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At least 19 records
All sourcesNASA NTRSDOE OSTI
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 NTRS2007

Feasibility of Turing-Style Tests for Autonomous Aerial Vehicle "Intelligence"

A new approach is suggested to define and evaluate key metrics as to autonomous aerial vehicle performance. This approach entails the conceptual definition of a "Turing Test" for UAVs. Such a "UAV Turing test" would be conducted by means of mission simulations and/or tailored flight demonstrations of vehicles under the guidance of their autonomous system software. These autonomous vehicle mission simulations and flight demonstrations would also have to be benchmarked against missions "flown" with pilots/human-operators in the loop. In turn, scoring criteria for such testing could be based upon both quantitative mission success metrics (unique to each mission) and by turning to analog "handling quality" metrics similar to the well-known Cooper-Harper pilot ratings used for manned aircraft. Autonomous aerial vehicles would be considered to have successfully passed this "UAV Turing Test" if the aggregate mission success metrics and handling qualities for the autonomous aerial vehicle matched or exceeded the equivalent metrics for missions conducted with pilots/human-operators in the loop. Alternatively, an independent, knowledgeable observer could provide the "UAV Turing Test" ratings of whether a vehicle is autonomous or "piloted." This observer ideally would, in the more sophisticated mission simulations, also have the enhanced capability of being able to override the scripted mission scenario and instigate failure modes and change of flight profile/plans. If a majority of mission tasks are rated as "piloted" by the observer, when in reality the vehicle/simulation is fully- or semi- autonomously controlled, then the vehicle/simulation "passes" the "UAV Turing Test." In this regards, this second "UAV Turing Test" approach is more consistent with Turing s original "imitation game" proposal. The overall feasibility, and important considerations and limitations, of such an approach for judging/evaluating autonomous aerial vehicle "intelligence" will be discussed from a theoretical perspective.

Young, Larry A.↗
NASA NTRS2003

Actions, Observations, and Decision-Making: Biologically Inspired Strategies for Autonomous Aerial Vehicles

This paper details the development and demonstration of an autonomous aerial vehicle embodying search and find mission planning and execution srrategies inspired by foraging behaviors found in biology. It begins by describing key characteristics required by an aeria! explorer to support science and planetary exploration goals, and illustrates these through a hypothetical mission profile. It next outlines a conceptual bio- inspired search and find autonomy architecture that implements observations, decisions, and actions through an "ecology" of producer, consumer, and decomposer agents. Moving from concepts to development activities, it then presents the results of mission representative UAV aerial surveys at a Mars analog site. It next describes hardware and software enhancements made to a commercial small fixed-wing UAV system, which inc!nde a ncw dpvelopnent architecture that also provides hardware in the loop simulation capability. After presenting the results of simulated and actual flights of bioinspired flight algorithms, it concludes with a discussion of future development to include an expansion of system capabilities and field science support.

Pisanich, Greg↗
NASA NTRS2020

Autonomous Aerial Vehicles (AAVs) as a Tool for Improving the Spatial Resolution of Snow Albedo Measurements in Mountainous Regions

We present technical advances and methods to measure effective broadband physical albedo in snowy mountain headwaters using a prototype dual-sensor pyranometer mounted on an Autonomous Aerial Vehicle (an AAV). Our test flights over snowy meadows and forested areas performed well during both clear sky and snowy/windy conditions at an elevation of ~2650 m above mean sea level (MSL). Our AAV-pyranometer platform provided high spatial (m) and temporal resolution (sec) measurements of effective broadband (310–2700 nm) surface albedo. The AAV-based measurements reveal spatially explicit changes in landscape albedo that are not present in concurrent satellite measurements from Landsat and MODIS due to a higher spatial resolution. This AAV capability is needed for validation of satellite snow albedo products, especially over variable montane landscapes at spatial scales of critical importance to hydrological applications. Effectively measuring albedo is important, as annually the seasonal accumulation and melt of mountain snowpack represent a dramatic transformation of Earth’s albedo, which directly affects headwaters’ water and energy cycles.

albedo↗
NASA NTRS2003

Holarchical Systems and Emotional Holons : Biologically-Inspired System Designs for Control of Autonomous Aerial Vehicles

The BEES (Bio-inspired Engineering for Exploration Systems) for Mars project at NASA Ames Research Center has the goal of developing bio-inspired flight control strategies to enable aerial explorers for Mars scientific investigations. This paper presents a summary of our ongoing research into biologically inspired system designs for control of unmanned autonomous aerial vehicle communities for Mars exploration. First, we present cooperative design considerations for robotic explorers based on the holarchical nature of biological systems and communities. Second, an outline of an architecture for cognitive decision making and control of individual robotic explorers is presented, modeled after the emotional nervous system of cognitive biological systems. Keywords: Holarchy, Biologically Inspired, Emotional UAV Flight Control

Ippolito, Corey↗
NASA NTRS2001

Exploration of Titan Using Vertical Lift Aerial Vehicles

Autonomous vertical lift aerial vehicles (such as rotorcraft or powered-lift vehicles) hold considerable potential for supporting planetary science and exploration missions. Vertical lift aerial vehicles would have the following advantages/attributes for planetary exploration: (1) low-speed and low-altitude detailed aerial surveys; (2) remote-site sample return to lander platforms; (3) precision placement of scientific probes; (4) soft landing capability for vehicle reuse (multiple flights) and remote-site monitoring; (5) greater range, speed, and access to hazardous terrain than a surface rover; and (6) greater resolution of surface details than an orbiter or balloons. Exploration of Titan presents an excellent opportunity for the development and usage of such vehicles. Additional information is contained in the original extended abstract.

Young, L. A.↗
NASA NTRS2001

Exploration of Titan using Vertical Lift Aerial Vehicles

Autonomous vertical lift aerial vehicles (such as rotorcraft or powered-lift vehicles) hold considerable potential for supporting planetary science and exploration missions. Vertical lift aerial vehicles would have the following advantages/attributes for planetary exploration: low-speed and low-altitude detailed aerial surveys; remote-site sample return to lander platforms; precision placement of scientific probes; soft landing capability for vehicle reuse (multiple flights) and remote-site monitoring; greater range, speed, and access to hazardous terrain than a surface rover; greater resolution of surface details than an orbiter or balloons. Exploration of Titan presents an excellent opportunity for the development and usage of such vehicles.

Young, L. A.↗
NASA NTRS2007

Automated Target Planning for FUSE Using the SOVA Algorithm

The SOVA algorithm was originally developed under the Resilient Systems and Operations Project of the Engineering for Complex Systems Program from NASA s Aerospace Technology Enterprise as a conceptual framework to support real-time autonomous system mission and contingency management. The algorithm and its software implementation were formulated for generic application to autonomous flight vehicle systems, and its efficacy was demonstrated by simulation within the problem domain of Unmanned Aerial Vehicle autonomous flight management. The approach itself is based upon the precept that autonomous decision making for a very complex system can be made tractable by distillation of the system state to a manageable set of strategic objectives (e.g. maintain power margin, maintain mission timeline, and et cetera), which if attended to, will result in a favorable outcome. From any given starting point, the attainability of the end-states resulting from a set of candidate decisions is assessed by propagating a system model forward in time while qualitatively mapping simulated states into margins on strategic objectives using fuzzy inference systems. The expected return value of each candidate decision is evaluated as the product of the assigned value of the end-state with the assessed attainability of the end-state. The candidate decision yielding the highest expected return value is selected for implementation; thus, the approach provides a software framework for intelligent autonomous risk management. The name adopted for the technique incorporates its essential elements: Strategic Objective Valuation and Attainability (SOVA). Maximum value of the approach is realized for systems where human intervention is unavailable in the timeframe within which critical control decisions must be made. The Far Ultraviolet Spectroscopic Explorer (FUSE) satellite, launched in 1999, has been collecting science data for eight years.[1] At its beginning of life, FUSE had six gyros in two IRUs and four reaction wheels. Over time through various failures, the satellite has been left with one reaction wheel on the vehicle skew axis and two gyros. To remain operational, a control scheme has been implemented using the magnetic torque rods and the remaining momentum wheel.[2] As a consequence, there are attitude regions where there is insufficient torque authority to overcome environmental disturbances (e.g. gravity gradient torques). The situation is further complicated by the fact that these attitude regions shift inertially with time as the spacecraft moves through earth s magnetic field during the course of its orbit. Under these conditions, the burden of planning targets and target-to-target slew maneuvers has increased significantly since the beginning of the mission.[3] Individual targets must be selected so that the magnetic field remains roughly aligned with the skew wheel axis to provide enough control authority to the other two orthogonal axes. If the field moves too far away from the skew axis, the lack of control authority allows environmental torques to pull the satellite away from the target and can potentially cause it to tumble. Slew maneuver planning must factor the stability of targets at the beginning and end, and the torque authority at all points along the slew. Due to the time varying magnetic field geometry relative to any two inertial targets, small modifications in slew maneuver timing can make large differences in the achievability of a maneuver.

Heatwole, Scott↗
NASA NTRSDate not supplied

High Resolution Terrain Sensing Lidar for Precision Navigation and Safe Landing of Space and Aerial Vehicles

A 3-D imaging flash lidar sensor employing a resolution enhancement algorithm is being developed at NASA Langley Research Center for providing Terrain Relative Navigation and Hazard Avoidance capabilities onboard spacecraft landing on the Moon, Mars, and other planetary bodies. This lidar sensor, we refer to as Terrain Sensing Lidar (TSL), is a solution for future missions that require landing at pre-designated sites near high value resources or at areas of high scientific value, while avoiding hazardous terrain features, such as escarpments, craters, slopes, and rocks, or pre-deployed assets. TSL can also benefit terrestrial applications such as autonomous aerial vehicles without reliance on signals from Global Positioning System (GPS). The feasibility of the TSL concept has been shown through a series of drone, fixed-wing aircraft, and helicopter flight tests. A prototype version of the TSL has been recently assembled for conducting another set of flight tests to demonstrate its readiness for upcoming landing missions. This paper describes the TSL, provides its performance parameters, and explains its operational concepts for landing missions.

Precision Navigation↗
NASA NTRS2017

Coastal California's Fog as a Unique Habitable Niche: Design for Autonomous Sampling and Preliminary Aerobiological Characterization

Just as on the land or in the ocean, atmospheric regions may be more or less hospitable to life. The aerobiosphere, or collection of living things in Earth's atmosphere, is poorly understood due to the small number and ad hoc nature of samples studied. However, we know viable airborne microbes play important roles, such as providing cloud condensation nuclei. Knowing the distribution of such microorganisms and how their activity can alter water, carbon, and other geochemical cycles is key to developing criteria for planetary habitability, particularly for potential habitats with wet atmospheres but little stable surface water. Coastal California has regular, dense fog known to play a major transport role in the local ecosystem. In addition to the significant local (1 km) geographical variation in typical fog, previous studies have found that changes in height above surface of as little as a few meters can yield significant differences in typical concentrations, populations and residence times. No single current sampling platform (ground-based impactors, towers, balloons, aircraft) is capable of accessing all of these regions of interest.A novel passive fog and cloud water sampler, consisting of a lightweight passive impactor suspended from autonomous aerial vehicles (UAVs), is being developed to allow 4D point sampling within a single fog bank, allowing closer study of small-scale (100 m) system dynamics. Fog and cloud droplet water samples from low-altitude aircraft flights in nearby coastal waters were collected and assayed to estimate the required sample volumes, flight times, and sensitivity thresholds of the system under design.125 cloud water samples were collected from 16 flights of the Center for Interdisciplinary Remotely Piloted Aircraft Studies (CIRPAS) instrumented Twin Otter, equipped with a sampling tube collector, occurring between 18 July and 12 August 2016 below 1 km altitude off the central coast. The collector was flushed first with 70 ethanol, then with sterile DI water, between sampling regions. Collected volumes ranged from 100 L to 12 mL. All samples were diluted serially and plated on two different types of agar, nutrient-dense (PCA) and sparse (R-2A). Plates were incubated at room temperature and counted when colonies first appeared and again at 2 weeks.Preliminary results from seven flights are consistent with generally reported colony-forming unit (CFU) values for terrestrial fog water (e.g., [4]). The PCA assay ranged from 400 to 125,000 CFU/mL, R-2A from 700 to 130,000 CFU/mL. PCA and R-2A counts were not significantly different from each other at I^ plus or minus +/- 0.05, although observationally, the R2A plates had more pigmented colonies. CFU counts from the majority of flights were not different from each other in mean at the same level of significance, but about half differed in median, indicating differences in underlying distribution. These results validate the presence of viable microorganisms in coastal California fog at levels that should be easily detectable by our sampling system. The indicated distribution differences underscore the need for small-scale, long-term sampling surveys. Future planned work includes ion chromatography for limiting nutrients, ATP quantification, and qPCR for several microbial classes of interest.

Coastal↗
DOE OSTI2024

Data collected using small uncrewed aircraft systems during the TRacking Aerosol Convection interactions ExpeRiment (TRACER)

Abstract. The main goal of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) project was to further understand the role that regional circulations and aerosol loading play in the convective cloud life cycle across the greater Houston, Texas, area. To accomplish this goal, the United States Department of Energy and research partners collaborated to deploy atmospheric observing systems across the region. Cloud and precipitation radars, radiosondes, and air quality sensors captured atmospheric and cloud characteristics. A dense lower-atmospheric dataset was developed using ground-based remote sensors, a tethersonde, and uncrewed aerial systems (UASs). TRACER-UAS is a subproject that deployed two UAS platforms to gather high-resolution observations in the lower atmosphere between 1 June and 30 September 2022. The University of Oklahoma CopterSonde and the University of Colorado Boulder RAAVEN (Robust Autonomous Aerial Vehicle – Endurant Nimble) were flown at two coastal locations between the Gulf of Mexico and Houston. The University of Colorado Boulder RAAVEN gathered measurements of atmospheric thermodynamic state, winds and turbulence, and aerosol size distribution. Meanwhile, the University of Oklahoma CopterSonde system operated on a regular basis to resolve the vertical structure of the thermodynamic and kinematic state. Together, a complementary dataset of over 200 flight hours across 61 d was generated, and data from each platform proved to be in strong agreement. In this paper, the platforms and respective data collection and processing are described. The dataset described herein provides information on boundary layer evolution, the sea breeze circulation, conditions prior to and nearby deep convection, and the vertical structure and evolution of aerosols. The quality-controlled TRACER-UAS observations from the CopterSonde and RAAVEN can be found at https://doi.org/10.5439/1969004 (Lappin, 2023) and https://doi.org/10.5439/1985470 (de Boer, 2023), respectively.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

Aerial Explorers and Robotic Ecosystems

A unique bio-inspired approach to autonomous aerial vehicle, a.k.a. aerial explorer technology is discussed. The work is focused on defining and studying aerial explorer mission concepts, both as an individual robotic system and as a member of a small robotic "ecosystem." Members of this robotic ecosystem include the aerial explorer, air-deployed sensors and robotic symbiotes, and other assets such as rovers, landers, and orbiters.

Young, Larry A.↗
NASA NTRS2015

Reinforcement Learning with Autonomous Small Unmanned Aerial Vehicles in Cluttered Environments

We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilot's flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences.

Tran, Loc↗