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At least 199 records · Page 11
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NASA NTRS2014

Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project - Gen-4 and Gen-5 Radio Plans

NASA's UAS Integration in the NAS project, has partnered with Rockwell Collins to develop a concept Control and Non-Payload Communication (CNPC) system prototype radio, operating on recently allocated UAS frequency spectrum bands. This prototype radio is being used to validate initial proposed performance requirements for UAS control communications. This presentation will give an overview of the current plans for the prototype radio development.

communication↗
NASA NTRS2014

Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project, UAS Control and Non-Payload Communication System Phase-1 Flight Test Results

NASA's UAS Integration in the NAS project, has partnered with Rockwell Collins to develop a concept Control and Non-Payload Communication (CNPC) system prototype radio, operating on recently allocated UAS frequency spectrum bands. This prototype radio is being used to validate initial proposed performance requirements for UAS control communications. This presentation will give an overview of the current status of the prototype radio development, and results from phase 1 flight tests conducted during 2013.

communication↗
NASA NTRS2017

Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project

Over the past 5 years, the UAS integration into the NAS project has worked to reduce technical barriers to integration. A major focus of this work has been in support of RTCA SC-228. This committee has recently published the first UAS integration minimum performance standards (MOPS). This work has spanned detect and avoid (DAA) as well as command and control comm datalinks. I will discuss DAA efforts with focus on the human systems work. I will discuss how automation was discussed and addressed within this context. ICAO stood up a remotely piloted aircraft systems (RPAS) panel in 2014. They have developed an RPAS manual and are now working to revise existing annexes and standards and recommended practices. The Human In The System (HITS) has worked to infuse human factors guidelines into those documents. I will discuss that effort as well as how ICAO has defined and address autonomy. There is a great deal of interest in the control of multiple vehicles by a single operator. The UAS EXCOM Science and Research Panel (SARP) is holding a workshop on this topic in late June. I will discuss research performed on this topic when I worked for the Army and on-going work within the division and a NATO working group on Human-Autonomy Teaming.

Unmanned Aircraft Systems (UAS)↗
NASA NTRS2018

A VTOL Small Unmanned Aircraft System to Expand Payload Capabilities

One of the goals of Unmanned Aerial Systems (UAS) is to increase the capabilities of flight vehicles while maintaining small airframes that are also lightweight. Private package delivery companies and government agencies have an interest in vehicles that have large payload to weight ratios; thus allowing to deliver heavy payloads. Currently, UAS configurations suffer from propulsive and aerodynamic limitations that decrease the payload weight and/or mission range. In order to decrease these limitations, the Kinetic and Potential Energy Alternation for Greater Lift Enhancement (KP EAGLE) concept provides a novel way to transport payloads that may be too heavy for vehicles in a similar weight class. The concept vehicle takes off vertically without the payload. It then performs a dive maneuver that transfers the gained potential energy into kinetic energy to pick up the payload. This concept does not require special equipment during takeoff or landing and provides a better payload to weight ratio than other conventional vehicles in the same weight class.

Geuther, Steven↗
NASA NTRS2019

Usability Test of a Prototype UAS (Unmanned Aircraft Systems) Awareness Interface for Public Users

A tool to give the public a window into Unmanned Aerial Systems (UAS) Traffic Management (UTM) operations was created from an existing data collection tool. The interface included a map and a table showing details about UAS operations that could be queried in a number of ways. Eleven participants attended the study, successfully completing a 19-item task set in about 30 minutes. They correctly found information for 87% of the non-subjective tasks at a rate of around a minute per task, and rated the usability of the tool at the end of the session above the industry benchmark. Participants gave favorable reviews of the "public portal tool", even reporting that they would be satisfied with less information that it presented. There were one or two elements of the display that users found distracting and some navigation functions that need improvement, but on balance, the public representatives liked the features they saw in, and had few criticisms of, the public portal tool. One important issue for the small Unmanned Aerial System community to resolve will be how much or how little information should be available about UTM operations to members of the public.

Martin, Lynne H.↗
NASA NTRS2018

Giving the Public a Perspective into Unmanned Aircraft Systems' Operations

NASA is currently engaged in research to safely enable large-scale commercial applications of small Unmanned Aerial Systems (UAS) in low altitude airspace. This research effort, referred to as UAS Traffic Management (UTM), encompasses the concepts and technologies needed to accommodate the projected demand of UAS operating in the national airspace. One aspect related to the successful implementation of UTM in the future is public acceptance. Transparency will heavily influence this acceptance, and a public portal will provide much of that transparency through ease of access to information about the operations - mainly who, why, and where such operations are taking place. Related concerns to the public are individual privacy, security, and accountability of the operators. Providing the aforementioned information about operations can mitigate these concerns, but a balance will have to be achieved between the need for transparency from the public and the privacy of the operators. The proper balance and the needs of the various UTM stakeholders with regard to information access will be explored through the development and testing of a public portal as part of NASA's Technical Capability 3 (TCL 3) demonstration. Additionally, various approaches to the display of information and user interfaces will be surveyed through the development of a public portal by multiple UTM industry partners across different test sites.

UAS↗
NASA NTRS2016

Feasibility of Varying Geo-Fence Around an Unmanned Aircraft Operation Based on Vehicle Performance and Wind

Managing trajectory separation is critical to ensuring accessibility, efficiency, and safety in the unmanned airspace. The notion of geo-fences is an emerging concept, where distance buffers enclose individual trajectories and areas of operation in order to manage the airspace. Currently, the Air Traffic Management system for commercial travel defines static distance buffers around the aircraft; however, commercial UASs are envisioned to operate in significantly closer proximity to other UAS requiring a geo-fence for spacing operations. The geo-fence size can be determined based on vehicle performance characteristics, state of the airspace, weather, and other unforeseen events such as emergency or disaster response. Calculation of the geo-fence size could be determined as part of pre-flight planning and during real-time operations. A largely non-homogeneous fleet of UASs will be operating in low altitude and will likely be commercially developed. Due to intellectual property concerns, the operators may not provide detailed specifications of the control system to UTM. In addition, the huge variety of UAS makes modeling each control system prohibitive and flight data for these vehicles may not exist. Therefore, a generalized, simple geo-fence sizing algorithm must be developed such that it does not rely on detailed knowledge of the vehicle control system, accounts for the presence of urban winds, and is sufficiently accurate. In this work, two simple models are investigated to determine its feasibility as an adequate means for calculating the geo-fence size. The vehicle data used in this work are provided by UAS manufactures who have partnered with NASA's UTM project and some publicly available websites. The first model utilizes wind data processed from the NOAA HRRR (Hourly Rapid Refresh) product and Sonar Annemometer data provided by San Jose State. The second model utilizes OpenFOAM which is a CFD code used to generate a wind field for flow around a single building. The key vehicle performance parameters can include UAS response time to disturbances, command to actuation latency, control system rate limits, time to recovery to desired path, and aerodynamics. It was found that the first model provides an initial understanding of geo-fence sizing, but does not provide enough accuracy to provide UTM with an efficient means of scheduling vehicles. The results of the second model reveal that modeling UAS controls systems with a linearized plant and gain scheduled PID controller does not allow capture the UAS flight dynamics within a significant envelope of the wind disturbances.

uncertainty↗
NASA NTRS2016

Safely Enabling Low-Altitude UAS Operations: Unmanned Aircraft System Traffic Management

Concept of operations; Roles, responsibilities, implications on who pays; Information architecture paved way for FAA's (Federal Aviation Administration's) RFI (Request for Information); Demonstrated initial feasibility of architecture, application-protocol-interface-based approach, and overall construct; Data exchange and protocols; Demonstration of UTM (Unmanned Aerial Systems Traffic Management) TCL1 (Tuscaloosa, AL Airport Terminal 1) with all 6 test sites; Initial demonstration of UTM TCL2 for BVLOS (Beyond Visual Line-of-Sight) requirements.

Low-altitude UTM↗
NASA NTRS2017

Unmanned Aircraft Systems Traffic Management (UTM) Safely Enabling UAS Operations in Low-Altitude Airspace

Conduct research, development and testing to identify airspace operations requirements to enable large-scale visual and beyond visual line of sight UAS operations in the low-altitude airspace. Use build-a-little-test-a-little strategy remote areas to urban areas Low density: No traffic management required but understanding of airspace constraints. Cooperative traffic management: Understanding of airspace constraints and other operations. Manned and unmanned traffic management: Scalable and heterogeneous operations. UTM construct consistent with FAAs risk-based strategy. UTM research platform is used for simulations and tests. UTM offers path towards scalability

Low-altitude UTM↗
NASA NTRS2019

Scenario Complexity for Unmanned Aircraft System Traffic

This work introduces an approach to estimate the complexity of a low-altitude air traffic scenario involving multiple UASs using mathematical programming. Given a set of multi-point UAS flight trajectories, vehicle dynamics, and a conflict resolution algorithm, an abstract model is developed such that it can be solved quickly using a mathematical programming optimization software without running high-fidelity simulations that can be computationally expensive and may not suit real-time apA quick and accurate assessment of complexity for a given traffic scenario can help plan and schedule flights to alleviate traffic bottleneck and mitigate operation risks, especially for unmanned aerial system traffic management where high traffic density or complexity is expected. This work introduces a traffic scenario complexity metric that was constructed based on the number of potential conflicts weighted by the conflict resolution cost associated. The cost associated with a conflict is calculated based on the corresponding conflict resolution maneuvers. To obtain the conflict resolution maneuvers, a MILP-based optimization was formulated with the vehicle model and conflict management parameters incorporated. To evaluate the complexity metrics, an approach of using measurements from high-fidelity simulations was proposed. The scenario complexity measurements for 920 random-generated scenarios were obtained through high-fidelity simulations and treated as the ground truth. Two statistics methods: Pearson and Alternative Conditional Expectations were applied for analysis. The results showed that the number of flights has low correlation with the scenario complexity according to the correlation coefficients calculated by both methods. The Alternative Conditional Expectations method shows that the proposed scenario complexity metric has better correlation with the ground truth than the number of potential conflicts.plications. In the abstract model, each vehicle is represented by a time-varied vector associated with position, speed, and heading information. The total extra distance that aircraft need to divert from their original routes to avoid collisions is computed and used to setup a quadratic programming formula. The metrics including the number of conflicts and extra distances travelled by all vehicles are then utilized to estimate the complexity of a given UAS flight scenario. Results and verification against high-fidelity simulations will be provided in the final draft.

traffic complexity↗
NASA NTRS2012

UAS Integration into the NAS: Unmanned Aircraft System (UAS) Delegation of Separation

FAA Modernization and Reform Act of 2012 mandates UAS integration in the NAS by 2015. Operators must be able to safely maneuver UAS to maintain separation and collision avoidance. Delegated Separation is defined as the transfer of responsibility for maintaining separation between aircraft or vehicles from the air navigation service provider to the relevant flight operator, and will likely begin in sparsely trafficked areas before moving to more heavily populated airspace. As UAS operate primarily in areas with lower traffic density and perform maneuvers routinely that are currently managed through special handling, they have the advantage of becoming an early adopter of delegated separation. This experiment will examine if UAS are capable of performing delegated separation in 5 nm horizontal and 1000 ft vertical distances under two delegation conditions. In Extended Delegation, ATC are in charge of identifying problems and delegating to pilot identification and implementation of the solution and monitoring. In Full Delegation, the pilots are responsible for all tasks related to separation assurance: identification of problems and solutions, implementation and monitoring.

unmanned systems↗