that national civil aviation authorities recognize the unique benefits of such platforms. It is important that authorities understand that severely restricting or not deploying such tools in airspace over restless volcanoes or within eruption plumes, ostensibly because of the perceived (small) risk that such unmanned aircraft pose to manned air operations, itself poses a bigger transcendental risk to proximal populations and particularly to the aviation community, itself.

Volcanic Ash Advisory Centers (VAACs)↗

Energy‐aware relay positioning in flying networks

Summary The ability to move and hover has made rotary‐wing unmanned aerial vehicles (UAVs) suitable platforms to act as flying communications relays (FCRs), aiming at providing on‐demand, temporary wireless connectivity when there is no network infrastructure available or a need to reinforce the capacity of existing networks. However, since UAVs rely on their on‐board batteries, which can be drained quickly, they typically need to land frequently for recharging or replacing them, limiting their endurance and the flying network availability. The problem is exacerbated when a single FCR UAV is used. The FCR UAV energy is used for two main tasks: Communications and propulsion. The literature has been focused on optimizing both the flying network performance and energy efficiency from the communications point of view, overlooking the energy spent for the UAV propulsion. Yet, the energy spent for communications is typically negligible when compared with the energy spent for the UAV propulsion. In this article, we propose energy‐aware relay positioning (EREP), an algorithm for positioning the FCR taking into account the energy spent for the UAV propulsion. Building upon the conclusion that hovering is not the most energy‐efficient state, EREP defines the trajectory and speed that minimize the energy spent by the FCR UAV on propulsion, without compromising in practice the quality of service offered by the flying network. The EREP algorithm is evaluated using simulations. The obtained results show gains up to 26% in the FCR UAV endurance for negligible throughput and delay degradation.

Rodrigues, Hugo↗