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NASA NTRSDate not supplied

Automated Management of Small Unmanned Aircraft System Communications and Navigation Contingency

The number of small Unmanned Aircraft System (sUAS) operating in the low-altitude of the National Airspace System (NAS) has been rapidly increasing in the past few years and this number is expected to grow in the future. However, aside from a few special cases, all sUAS must fly within visual line-of-sight (VLOS) of their operators and this limitation is blocking highly anticipated beyond visual line-of-sight (BVLOS) sUAS applications such as package delivery from practice. To enable routine low-altitude BVLOS operations, there needs to be a traffic management ecosystem that complements the FAA’s Air Traffic Management (ATM) system, which does not provide air traffic services under 400 feet above ground level (AGL). NASA has been pioneering research and development of this ecosystem under UAS Traffic Management (UTM) project since 2015 in a series of Technical Capability Levels (TCL) activities that are increasingly complex. In TCL1, completed in 2015, visual line-of-sight operations such as agriculture, firefighting, and infrastructure monitoring were addressed with a focus on geofencing and operations scheduling. Technologies and requirements needed for BVLOS operations in sparsely populated areas were examined in TCL2 in 2016, and those for operations over moderately populated areas in TCL3 in 2017 and 2018. TCL4 is building on the earlier TCLs and focuses on technologies and requirements for operations in higher-density urban areas for tasks such as newsgathering and package delivery and for managing large-scale contingencies. To coordinate and facilitate the incremental implementation of the UTM ecosystem in the NAS, a Research Transition Team (RTT) has been formed between the FAA, NASA, and industry. The RTT is divided into four subgroups, concept and use case development, data exchange and information architecture, sense and avoid, and communications and navigation (C&N). This paper focuses on C&N subgroup activities, in particular about the development of automated sUAS communications and navigation contingency management. The goal of this development is to prepare sUAS to display predictable behavior while handling C&N off-nominal events. It is expected that the adoption of the presented automated contingency management by the sUAS community will accommodate and inform rulemaking towards governing low-altitude BVLOS operations.

Unmanned Aviation System (UAS) Traffic Management↗
NASA NTRSDate not supplied

Automated Management of Small Unmanned Aircraft System (UAS) Communications and Navigation Contingency

The number of small Unmanned Aircraft System (sUAS) operating in the low-altitude of the National Airspace System (NAS) has been rapidly increasing in the past few years and this number is expected to grow in the future. However, aside from a few special cases, all sUAS must fly within visual line-of-sight (VLOS) of their operators and this limitation is blocking highly anticipated beyond visual line-of-sight (BVLOS) sUAS applications such as package delivery from practice. To enable routine low-altitude BVLOS operations, there needs to be a traffic management ecosystem that complements the FAA’s Air Traffic Management (ATM) system, which does not provide air traffic services under 400 feet above ground level (AGL). NASA has been pioneering research and development of this ecosystem under UAS Traffic Management (UTM) project since 2015 in a series of Technical Capability Levels (TCL) activities that are increasingly complex. In TCL1, completed in 2015, visual line-of-sight operations such as agriculture, firefighting, and infrastructure monitoring were addressed with a focus on geofencing and operations scheduling. Technologies and requirements needed for BVLOS operations in sparsely populated areas were examined in TCL2 in 2016, and those for operations over moderately populated areas in TCL3 in 2017 and 2018. TCL4 is building on the earlier TCLs and focuses on technologies and requirements for operations in higher-density urban areas for tasks such as newsgathering and package delivery and for managing large-scale contingencies. To coordinate and facilitate the incremental implementation of the UTM ecosystem in the NAS, a Research Transition Team (RTT) has been formed between the FAA, NASA, and industry. The RTT is divided into four subgroups, concept and use case development, data exchange and information architecture, sense and avoid, and communications and navigation (C&N). This paper focuses on C&N subgroup activities, in particular about the development of automated sUAS communications and navigation contingency management. The goal of this development is to prepare sUAS to display predictable behavior while handling C&N off-nominal events. It is expected that the adoption of the presented automated contingency management by the sUAS community will accommodate and inform rulemaking towards governing low-altitude BVLOS operations.

Unmanned Aviation Systems (UAS) Traffic Management↗
NASA NTRS2021

Detection and Tracking of Aircraft in the Far-Field from Small Unmanned Aerial Systems

Onboard far-field aircraft detection is needed for safe non-cooperative traffic mitigation in autonomous small Unmanned Aerial System (sUAS) operations. Machine vision systems, based on standard optics and visible light detectors, possess the ideal size, weight, and power (SWaP) requirements for sUAS. This work presents the design and analysis of a novel aircraft detection and tracking pipeline based on optical sensing alone. Key contributions of the work include a refined range inequality model based on sensing and detection with FAA well-clear separation assurance distances between aircraft in mind, a detector fusion method to maximize the benefit of two image detectors, and a comparative analysis of Linear Kalman-filtering and Extended Kalman-filtering to seek optimal tracking performance. The pipeline is evaluated offline against multiple intruder platforms, using two types of flight encounters: multirotor sUAS vs. fixed-wing sUAS and multirotor sUAS vs. general aviation(GA)plane. Analysis is restricted to the rate-limiting head-on and departing collision volume cases vertically separated for safety. Results indicate that it is feasible to use the proposed optical spatial-temporal tracking algorithm to provide adequate alerting time to prevent penetration of well-clear separation volumes for both sUAS and GA aircraft.

Unmanned Aerial System↗
NASA NTRS2017

Real-Time Risk Assessment Framework for Unmanned Aircraft System (UAS) Traffic Management (UTM)

The new Federal Aviation Administration (FAA) Small Unmanned Aircraft rule (Part 107) marks the first national regulations for commercial operation of small unmanned aircraft systems (sUAS) under 55 pounds within the National Airspace System (NAS). Although sUAS flights may not be performed beyond visual line-of-sight or over non- participant structures and people, safety of sUAS operations must still be maintained and tracked at all times. Moreover, future safety-critical operation of sUAS (e.g., for package delivery) are already being conceived and tested. NASA's Unmanned Aircraft System Trac Management (UTM) concept aims to facilitate the safe use of low-altitude airspace for sUAS operations. This paper introduces the UTM Risk Assessment Framework (URAF) which was developed to provide real-time safety evaluation and tracking capability within the UTM concept. The URAF uses Bayesian Belief Networks (BBNs) to propagate off -nominal condition probabilities based on real-time component failure indicators. This information is then used to assess the risk to people on the ground by calculating the potential impact area and the effects of the impact. The visual representation of the expected area of impact and the nominal risk level can assist operators and controllers with dynamic trajectory planning and execution. The URAF was applied to a case study to illustrate the concept.

Ancel, Ersin↗
NASA NTRSDate not supplied

Monocular Ranging for Small Unmanned Aerial Systems in the Far-Field

Recent proliferation of small Unmanned Aerial Systems (sUAS) applications requires onboard collision avoidance systems to mitigate the risk of collision with non-cooperative aircraft and manned aircraft, which may not see sUAS in time to perform an avoidance maneuver. An attractive avenue for onboard collision avoidance is the utilization of machine vision cameras due to their low size, weight and power (SWaP) requirements. In this paper, we characterize the range performance of a machine vision system developed in-house and mounted onto an sUAS. The technique was designed to estimate the performance of a sense-and-avoid system to ensure that the sensing components meet the well-clear requirements for the chosen platform and avoidance strategy. Experimental flight-test data was acquired from test-flights flown along multiple collision geometries for two intruders: a general Aviation (GA) aircraft and a fixed-wing sUAS. The ownship and both intruders were instrumented with inertial navigation systems (INS) recording position and attitude information. The range at first detection, 𝑹𝟎, was extracted from in-flight imagery of head-on collision course geometry synchronized with INS data from both aircraft and ground-truth values extracted from the raw imagery. This initial detection distance, 𝑹𝟎, scales with atmospheric attenuation. Therefore, under clear sky conditions, the derived 𝑹𝟎 value represents the upper bound on the detection range achievable by the test configuration of the detector. Results indicate that the maximum initial detection distance for a 4k resolution action camera fitted with a 41º Field of View (FOV) lens is 2.763 ± 0.037 km for a GA aircraft and 0.881 ± 0.061 km for a fixed-wing sUAS, respectively. The in results this study suggest that a vision-based detect and track system may be analyzed using the sensor characterization and contextualized within aircraft well-clear volumes.

Chester V. Dolph↗
NASA NTRS1996

Effects of Special Use Airspace on Economic Benefits of Direct Flights

A methodology for estimating the economic effects of Special Use Airspace (SUA) on direct route flights is presented in this paper. The methodology is based on evaluating operating costs of aircraft and analyzing the different ground-track distances traveled by flights under different air traffic scenarios. Using this methodology the following objectives are evaluated: optimistic bias of studies that assume accessible SUAs the maximum economic benefit of dynamic use of SUAs and the marginal economic benefit of the dynamic use of individual SUAs.

Datta, Koushik↗
NASA NTRS2016

Use of a Small Unmanned Aircraft System for Autonomous Fire Spotting at the Great Dismal Swamp

This paper describes the results of a set of experiments and analyses conducted to evaluate the capability of small unmanned aircraft systems (sUAS) to spot nascent fires in the Great Dismal Swamp (GDS) National Wildlife Refuge. This work is the result of a partnership between the National Aeronautics and Space Administration and the US Fish and Wildlife service specifically to investigate sUAS usage for fire-spotting. The objectives of the current effort were to: 1) Determine suitability and utility of low-cost Small Unmanned Aircraft Systems (sUAS) to detect nascent fires at GDS; 2) Identify and assess the necessary National Airspace System (NAS) integration issues; and 3) Provide information to GDS and the community on system requirements and concepts-of-operation (CONOPS) for conducting fire detection/support mission in the National Airspace and (4) Identify potential applications of intelligent autonomy that would enable or benefit this high-value mission. In addition, data on the ability of various low-cost sensors to detect smoke plumes and fire hot spots was generated during the experiments as well as identifying a path towards a future practical mission utility by using sUAS in beyond visual-line-of-sight operation in the National Airspace System (NAS).

Logan, Michael J.↗
NASA NTRS2017

High-Fidelity Multi-Rotor Unmanned Aircraft System Simulation Development for Trajectory Prediction Under Off-Nominal Flight Dynamics

The NASA Unmanned Aircraft System (UAS) Traffic Management (UTM) project is conducting research to enable civilian low-altitude airspace and UAS operations. A goal of this project is to develop probabilistic methods to quantify risk during failures and off nominal flight conditions. An important part of this effort is the reliable prediction of feasible trajectories during off-nominal events such as control failure, atmospheric upsets, or navigation anomalies that can cause large deviations from the intended flight path or extreme vehicle upsets beyond the normal flight envelope. Few examples of high-fidelity modeling and prediction of off-nominal behavior for small UAS (sUAS) vehicles exist, and modeling requirements for accurately predicting flight dynamics for out-of-envelope or failure conditions are essentially undefined. In addition, the broad range of sUAS aircraft configurations already being fielded presents a significant modeling challenge, as these vehicles are often very different from one another and are likely to possess dramatically different flight dynamics and resultant trajectories and may require different modeling approaches to capture off-nominal behavior. NASA has undertaken an extensive research effort to define sUAS flight dynamics modeling requirements and develop preliminary high fidelity six degree-of-freedom (6-DOF) simulations capable of more closely predicting off-nominal flight dynamics and trajectories. This research has included a literature review of existing sUAS modeling and simulation work as well as development of experimental testing methods to measure and model key components of propulsion, airframe and control characteristics. The ultimate objective of these efforts is to develop tools to support UTM risk analyses and for the real-time prediction of off-nominal trajectories for use in the UTM Risk Assessment Framework (URAF). This paper focuses on modeling and simulation efforts for a generic quad-rotor configuration typical of many commercial vehicles in use today. An overview of relevant off-nominal multi-rotor behaviors will be presented to define modeling goals and to identify the prediction capability lacking in simplified models of multi-rotor performance. A description of recent NASA wind tunnel testing of multi-rotor propulsion and airframe components will be presented illustrating important experimental and data acquisition methods, and a description of preliminary propulsion and airframe models will be presented. Lastly, examples of predicted off-nominal flight dynamics and trajectories from the simulation will be presented.

Foster, John V.↗
NASA NTRS2018

UTM and D-NET: NASA and JAXA's Collaborative Research on Integrating Small UAS with Disaster Response Efforts

Natural disasters, such as flooding, wildfire, hurricane, tornadoes, earthquakes and tsunamis, pose challenges in preserving human life and minimizing the damages to a region. During catastrophic events, timely response of disaster relief personnel, an efficient deployment of resources in the recovery effort, and coordinated information sharing amongst different relief agencies can make a substantial difference in responding to those impacted by the disaster. Many relief activities currently utilize both ground personnel and manned airborne assets during different phases of the disaster response. Typically, multiple organizations support relief activities and this often creates logistics coordination challenges between agencies which can result in wasted time or resources. The Japan Aerospace Exploration Agency (JAXA) has been developing an "Integrated aircraft operation system for disaster relief (D-NET)", which assists collection and sharing of disaster information through the integrated operation of aircraft such as helicopters, aircraft, and satellites, for efficient and safe rescue operations by disaster relief aircraft. Due to the advancement in unmanned aircraft systems (UAS) technologies, public safety organizations have started incorporating small UAS (sUAS) as an asset in their disasters response activities. To address the airspace integration challenges of the influx of sUAS in the United States the National Aeronautics and Space Administration (NASA), under the UAS Traffic Management (UTM) project, has been engaged in research to enable large-scale commercial applications of sUAS operating in low altitude airspace. This paper presents the integration of D-NET, which incorporate sUAS in the planning, information sharing, and operation support of disasters response activities, and UTM, which provides airspace management to enable large scale high density operations. The integration of the DNET and UTM systems enables coordination, data sharing, and airspace management to improve the timeliness of the disaster response, enable relief organization to reduce cost and overhead by using UAS assets and still maintain airspace safety during the relief activities.

UTM↗
NASA NTRS2017

A Fuzzy Logic Approach for Separation Assurance and Collision Avoidance for Unmanned Aerial Systems

In the coming years, operations in low altitude airspace will vastly increase as the capabilities and applications of small Unmanned Aerial Systems (sUAS) continue to multiply. Therefore, solutions to managing sUAS in highly congested airspace must be explored. In this study, a Fuzzy Logic based approach was used to help mitigate the risk of collisions between aircraft using separation assurance and collision avoidance techniques. The system was evaluated for its effectiveness to mitigate the risk of mid-air collisions between aircraft. This system utilizes only current state information and can resolve potential conflicts without knowledge of intruder intent. The avoidance logic was verified using formal methods and shown to select the correct action in all instances. Additionally, the Fuzzy Logic Controllers were shown to always turn the vehicles in the correct direction. Numerical testing demonstrated that the avoidance system was able to prevent a mid-air collision between two sUAS in all tested cases. Simulations were also performed in a three-dimensional environment with a heterogenous fleet of sUAS performing a variety of realistic missions. Simulations showed that the system was 99.98 effective at preventing mid-air collisions when separation assurance was disabled (unmitigated case) and 100 effective when enabled (mitigated case).

Verification & Validation of a Fuzzy Logic Control↗
NASA NTRS2019

An Improved Far-Field Small Unmanned Aerial System Optical Detection Algorithm

Onboard far-field aircraft detection is needed for safe non-cooperative traffic mitigation for autonomous small Unmanned Aerial System (sUAS) operations. This work presents an aircraft detect and track pipeline that fuses image differencing and morphological filtering detections for inputs to a Kalman-based object tracking pipeline. The pipeline is evaluated using two types of flight encounters: 1) motorcopter sUAS vs. fixed-wing sUAS 2) motorcopter sUAS vs general aviation plane.

Chester Dolph↗
NASA NTRSDate not supplied

Aircraft Classification Using Radar from Small Unmanned Aerial Systems for Scalable Traffic Management Emergency Response Operations

This work investigates two machine learning techniques: Support Vector Machine (SVM) and Autoencoders (AE)with SVM layer for classification of radar trajectories as General Aviation (GA), fixed-wing small Unmanned Aerial System (sUAS), or not-an-aircraft using radar data recorded from sUAS. Onboard identification of intruder aircraft type is useful for planning avoidance maneuvers and is necessary to provide autonomous systems to meet or exceed the avoidance capability of a human pilot. Aircraft classification can identify intruder aircraft that are not part of the team and may be violating a Temporary Flight Restriction. Aircraft classification is needed in monitoring an airspace where multiple aircraft are teaming on a shared task. Scalable Traffic Management for Emergency Response Operations (STEReO) is a NASA project aimed at improving disaster response by enabling large scale aircraft operations through the teaming of manned aircraft with sUAS to maximize emergency response resources. To this end, this work uses trajectories and radar derived features to classify aircraft from a multirotor sUAS. The AE + SVM generated the strongest classification overall accuracy of 93.5% using the first 4 seconds of radar track data for tracks that activated the avoidance system. Subsampling the available track data increased the available training data with the maximum aircraft recall of 0.94 achieved using the SVM with 1 second track data.

Chester V. Dolph↗
NASA NTRSDate not supplied

Distributed Vision Sensing of Small Uncrewed Aircraft Systems in Urban Traffic Corridors

The NASA Advanced Air Mobility mission will enable widespread low altitude passenger travel, cargo delivery, and a variety of public services through the development of Uncrewed Aerial Systems (UAS) operations. Ensuring safe, autonomous operations in densely populated environments requires careful consideration towards hazards including other aircraft, infrastructure, and evolving weather. Small Uncrewed Aerial Systems (SUAS) present a unique hazard to UAS operations as they share airspace and may be readily operated in a non-cooperative fashion. This work investigates distributed sensing of SUAS traversing an air traffic corridor in an urban setting. This work develops a distributed vision detect and track strategy at NASA Langley Research Center. Three nodes, each with at least one global shutter camera, are distributed around a traffic corridor to surveil flight operations for two SUAS performing low altitude flight operations. Each node is equipped with a GPS and cellular modem to enable timestamping and remote control of acquisition. Node one faces a traffic roundabout with buildings in the background and achieves 99% surveillance coverage for two SUAS against building and tree backgrounds at ranges 50 to 130m. The second node points down Langley Boulevard with trees and buildings in the background and achieves 99% coverage at separation distances between 70 and 180m. The analysis for the second node is limited to ranges below 180m due to low contrast against dark, tree backgrounds. Finally, the third node points down Langley Boulevard from another perspective and achieves 99% coverage at ranges 60m to 200m against mostly building with a few sections of trees in the background.

Chester V Dolph↗
NASA NTRSDate not supplied

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↗
NASA NTRSDate not supplied

Initial testing of the Uncrewed Aerial System Pilot Kit (UASP-kit) in Operational Settings

Pilots for small uncrewed aerial systems (sUAS) are at a disadvantage for building situation awareness of the remote airspace in which they are flying, simply because they are distant from their vehicles. A tool to provide increased air traffic situation awareness for an sUAS pilot is being developed. The UAS pilot kit, “UASP-kit,” is small and self-contained, with its chief capability being to collect and display Automatic Dependent Surveillance-Broadcast reports of local aircraft. UASP-kits were taken into the field, introduced to users during a training course, and then left with them for the summer fire season. sUAS pilots used the prototypes when it was appropriate throughout the summer. The UASP-kits were operational for a total of 79 flight-days. Users reported that the UASP-kits supported their situation awareness but also identified several usability issues. The findings contribute to validation of the UASP-kit, and support continuing the work to improve the tool and develop additional functionality.

STEReO↗
NASA NTRSDate not supplied

Initial testing of the Uncrewed Aerial System Pilot Kit (UASP-kit) in Operational Settings

Remote pilots for small uncrewed aerial systems (sUAS) are at a disadvantage for building situation awareness of the airspace in which they are flying, because of limited sight lines and distance from cell towers. A tool to provide increased air traffic situation awareness for an sUAS pilot is being developed. The “UASP-kit” is small and self-contained, with its chief capability being to collect and display Automatic Dependent Surveillance-Broadcast reports of local aircraft. UASP-kits were taken into the field, introduced to users during a training course, and then left with them for the summer fire season. sUAS pilots used the prototypes when it was appropriate throughout the summer. The UASP-kits were operational for a total of 79 flight-days. Users noted that the UASP-kits supported their situation awareness but also identified several usability issues. The findings contribute to validation of the UASP-kit, and support continuing the work to improve the tool and develop additional functionality.

STEReO↗
NASA NTRS2024

Testing of Advanced Capabilities to Enable In-time Safety Management and Assurance for Future Flight Operations

In order to refine an initial Concept of Operations, explore Concepts of Use, and expose/validate requirements for future In-Time Aviation Safety Management Systems (IASMS), testing architectures were created, along with a set of capabilities and underlying information exchange protocols. These systems were conceived and developed based on hazards associated with two envisioned urban area flight domains: (1) highly autonomous small uncrewed aerial systems (sUAS) operating at low altitudes, and (2) highly autonomous air taxis. The initial scope of this development is described in [1]; this report provides an update, focusing on the subsequent developments and test activities. As stated in [1], it is important to note that there are many capabilities already in use by the industry (or soon to be in use) that will play critical roles in future IASMS designs. Those reported here were developed to address a gap in the current state-of-the-art regarding specific hazards/risks, and/or to allow for investigation of the interplay between and across hazard types — particularly regarding how overall safety risk can be reduced or managed effectively. Results of testing and development activities are organized by the operational phase wherein a particular capability would be employed (i.e., preflight, in-flight, and post-flight/off-line). Pre-flight: A set of capabilities were developed to help mitigate safety risk prior to flight (e.g., during flight and mission planning). Results of testing summarize (1) validation activities to raise the Technology Readiness Level (TRL) and (2) evaluation activities where the capabilities were applied to flight/mission planning procedures and used by operators/pilots. For the latter, flight plans were automatically assessed, and operators/pilots were notified of hazardous flight segments so as to enable adjustment of the flight plan and re-evaluation, and/or to better inform go/no-go decisions. Capabilities addressed hazards associated with power consumption, third-party risk, wind, navigation system performance, radiofrequency interference, and proximity to geo-spatial threats (e.g., buildings, trees, and no-fly zones). In-flight: Flight experiments tested capabilities that detect and respond to hazards encountered during flight. In the first series, safety hazards were monitored and assessed onboard, and system-generated mitigation maneuvers were recorded (but not acted upon by the vehicle). In the second series, mitigation maneuver commands directed the aircraft in response to safety hazards (i.e., auto-mitigation). The sUAS used for testing is described in full, as is the test architecture, which included commercial avionics, research avionics, and onboard software designed to detect, assess, and respond to hazards. The onboard system was designed as a run-time assurance framework, consistent with [2] and supportive of both supervisory and automated modes. The primary functions included: real-time risk assessment (RTRA), auto-pilot monitoring, constraint monitoring, and contingency select/triggering. RTRA performs integrated risk assessment considering data from several hazard-related monitors (e.g., battery, motors, navigation, communications, population density, and loss-of-control). Post-flight/off-line: Data monitored and recorded during flights can enable IASMS capabilities that execute after flights have completed (or “off-line”). These include: (1) the ability to identify anomalies and trends that may only be observable when comparing data spanning a number of similar flights; (2) the ability to update and validate pre-flight and in-flight capabilities and any underlying models to improve their performance; (3) the ability to report anomalies/off-nominals that may indicate design changes or maintenance actions are needed; and (4) the ability for humans involved in operations to report safety-relevant observations to help in understanding the flight data and/or the operational context of a flight. Progress on three such capabilities is summarized; the first investigates anomaly detection given a limited set of flight logs and applies an approach previously used for space operations. The second explores what could be identified using a larger set of flight logs, including from web-based forums where flight logs are posted by sUAS autopilot users. The third creates a new means of collecting information on UAS incidents and accidents via the Aviation Safety Reporting System (ASRS).

sUAS↗
NASA NTRS2011

A Framework for Safe Integration of Small UAS Into the NAS

This paper discusses a proposed framework for the safe integration of small unmanned aerial systems (sUAS) into the National Airspace System (NAS). The paper examines the potential uses of sUAS to build an understanding of the location and frequency of potential future flight operations based on the future applications of the sUAS systems. The paper then examines the types of systems that would be required to meet the application-level demand to determine classes of platforms and operations. Finally, a framework is proposed for both airworthiness and operations that attempts to balance safety with utility for these important systems.

Logan, Michael J.↗