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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Use of Unmanned Aerial Systems for Post-closure Waste Site Maintenance and Surveillance - 20143

The P- and R-Area Reactor buildings, located at SRS near Aiken, SC, were in-situ decommissioned by grouting below-grade portions of the buildings and demolishing some above-grade structures to grade level. Other reactor building structures were left above-grade and sealed to prevent human or animal access. Because the above grade-structures are expected to continue in their present state for hundreds of years, the condition of the building roofs is critical to mitigate rainwater intrusion. For this reason, building roof areas were strengthened with high strength concrete to ensure the long-term integrity of the roof structure. Periodic inspections of the roof structures are required to ensure that the roofs are functioning properly and to identify damage areas. The traditional method of inspecting closed reactor buildings requires the use of a helicopter and photographer to generate photographs and videos for review. This method proved sufficient but lacked the resolution and clarity that is required for a thorough inspection of the building structure. Fortunately, the Savannah River National Laboratory (SRNL) has established a Small Unmanned Aircraft System (sUAS) program using commercially available and custom-built remote-controlled aircraft. EC and ACP contacted the SRNL program to evaluate whether the sUAS technology could be employed for periodic reactor building inspections. The sUAS can fly within a few meters of the reactor buildings and hover, allowing for a more thorough aerial inspection. The initial sUAS inspection of the P-Area Reactor building was completed in February 2018 and at the R-Area Reactor building in August 2018 and were successful in providing higher resolution photos and videos. The sUAS inspections also revealed that vegetation had begun to grow on the roofs that could potentially damage the structure integrity. SRNL partnered with Virginia Polytechnic Institute and State University to build a custom heavy lift sUAS that could be equipped with herbicides to remotely treat the vegetative growth. Herbicides were successfully applied to the R-Area Reactor building roof using the custom sUAS in September 2018. The use of unmanned aircraft systems at SRS to perform aerial inspections and herbicide treatment in otherwise inaccessible areas has proven to be an efficient and cost-effective technology that provides high value, increases knowledge of facility conditions, and provides for early detection of damage. Periodic inspections of the reactor building roofs using sUAS technology are performed safely, efficiently, and at a significant cost and schedule savings while reducing emissions, noise, and fossil fuel use. The use of sUAS equipment for building inspections and herbicide application at SRS is unique within the DOE complex. The goal of SRS is to apply this remote technology to other waste unit operations and maintenance activities in the future. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2006

Networked Unmanned Aerial Vehicle Teams (NUAVT)

A partnership between the NASA Ames Research Center and the NASA Dryden Flight Research Center (DFRC) explored the ability of small unmanned aircraft to support forest fire fighting using teaming behavior. The Networked UAV Teams project flight tested mission planning algorithms for multi-UAV cooperative transit, area search, and waypoint time-of-arrival that might someday allow the early detection of developing forest fires and support the gathering of images and atmospheric samples to help improve predictions of the future behavior of established fires.

Ryan, Jack↗
NASA NTRSDate not supplied

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

In support of the Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project, a fleet of unmanned ground vehicles (UGVs) was developed as a test and evaluation (T\&E) platform to reduce system integration gaps between simulation and live flight hardware. While simulation and hardware-in-the-loop bench testing provide adequate environments for preliminary validation, differences in system deployment architecture, software interfaces, and hardware infrastructure increase the risks to safety, property, and the project. Given ATTRACTOR’s goal of establishing a basis of certification of trust and trustworthiness in multi-agent autonomous systems, bridging these gaps was critical to successful project execution and feasibility assessment. In this paper we present the UGV fleet and its role in speeding up system integration, smoothing the transition from simulation to flight, and providing researchers an easy-to-use hardware test bed. An overview of the hardware and software on-board the vehicles is provided along with supporting infrastructure. The system integration process is documented including results in supporting both the overarching design reference mission (DRM) of ATTRACTOR and individual research efforts conducted since the creation of the fleet. Finally, we discuss the practical lessons learned regarding the testing, deployment, and operation of multi-agent autonomous systems.

Matthew P. Vaughan↗
NASA NTRSDate not supplied

Computational Prediction of Broadband Noise from a Representative Unmanned Aerial System Rotor

This work details the application of a lattice-Boltzmann method–very-large-eddy simulation (LBM-VLES) employed by the software suite, PowerFLOW. This LBM-VLES simulation predicted the aeroacoustic noise emanating from a representative, small unmanned aircraft system rotor, namely, the DJI-9450 in a hover condition. Predicted total aerodynamic loading as well as 2D aerodynamic loading along discrete spanwise sections of a rotor blade were compared to lower fidelity predictions and experimental results acquired in the Structural Acoustic Loads and Transmission anechoic chamber facility at the NASA Langley Research Center. The total acoustic spectra were decomposed into tonal and broadband components, which showed that broadband noise was a dominant contributor above 1 kHz for this rotor. These data were then compared to experimentally acquired data, showing good agreement up to approximately 11 kHz. Above 11 kHz, however, a grid sensitivity study showed dependency of the highest resolvable frequency on the spatial resolution of the computational domain, explaining the roll off in predicted data. Individual broadband noise sources were further investigated by calculating one-third octave sound pressure levels of the unsteady pressure fluctuations acting on the rotor, providing evidence that blade self-noise was the prominent noise source. Using these results, blade wake interaction noise was seen to be negligible for this particular rotor, which was further validated by calculating blade vortex miss distances and comparing to theory.

Christopher S. Thurman↗
NASA NTRS2007

Ikhana: A NASA Unmanned Aerial System Supporting Long-Duration Earth Science Missions

This viewgraph presentation reviews Ikhana's project goals: (1) Develop an airborne platform to conduct Earth observation and atmospheric sampling science missions both nationally and internationally, (2) develop and demonstrate technologies that improve the capability of UAVs to conduct science collection missions, (3) develop technologies that improve manned and unmanned aircraft systems, and (4) support important national UAV development activities. The criteria that guided the selection of the aircraft are listed. The payload areas on Ikhana are shown and the network that connects the systems are also reviewed. The data recorder is shown. Also the diagram of the Airborne Research Test System (ARTS) is reviewed. The Mobile Ground Control Station and the Mobile Ku SatCom Antenna are also shown and described.

Cobleigh, Brent R.↗
DOE OSTI2022

Designing Unmanned Aerial Survey Monitoring Program to Assess Floating Litter Contamination

Monitoring marine contamination by floating litter can be particularly challenging since debris are continuously moving over a large spatial extent pushed by currents, waves, and winds. Floating litter contamination have mostly relied on opportunistic surveys from vessels, modeling and, more recently, remote sensing with spectral analysis. This study explores how a low-cost commercial unmanned aircraft system equipped with a high-resolution RGB camera can be used as an alternative to conduct floating litter surveys in coastal waters or from vessels. The study compares different processing and analytical strategies and discusses operational constraints. Collected UAS images were analyzed using three different approaches: (i) manual counting (MC), using visual inspection and image annotation with object counts as a baseline; (ii) pixel-based detection, an automated color analysis process to assess overall contamination; and (iii) machine learning (ML), automated object detection and identification using state-of-the-art convolutional neural network (CNNs). Our findings illustrate that MC still remains the most precise method for classifying different floating objects. ML still has a heterogeneous performance in correctly identifying different classes of floating litter; however, it demonstrates promising results in detecting floating items, which can be leveraged to scale up monitoring efforts and be used in automated analysis of large sets of imagery to assess relative floating litter contamination.

Almeida, Sílvia (ORCID:0000000320636826)↗
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 NTRS2015

Unmanned Aerial Systems (UAS): Evolving Trends

Near-term Goal: Enable initial low-altitude airspace and UAS operations with demonstrated safety as early as possible, within 5 years; Long-term Goal: Accommodate increased UAS operations with highest safety, efficiency, and capacity as much autonomously as possible (10-15 years).

UAS↗
NASA NTRS2014

Enabling Civilian Low-Altitude Airspace and Unmanned Aerial System (UAS) Operations

UAS operations will be safer if a UTM system is available to support the functions associated with Airspace management and geo-fencing (reduce risk of accidents, impact to other operations, and community concerns); Weather and severe wind integration (avoid severe weather areas based on prediction); Predict and manage congestion (mission safety);Terrain and man-made objects database and avoidance; Maintain safe separation (mission safety and assurance of other assets); Allow only authenticated operations (avoid unauthorized airspace use).

Unmanned↗