Results for “UAS (Unmanned Aircraft Systems)” — Page 8

NASA NTRSDate not supplied

What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition

Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported in SAFECOM. The custom NER model is built by fine-tuning an existing (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from any failure-relevant text. Similar mishaps are clustered and reported as single rows within the FMEA. For each cluster, frequency, severity, and overall risk are computed. The methodology can be applied as part of a broader safety management system to track trends in mishaps and discover knowledge that can be utilized to improve safety outcomes and system performance.

Machine Learning↗

NASA NTRSDate not supplied

What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition

Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, aviation incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts entities relevant to safety analysts. The custom NER model is built by fine-tuning an existing Bidirectional Encoder Representations from Transformers (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from failure-relevant text. This model performs passably, with a weighted average f1 score of 0.33 across entity types, indicating more labeled training data is needed. Extracted entities are used to form a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported using the SAFECOM system. Similar mishaps are manually clustered and reported as single rows within an FMEA. Foreach cluster, we compute frequency, severity, and overall riskin accordance with FAA standards. This methodology can beapplied as part of a broader safety management system totrack trends in mishaps (e.g., likelihood, severity) and discoverknowledge (e.g., causes, effects) that can be utilized to improvesafety outcomes and system performance.

Machine Learning↗

NASA NTRSDate not supplied

What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition

Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported in SAFECOM. The custom NER model is built by fine-tuning an existing (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from any failure-relevant text. Similar mishaps are clustered and reported as single rows within the FMEA. For each cluster, frequency, severity, and overall risk are computed. The methodology can be applied as part of a broader safety management system to track trends in mishaps and discover knowledge that can be utilized to improve safety outcomes and system performance.

Machine Learning↗

NASA NTRS2016

Unmanned Aircraft Systems Human-in-the-Loop Controller and Pilot Acceptability Study: Collision Avoidance, Self-Separation, and Alerting Times (CASSAT)

The Federal Aviation Administration (FAA) has been mandated by the Congressional funding bill of 2012 to open the National Airspace System (NAS) to Unmanned Aircraft Systems (UAS). With the growing use of unmanned systems, NASA has established a multi-center "UAS Integration in the NAS" Project, in collaboration with the FAA and industry, and is guiding its research efforts to look at and examine crucial safety concerns regarding the integration of UAS into the NAS. Key research efforts are addressing requirements for detect-and-avoid (DAA), self-separation (SS), and collision avoidance (CA) technologies. In one of a series of human-in-the-loop experiments, NASA Langley Research Center set up a study known as Collision Avoidance, Self-Separation, and Alerting Times (CASSAT). The first phase assessed active air traffic controller interactions with DAA systems and the second phase examined reactions to the DAA system and displays by UAS Pilots at a simulated ground control station (GCS). Analyses of the test results from Phase I and Phase II are presented in this paper. Results from the CASSAT study and previous human-in-the-loop experiments will play a crucial role in the FAA's establishment of rules, regulations, and procedures to safely, efficiently, and effectively integrate UAS into the NAS.

Comstock, James R., Jr.↗

NASA NTRS2019

Remote ID for Rapid Assessment of Flight and Vehicle Information

The ability to rapidly identify UAS in the field has emerged as a critical need for the integration of small UASs into the national airspace and counter-uas operations. This paper proposes an architecture for rapid retrieval of UAS information leveraging NASA's current Unmanned Aircraft System (UAS) Traffic Management (UTM) system. The proposed architecture utilizes UTM components: FIMS (Flight Information Management System), USS (UAS Service Supplier), and vehicle registration and model database in order to provide assessment of the UAS reported in the field including the ability to distinguish between participating and non- participating UTM actors. Detailed system descriptions are provided and preliminary results from field tests conducted during UTM TCL (Technical Capability Level) 3 are discussed. It is found that 94% of the remote ID look-ups were successful. The average time of a look-up is found to be 1.2 seconds. Failure cases are examined and recommendations on next steps to advance UAS remote identification are provided.

Ishihara, Abraham K.↗

NASA NTRS2019

Indoor Ground Testing of a Small UAS Sense and Avoid Airborne Doppler Radar

The National Aeronautics and Space Administration’s Unmanned Aircraft System (UAS) Traffic Management (UTM) project is researching prototype technologies needed to ensure safe integration of UAS operations into the National Airspace System (NAS). Within the UTM Concept of Operations, UAS would be equipped with on-board Sense and Avoid (SAA) technology to continually monitor for manned and unmanned aircraft in its vicinity while operating beyond visual line of sight in uncontrolled airspace. To support this effort, a candidate commercially available 24.5 GHz Doppler radar was selected and evaluated to determine if the technology could reliably support minimum requirements for SAA applications of small UAS (sUAS). Indoor ground tests were conducted inside the NASA Langley Research Center’s Experimental Test Range (ETR) from a stationary platform to evaluate the Doppler radar performance characteristics and gain operational proficiency before the radar was authorized to transmit outdoors. A high speed linear rail system was developed for the radar evaluation and was shown to be an effective method to generate Doppler radar targets of known radar cross section. The accuracy of the range and velocity reported by the radar was shown to be dependent on the Kalman filter state variance parameter settings. Antenna measurements were collected with the radar installed both on and off a sUAS to quantify the relative antenna gain, beam width and side lobe levels of the radar’s Metamaterial Electronically Scanning Array (MESA) antennas at boresight and extreme field of view pointing vectors. The relative antenna gain measured 2.6 dB lower at extreme field view angles compared to the boresight radiation pattern.

Szatkowski, George N.↗

NASA NTRS2021

Bell Unmanned Aircraft Systems Integration and Operationalization (SIO) Demonstration: Final Report, Summary of Research

National Aeronautics and Space Administration (NASA) initiated the Unmanned Aircraft Systems Integration and Operationalization (SIO) demonstration as a partnership between NASA and Industry with the goal of accelerating routine unmanned aircraft systems (UAS) operations in the national airspace (NAS). In order to accomplish this goal, NASA partnered separately with Bell and two other industry teams each pioneering the development, integration, and testing of their UAS, with the intent to make progress towards type certification. The program culminated in flight demonstrations representing future commercial operations by each partner.

Flight Demonstration↗

NASA NTRS2012

Varying Levels of Automation on UAS Operator Responses to Traffic Resolution Advisories in Civil Airspace

Continuing demand for the use of Unmanned Aircraft Systems (UAS) has put increasing pressure on operations in civil airspace. The need to fly UAS in the National Airspace System (NAS) in order to perform missions vital to national security and defense, emergency management, and science is increasing at a rapid pace. In order to ensure safe operations in the NAS, operators of unmanned aircraft, like those of manned aircraft, may be required to maintain separation assurance and avoid loss of separation with other aircraft while performing their mission tasks. This experiment investigated the effects of varying levels of automation on UAS operator performance and workload while responding to conflict resolution instructions provided by the Tactical Collision Avoidance System II (TCAS II) during a UAS mission in high-density airspace. The purpose of this study was not to investigate the safety of using TCAS II on UAS, but rather to examine the effect of automation on the ability of operators to respond to traffic collision alerts. Six licensed pilots were recruited to act as UAS operators for this study. Operators were instructed to follow a specified mission flight path, while maintaining radio contact with Air Traffic Control and responding to TCAS II resolution advisories. Operators flew four, 45 minute, experimental missions with four different levels of automation: Manual, Knobs, Management by Exception, and Fully Automated. All missions included TCAS II Resolution Advisories (RAs) that required operator attention and rerouting. Operator compliance and reaction time to RAs was measured, and post-run NASA-TLX ratings were collected to measure workload. Results showed significantly higher compliance rates, faster responses to TCAS II alerts, as well as less preemptive operator actions when higher levels of automation are implemented. Physical and Temporal ratings of workload were significantly higher in the Manual condition than in the Management by Exception and Fully Automated conditions.

Kenny, Caitlin↗

NASA NTRS2020

UAS Pilot Assessments of Display and Alerting for the Airborne Collision Avoidance System XU

Unmanned aircraft systems (UAS) must comply with specific standards to operate in the National Airspace System (NAS). Among the requirements are the detect and avoid (DAA) capabilities, which include display, alerting, and guidance specifications. Previous studies have queried pilots for their subjective feedback on these display elements on earlier systems; the present study sought pilot evaluations with an initial iteration of the unmanned variant of a Next Generation Airborne Collision Avoidance System (ACAS XU). Sixteen participants piloted simulated aircraft with both standalone and integrated DAA displays. Their opinions were gathered using post-block and post-simulation questionnaires as well as guided debriefs. The data showed pilots had better understanding and comfort with the system when using an integrated display. Pilots also rated ACAS Xu alerting and guidance as generally acceptable and effective. Implications for further development of ACAS XU and DAA displays are discussed.

UAS↗

NASA NTRS2011

NASA's UAS NAS Access Project

The vision of the Unmanned Aircraft System (UAS) Integration in the National Airspace System (NAS) Project is "A global transportation system which allows routine access for all classes of UAS." The goal of the UAS Integration in the NAS Project is to "contribute capabilities that reduce technical barriers related to the safety and operational challenges associated with enabling routine UAS access to the NAS." This goal will be accomplished through a two-phased approach based on development of system-level integration of key concepts, technologies and/or procedures, and demonstrations of integrated capabilities in an operationally relevant environment. Phase 1 will take place the first two years of the Project and Phase 2 will take place the following three years. The Phase 1 and 2 technical objectives are: Phase 1: Developing a gap analysis between current state of the art and the Next Generation Air Transportation System (NextGen) UAS Concept of Operations . Validating the key technical areas identified by this Project . Conducting initial modeling, simulation, and flight testing activities . Completing Sub-project Phase 1 deliverables (spectrum requirements, comparative analysis of certification methodologies, etc.) and continue Phase 2 preparation (infrastructure, tools, etc.) Phase 2: Providing regulators with a methodology for developing airworthiness requirements for UAS, and data to support development of certifications standards and regulatory guidance . Providing systems-level, integrated testing of concepts and/or capabilities that address barriers to routine access to the NAS. Through simulation and flight testing, address issues including separation assurance, communications requirements, and human systems integration in operationally relevant environments. The UAS in the NAS Project will demonstrate solutions in specific technology areas, which will address operational/safety issues related to UAS access to the NAS. Since the resource allocation for this Project is limited ($150M over the five years), the focus is on reducing the technical barriers where NASA has unique capabilities. As a result, technical areas, such as Sense and Avoid (SAA) and beyond line of sight command and control will not be addressed. While these are critical barriers to UAS access, currently, there is a great deal of global effort being exercised to address these challenge areas. Instead, specific technology development in areas where there is certainty that NASA can advance the research to high technology readiness levels will be the Project's focus. Specific sub-projects include Separation Assurance, Human Systems Integration, Communications, Certification, and Integrated Test and Evaluation. Each sub-project will transfer technologies to relevant key stakeholders and decision makers through research transition teams, technology forums, or through other analogous means.

Johnson, Charles W.↗

NASA NTRS2016

Crowd Sourcing Approach for UAS Communication Resource Demand Forecasting

Congressional attention to Unmanned Aircraft Systems (UAS) has caused the Federal Aviation Administration (FAA) to move the National Airspace System (NAS) Integration project forward, but using guidelines, practices and procedures that are yet to be fully integrated with the FAA Aviation Management System. The real drive for change in the NAS will to come from both UAS operators and the government jointly seeing an accurate forecast of UAS usage demand data. This solid forecast information would truly get the attention of planners. This requires not an aggregate demand, but rather a picture of how the demand is spread across small to large UAS, how it is spread across a wide range of missions, how it is expected over time and where, in terms of geospatial locations, will the demand appear. In 2012 the Volpe Center performed a study of the overall future demand for UAS. This was done by aggregate classes of aircraft types. However, the realistic expected demand will appear in clusters of aircraft activities grouped by similar missions on a smaller geographical footprint and then growing from those small cells. In general, there is not a demand forecast that is tightly coupled to the real purpose of the mission requirements (e.g. in terms of real locations and physical structures such as wind mills to inspect, farms to survey, pipelines to patrol, etc.). Being able to present a solid basis for the demand is crucial to getting the attention of investment, government and other fiscal planners. To this end, Mosaic ATM under NASA guidance is developing a crowd sourced, demand forecast engine that can draw forecast details from commercial and government users and vendors. These forecasts will be vetted by a governance panel and then provide for a sharable accurate set of projection data. Our paper describes the project and the technical approach we are using to design and create access for users to the forecast system.

air traffic↗

NASA NTRS2016

Effect of Vertical Rate Error on Recovery from Loss of Well Clear Between UAS and Non-Cooperative Intruders

When an Unmanned Aircraft System (UAS) encounters an intruder and is unable to maintain required temporal and spatial separation between the two vehicles, it is referred to as a loss of well-clear. In this state, the UAS must make its best attempt to regain separation while maximizing the minimum separation between itself and the intruder. When encountering a non-cooperative intruder (an aircraft operating under visual flight rules without ADS-B or an active transponder) the UAS must rely on the radar system to provide the intruders location, velocity, and heading information. As many UAS have limited climb and descent performance, vertical position andor vertical rate errors make it difficult to determine whether an intruder will pass above or below them. To account for that, there is a proposal by RTCA Special Committee 228 to prohibit guidance systems from providing vertical guidance to regain well-clear to UAS in an encounter with a non-cooperative intruder unless their radar system has vertical position error below 175 feet (95) and vertical velocity errors below 200 fpm (95). Two sets of fast-time parametric studies was conducted, each with 54000 pairwise encounters between a UAS and non-cooperative intruder to determine the suitability of offering vertical guidance to regain well clear to a UAS in the presence of radar sensor noise. The UAS was not allowed to maneuver until it received well-clear recovery guidance. The maximum severity of the loss of well-clear was logged and used as the primary indicator of the separation achieved by the UAS. One set of 54000 encounters allowed the UAS to maneuver either vertically or horizontally, while the second permitted horizontal maneuvers, only. Comparing the two data sets allowed researchers to see the effect of allowing vertical guidance to a UAS for a particular encounter and vertical rate error. Study results show there is a small reduction in the average severity of a loss of well-clear when vertical maneuvers are suppressed, for all vertical error rate thresholds examined. However, results also show that in roughly 35 of the encounters where a vertical maneuver was selected, forcing the UAS to do a horizontal maneuver instead increased the severity of the loss of well-clear for that encounter. Finally, results showed a small reduction in the number of severe losses of well-clear when the high performance UAS (2000 fpm climb and descent rate) was allowed to maneuver vertically, and the vertical rate error was below 500 fpm. Overall, the results show that using a single vertical rate threshold is not advisable, and that limiting a UAS to horizontal maneuvers when vertical rate errors are above 175 fpm can make a UAS less safe about a third of the time. It is suggested that the hard limit be removed, and system manufacturers instructed to account for their own UAS performance, as well as vertical rate error and encounter geometry, when determining whether or not to provide vertical guidance to regain well-clear.

detect and avoid↗

NASA NTRS2017

The Science of Drones

Drone: the public's term for any flying vehicle that doesn't have a pilot onboard. Unmanned aircraft system (UAS): preferred civil term that emphasizes the drone as a "system". Unmanned aerial vehicle (UAV): older but common term, especially in academia. Remotely piloted aircraft system (RPAS): the military's most common term for a drone, and probably the most accurate.

Drones↗

NASA NTRS2016

Systems and Methods for Collaboratively Controlling at Least One Aircraft

An unmanned vehicle management system includes an unmanned aircraft system (UAS) control station controlling one or more unmanned vehicles (UV), a collaborative routing system, and a communication network connecting the UAS and the collaborative routing system. The collaborative routing system being configured to receive flight parameters from an operator of the UAS control station and, based on the received flight parameters, automatically present the UAS control station with flight plan options to enable the operator to operate the UV in a defined airspace.

Estkowski, Regina I.↗

NASA NTRSDate not supplied

Comparison of Visual and LiDAR SLAM Algorithms using NASA Flight Test Data

Simultaneous Localization and Mapping (SLAM) is a promising technique that provides localization information and precise mapping of the physical environment without having much prior knowledge of the surroundings. SLAM may have a vital role in aeronautics and aerospace, where vehicles and aircraft must operate in complex environments with traditional localization services that may be degraded or unavailable. This paper compares several pre-canned 3D SLAM algorithms based on vision and LiDAR, namely ORB-SLAM, ORB-SLAM2, LOAM, A-LOAM, and F-LOAM on NASA UAS (Unmanned Aircraft System) flight test data. The NASA ARC UAS flight test demonstrates preliminary SLAM algorithm results, which serve as a stepping stone to simulated AAM (Advanced Air Mobility) concepts. Conducting AFRC UAS flight test for simulated AAM approach and landing with SLAM algorithms provides an Alternative Precision Navigation and Timing solution based on distributed landmarks and fiducials in the landing zone. These algorithms use the telemetry data as ground truth for a baseline comparison. The criteria of the performance comparison include robustness, accuracy, re-localization, response to environmental changes, and real-time effectiveness, which are currently qualitative but to be quantitative in the future.

computer vision↗

NASA NTRSDate not supplied

Comparison of Visual and LiDAR SLAM Algorithms using NASA Flight Test Data

Simultaneous Localization and Mapping (SLAM) is a promising technique that provides localization information and precise mapping of the physical environment without having much prior knowledge of the surroundings. SLAM may have a vital role in aeronautics and aerospace, where vehicles and aircraft must operate in complex environments with traditional localization services that may be degraded or unavailable. This paper compares several pre-canned 3D SLAM algorithms based on vision and LiDAR, namely ORB-SLAM, ORB-SLAM2, LOAM, A-LOAM, and F-LOAM on NASA UAS (Unmanned Aircraft System) flight test data. The NASA ARC UAS flight test demonstrates preliminary SLAM algorithm results, which serve as a stepping stone to simulated AAM (Advanced Air Mobility) concepts. Conducting AFRC UAS flight test for simulated AAM approach and landing with SLAM algorithms provides an Alternative Precision Navigation and Timing solution based on distributed landmarks and fiducials in the landing zone. These algorithms use the telemetry data as ground truth for a baseline comparison. The criteria of the performance comparison include robustness, accuracy, re-localization, response to environmental changes, and real-time effectiveness, which are currently qualitative but to be quantitative in the future.

computer vision↗

NASA NTRS2021

GA-ASI Final Report and Program Wrap-up for NASA System Integration and Operationalization (SIO)

The NASA SIO demonstration flight of April 3, 2020 represents a successful culmination of 18 months of coordinated effort between GA-ASI, NASA, the FAA, Collins Aerospace, and Honeywell Aerospace to operate a Medium Altitude, Long Endurance (MALE) Unmanned Aircraft System (UAS) safely in the National Airspace System (NAS) using industry leading prototype technologies. The GA-ASI team consisted of technical experts, program managers, engineers, mechanics, flight technicians, flight crews, and numerous other subject matter experts. This final report describes the most significant and potentially impactful aspects of the planning, integration, test, and flight aspects of this effort. GA-ASI successfully integrated the key technologies needed for UAS to fly in the NAS onto our prototype SkyGuardian UAS, which was designed to meet the most stringent airworthiness standards applicable to an aircraft of its size category. A proven Detect and Avoid (DAA) system, developed by GA-ASI and utilizing Honeywell Aerospace technology was integrated onto SkyGuardian for the first time, along with datalink radios from Collins Aerospace that meet the new civil standard for Control and Non-Payload Communication (CNPC) links. GA-ASI also obtained approvals from the FAA and FCC to operate the reconfigured UAS. The SIO demonstration flight represented a commercial aerial surveying operation conducted at medium altitude (>10,000ft above mean sea level). The aircraft’s onboard sensors were used to capture photographic, infrared and radar imagery of public and commercial infrastructure and land, and to subsequently produce the types of data products that would provide business value to potential customers. These survey services would supplement or replace services currently provided by manned airplanes and helicopters, small drones or satellites. A “virtual” mission was also planned, to show what additional survey data could have been captured during the flight if additional sensors had been installed on the aircraft’s external hardpoints. This revision of the final report focuses on information of value to the wider UAS community, and avoids propriety data to facilitate broad dissemination. It also includes a description of GA-ASI’s engagement with the FAA following the SIO flight, which led to the award of an updated Special Airworthiness Certificate in the Experimental Category (SAC-EC) and a Certificate of Waiver or Authorization (COA) allowing operation of the SkyGuardian UAS using its DAA system to satisfy right-of-way rules, instead of a chase plane. The associated operational limitations are described to illustrate the further steps that would be needed to remove those limitations for unhindered commercial operations.

Flight demonstration↗

NASA NTRSDate not supplied

UAS Pilot Assessments of Display and Alerting for the Airborne Collision Avoidance System XU

Unmanned aircraft systems (UAS) must comply with specific standards to operate in the National Airspace System (NAS). Among the requirements are the detect and avoid (DAA) capabilities, which include display, alerting, and guidance specifications. Previous studies have queried pilots for their subjective feedback on these display elements on earlier systems; the present study sought pilot evaluations with an initial iteration of the unmanned variant of a Next Generation Airborne Collision Avoidance System (ACAS XU). Sixteen participants piloted simulated aircraft with both standalone and integrated DAA displays. Their opinions were gathered using post-block and post-simulation questionnaires as well as guided debriefs. The data showed pilots had better understanding and comfort with the system when using an integrated display. Pilots also rated ACAS Xu alerting and guidance as generally acceptable and effective. Implications for further development of ACAS XU and DAA displays are discussed.

unmanned aircraft systems↗