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The PowerPoint presentation gives an overview of NASA Ames Research Center and its core competencies, as well as some of the highlights of Unmanned Aerial Vehicle (UAV) and Unmanned Aircraft Systems (UAS) accomplishments and innovations by researchers at Ames.
Prediction methods concerning remaining charge in lithium-ion batteries that power unmanned aerial vehicles are of critical concern for the safe fulfillment of mission objectives. In recent years, lithium-ion batteries have been the power source for both fixed wing and vertical lift electric vehicles. The purpose of this document is to describe in detail the implementation of a battery health monitor for estimating the state of charge of a lithium-ion battery and a lithium-ion polymer battery that is used to power a vertical lift aircraft test-bed. It will be demonstrated that an electro-chemistry based state of charge estimator effectively tracks battery discharge characteristics and may be employed as a useful tool in monitoring battery health.
The forest above-ground biomass (AGB) and spatial distribution of vegetation elements have profound effects on the productivity and biodiversity of terrestrial ecosystems. In this paper, we evaluated biomass estimation from L-band Synthetic Aperture Radar (SAR) data acquired by National Aeronautics and Space Administration (NASA) Uninhabited Aerial Vehicle SAR (UAVSAR) and the improvement of accuracy by adding canopy height information derived from stereo imagery acquired by Japan Aerospace Exploration Agency (JAXA) Panchromatic Remote Sensing Instrument for Stereo Mapping (PRISM) on-board the Advanced Land Observing Satellite (ALOS). Various models for prediction of forest biomass from UAVSAR data were investigated at pixel sizes of 1/4 ha (50 m x 50 m) and 1 ha. The variance inflation factor (VIF) was calculated for each of the explanatory variables in multivariable regression models to assess the multi-collinearity between explanatory variables. In addition, the t-and p-values were used to interpret the significance of the coefficients of each explanatory variables. The R(exp. 2), Root Mean Square Error (RMSE), bias and Akaike information criterion (AIC), and leave-one-out cross-validation (LOOCV) and bootstrapping were used to validate models. At 1/4-ha scale, the R(exp. 2) and RMSE of biomass estimation from a model using a single track of polarimetric UAVSAR data were 0.59 and 52.08 Mg/ha. With canopy height from PRISM as additional independent variable, R(exp. 2) increased to 0.76 and RMSE decreased to 39.74 Mg/ha (28.24%). At 1-ha scale, the RMSE of biomass estimation based on UAVSAR data of a single track was 39.42 Mg/ha with a R(exp. 2) of 0.77. With the canopy height from PRISM, R(exp. 2) increased to 0.86 and RMSE decreased to 29.47 Mg/ha (20.18%). The models using UAVSAR data alone underestimated biomass at levels above approximately 150 Mg/ha showing the saturation phenomenon. Adding canopy height from PRISM stereo imagery significantly improved the biomass estimation and elevated the saturation level in estimating biomass. Combined use of UAVSAR data acquired from opposite directions (odd and even tracks) slightly improved the biomass estimation.Combined use of UAVSAR data acquired from opposite directions (odd and even tracks) slightly improved the biomass estimation at 1/4-ha scale, R(exp. 2) increased from 0.59 to 0.66 and RMSE reduced from 52.08 to 48.57 Mg/ha. Averaging multiple acquisitions of UAVSAR data from the same look azimuth direction did not improve biomass estimation. A biomass map derived from NASA's LVIS (Laser Vegetation Imaging System) wave-form data was used as a reference for evaluation of the biomass maps from these models. The study has also shown that the errors decreased when deciduous, evergreen, and mixed forests were modeled separately but the improvement was not significant
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.
As the number of viable applications for unmanned aerial vehicle (UAV) systems increases at an exponential rate, interfaces that reduce the reliance on highly skilled engineers and pilots must be developed. Recent work aims to make use of common human communication modalities such as speech and gesture. This paper explores a multimodal natural language interface that uses a combination of speech and gesture input modalities to build complex UAV flight paths by defining trajectory segment primitives. Gesture inputs are used to define the general shape of a segment while speech inputs provide additional geometric information needed to fully characterize a trajectory segment. A user study is conducted in order to evaluate the efficacy of the multimodal interface.
This paper addresses the problem of building trust in the online prediction of a battery powered aircraft's remaining flying time. A series of flight tests is described that make use of a small electric powered unmanned aerial vehicle (eUAV) to verify the performance of the remaining flying time prediction algorithm. The estimate of remaining flying time is used to activate an alarm when the predicted remaining time is two minutes. This notifies the pilot to transition to the landing phase of the flight. A second alarm is activated when the battery charge falls below a specified limit threshold. This threshold is the point at which the battery energy reserve would no longer safely support two repeated aborted landing attempts. During the test series, the motor system is operated with the same predefined timed airspeed profile for each test. To test the robustness of the prediction, half of the tests were performed with, and half were performed without, a simulated powertrain fault. The pilot remotely engages a resistor bank at a specified time during the test flight to simulate a partial powertrain fault. The flying time prediction system is agnostic of the pilot's activation of the fault and must adapt to the vehicle's state. The time at which the limit threshold on battery charge is reached is then used to measure the accuracy of the remaining flying time predictions. Accuracy requirements for the alarms are considered and the results discussed.
We present two recent instrument technology developments at NASA, Fluid Lensing and MiDAR, and their application to remote sensing of Earth's aquatic systems. Fluid Lensing is the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions. MiDAR is a next-generation active hyperspectral remote sensing and optical communications instrument capable of active fluid lensing. Fluid Lensing has been used to provide 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft and underwater remotely operated vehicles (ROVs) to enable a new method for remote sensing of living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an objectâ€"TM"s non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Previously, MiDAR was developed into a TRL 2 laboratory instrument capable of imaging in thirty-two narrowband channels across the VNIR spectrum (400-950nm). Recently, MiDAR UV was raised to TRL4 and expanded to include five ultraviolet bands from 280-400nm, permitting UV remote sensing capabilities in UV A, B, and C bands and enabling mineral identification and stimulated fluorescence measurements of organic proteins and compounds, such as green fluorescent proteins in terrestrial and aquatic organics.
High-fidelity Computational Fluid Dynamics (CFD) have been carried out for the Elytron 4S Unmanned Aerial Vehicle (UAV), also known as the converticopter "proto12". It is the scaled wind tunnel model of the Elytron 4S, an Urban Air Mobility (UAM) concept, a tilt-wing, box-wing rotorcraft capable of Vertical Take-Off and Landing (VTOL). The three-dimensional unsteady Navier-Stokes equations are solved on overset grids employing high-order accurate schemes, dual-time stepping, and a hybrid turbulence model using NASA's CFD code OVERFLOW. The Elytron 4S UAV has been simulated in airplane mode and in helicopter mode.
This paper presents a novel hardware-in-the-loop (HIL) testbed for systems level diagnostics and prognostics of an electric propulsion system used in UAVs (unmanned aerial vehicle). Referencing the all electric, Edge 540T aircraft used in science and research by NASA Langley Flight Research Center, the HIL testbed includes an identical propulsion system, consisting of motors, speed controllers and batteries. Isolated under a controlled laboratory environment, the propulsion system has been instrumented for advanced diagnostics and prognostics. To produce flight like loading on the system a slave motor is coupled to the motor under test (MUT) and provides variable mechanical resistance, and the capability of introducing nondestructive mechanical wear-like frictional loads on the system. This testbed enables the verification of mathematical models of each component of the propulsion system, the repeatable generation of flight-like loads on the system for fault analysis, test-to-failure scenarios, and the development of advanced system level diagnostics and prognostics methods. The capabilities of the testbed are extended through the integration of a LabVIEW-based client for the Live Virtual Constructive Distributed Environment (LVCDC) Gateway which enables both the publishing of generated data for remotely located observers and prognosers and the synchronization the testbed propulsion system with vehicles in the air. The developed HIL testbed gives researchers easy access to a scientifically relevant portion of the aircraft without the overhead and dangers encountered during actual flight.
The guiding theme of Mars exploration is shifting from global and regional habitability assessment to biosignature detection. To locate features likely to contain biosignatures, it is useful to focus on the reliable identification of specific habitats with high biosignature preservation potential. Proposed chloride deposits on Mars may represent evaporitic environments conducive to the preservation of biosignatures. Analogous chloride- bearing, salt-encrusted playas (salars) are a habitat for life in the driest parts of the Atacama Desert, and are also environments with a taphonomic window. The specific geologic features that harbor and preserve microorganisms in Atacama salars are sub- meter to meter scale salt protuberances, or halite nodules. This study focuses on the ability to recognize and map halite nodules using images acquired from an unmanned aerial vehicle (UAV) at spatial resolutions ranging from mm/pixel to that of the highest resolution orbital images available for Mars.
Prognostics is the science of predicting the failure(s) of a component or a system and understanding how the performance will change in the event of a failure or degradation mechanism. With accurate predictions of possible failures, autonomous mitigative actions can be taken to correct/repair any issues or alert human operators of a failure threshold exceedance requiring condition-based maintenance. Although there is extensive research on failure predictions for a component or a system, there are significantly more opportunities to foray into failure predictions and prognostics for a system of systems such as an airspace consisting of multiple aircraft, a fleet of unmanned aerial vehicles, and a swarm of intelligent satellite systems. Failure prediction and mitigation are particularly important in autonomous systems such as satellite swarm systems that need effective resource management and minimal human interactions. Based on NASA's decadal survey, there is a clear need to prioritize the development of satellite swarm technology for studies of space physics and Earth science. The science community will propose future missions that return in-situ measurements from a 3-D (three-dimensional) volume of space, with relative spacecraft motion and inter-satellite baselines controlled according to the mission objectives. For such multi-spacecraft missions, it is required that ground operations resources do not scale with the number of satellites, thus compromising the swarm or leading to inefficiencies in resource allocation. Swarms of tens or hundreds of small satellites will require autonomy in attitude control, navigation and failure. Although significant research has been conducted in the areas of autonomous formation flying algorithms, less attention has been given to the development of resilient systems robust to failures.The focus of this research paper is the integration of model-based prognostics into the swarm dynamics control and decision-making algorithms. We simulate swarm management strategies for a subsystem failure to demonstrate the importance of failure predictions by comparing two cases: (i) no health information is provided to the system and utilized in the decision-making process and (2) system health information is obtained using prognostics and employed by the control system. One example scenario presented is for the GPS (Global Positioning System) system of an individual satellite to perform off-nominally due to increasing estimated error. In this scenario, the keep-out zone for that satellite would become more conservative, thereby decreasing the risk of collision. This is achieved via tuning the individual artificial repulsive functions assigned to each satellite.This paper is structured as follows. First we provide an overview of current swarm technology development, where we specifically use the term swarm to define multiple satellites flying in formation in similar orbits, with cross-link communication and station-keeping capabilities. Second, we give an introduction to the Swarm Orbital Dynamics Advisor (SODA), a tool that accepts high-level configuration commands and provides the orbital maneuvers required to achieve the prescribed formation configuration. Third, we provide the details of the model-based prognostics algorithm implementation in SODA. Finally, we present different case studies for potential component/subsystem failures and the swarm responses based with and without failure prediction information.
In this paper the problem of building trust in the online safety prediction of an fixed wing small electric unmanned aerial vehicles (e-UAV) for remaining flying time is addressed. A series of flight tests are described to verify the performance of the remaining flying time prediction algorithm. The estimate of remaining flying time is used to activate an alarm when the predicted remaining time falls below a threshold of two minutes. This updates the pilot to transition to the landing sequence of the flight profile. A second alarm is activated when the battery state of charge (SOC) falls below a specified safety limit threshold. This SOC threshold is the point at which the battery energy reserve would no longer safely support enough aborted landing attempts. During the test flights, the motor system is operated with the same predefined timed airspeed profile for each test. To test the robustness of the developed prediction algorithm, partial tests were performed with and remaining were performed without a simulated power train fault. To simulate a partial power train fault in the e-UAV the pilot engages a resistor bank at a specified time during the test flight. The flying time prediction system is agnostic of the pilot's activation of the fault and must adapt to the vehicle's state. The time at which the limit threshold on battery SOC is reached, it is then used to measure the accuracy of the remaining flying time predictions. This is demonstrated through comparing results from two battery models being developed. Accuracy requirements for the alarms are considered and the results discussed.
The concepts of unmanned aircraft system traffic management (UTM) and urban air mobility (UAM) are introducing high-density operations in low altitude airspace in closer proximity to populated areas than conventional high-altitude air traffic. The Flexible engine for Fast-time Evaluation of Flight Environments (Fe (sup 3)) provides the capability of statistically analyzing the high-density, high-fidelity, and low-altitude traffic system under numerous scenarios, such that stake holders can study impacts of factors in the low-altitude high-density traffic system and define requirements, policies, and protocols needed to support a safe yet efficient traffic system, and even assess operational risks and optimize flight schedules without conducting infeasible and cost-prohibitive flight tests that involve a large volume of aerial vehicles. This work provides an introduction to this simulation tool including its architecture and various models involved. Its performance and sample application in UAM and UTM are also presented.
The concepts of unmanned aircraft system traffic management (UTM) and urban air mobility (UAM) are introducing high-density operations in low altitude airspace in closer proximity to populated areas than conventional high-altitude air traffic. The Flexible engine for Fast-time Evaluation of Flight Environments (Fe (sup 3)) provides the capability of statistically analyzing the high-density, high-fidelity, and low-altitude traffic system under numerous scenarios, such that stake holders can study impacts of factors in the low-altitude high-density traffic system and define requirements, policies, and protocols needed to support a safe yet efficient traffic system, and even assess operational risks and optimize flight schedules without conducting infeasible and cost-prohibitive flight tests that involve a large volume of aerial vehicles. This work provides an introduction to this simulation tool including its architecture and various models involved. Its performance and sample application in UAM and UTM are also presented.
This report details test and measurement flights to demonstrate autonomous UAV (Unmanned Aerial Vehicle) inspection of high-voltage electrical transmission structures. A UAV built with commercial, off-the-shelf hardware and software, supplemented with custom sensors and logging software, measured ultraviolet (UV) emissions from a test generator placed on a low-altitude substation and a medium-altitude switching tower. Since corona discharge precedes catastrophic electrical faults on high-voltage structures, detection and geolocation of ultraviolet emissions is needed to develop a UAV-based self-diagnosing power grid. Signal readings from an onboard ultraviolet sensor were validated during flight with a commercial corona camera. Geolocation was accomplished with onboard GPS; the UAV position was logged to a local ground station and transmitted in real time to a NASA server for tracking in the national airspace. The method has practicality and relevance but not adequacy; either improved UAV position determination technology or increased sensor range is needed to enable broad deployment of this method.
This project is to develop antennas which enable beyond line of sight (BLOS) command and control for UAVs (Unmanned Aerial Vehicles). We will take advantage of newly assigned provisional Ku-band spectrum for UAVs and use unique antenna designs to avoid interference with ground systems. This will involve designing antennas with high isotropic effective radiated power (EIRP) and ultra-low sidelobes. The antennas will be made with polymer aerogel as a substrate to both reduce weight and improve performance, as demonstrated in an Aero Seedling. In addition, designing the antennas to be conformal to the aircraft fuselage will reduce drag.
An evacuated or vacuum airship relies on the same principle of buoyancy used by standard balloons. However, unlike a balloon which uses a lighter than air gas to displace air and provide lift, the vacuum airship leverages a rigid structure to maintain a vacuum and displace air, thereby providing buoyancy. This method is similar to how a ship uses a rigid structure to displace water and fill the space with air; an evacuated airship uses the same mechanism, except air is displaced and the space remains vacant. Using this method, the evacuated airship is capable of utilizing the full potential of the displaced mass of air, which has interesting implications in the Martian atmosphere. Unlike other aerial vehicles, which are at a disadvantage in Martian atmospheric conditions, the evacuated airship benefits from the Martian atmosphere by virtue of the temperature and molecular composition. As a result, the evacuated airship offers an unprecedented payload capacity and, if implemented, may be used to transport current and future scientific instruments, other vehicles, rovers, and possibly even human habitations. A standard dirigible or balloon for Mars would have a severely limited span of operation and a very narrow field of study, nearly exclusively the atmosphere, but a vacuum airship can be used as a long term tool for many different missions: transportation, ground study, communications, atmospheric study, etcetera, thereby making it a far more economically sensible choice
Dr. Ved Chirayath's plenary presentation will highlight two instrument technologies he invented at NASA including Fluid Lensing, the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions, and MiDAR (Multispectral Imaging, Detection and Active Reflectance), a next-generation active hyperspectral remote sensing and optical communications instrument. Fluid Lensing has been used to provide the first 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft, and underwater remotely operated vehicles (ROVs) as a new method to remotely sense living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an object's non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Finally, Chirayath will present preliminary results from NASA NeMO-Net (Neural Multi-Modal Observation and Training Network), the first neural network for global coral reef classification using fluid lensing and MiDAR.