SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
How NASA is supporting the research and development of a collaborative traffic management system for operations in upper E class airspace. This is uncontrolled airspace that will be used by potential new entrants: airships, hypersonics, supersonics, unmanned and manned aircraft, balloons, etc.
Recent advances in technology have enabled industry development of new and innovative vehicle types, offering lower operating costs and highly automated functionality that facilitates the introduction of new types of operations. These include low-altitude airspace operations with small Unmanned Aircraft Systems (UASs), short distance urban and intercity operations, and high- altitude Upper Class E operations. These and other new operations are expected to result in a much higher operational tempo than is currently experienced across the National Airspace System (NAS). The projected increase in operations, as well as the introduction of new aircraft form factors and supporting technologies—including increasing autonomy—will present challenges to the existing Air Traffic Management (ATM) system, which is currently unable to cost-effectively scale and deliver needed services. In response to these challenges and opportunities, a highly automated, cooperative environment incorporating a federated network has been envisioned and described through multiple operational concepts, depicting the future operating environment as part of the NAS. Foundational to the success of this future operating environment is the establishment of common business rules and understandings across relevant stakeholders, referred to as Community Based Rules (CBRs). Development, adoption, and implementation of CBRs will require collaboration across multiple stakeholders, including operators, support services (industry), and the Federal Aviation Administration (FAA), to identify and resolve a broad range of questions and challenges. Examples of these questions include “what rules are needed?”, “how are they expressed?”, and “how will they be managed?” This document identifies and describes an initial set of questions and considerations to be examined as efforts begin to create the innovative, automated, cooperative operating environment of the future. The goal is to establish a common frame of reference to support discussions and decisions regarding the future implementation of CBRs as part of the NAS.
The variety of vehicle performance in upper Class E airspace requires a method that can efficiently compute the minimum safe operational boundary between aircraft. This work presents a mathematical method to define the minimum safe operational boundary needed for aerial vehicles operating in upper Class E airspace. This method focuses on the extra separation required by vehicle maneuverability, communication delay, and control/operator response time. A sensitivity study is then performed to provide a general understanding of the impact of these factors on the extra separation needed. Experiments with pairwise encounters are conducted to verify the results generated by the proposed methods.
This work presents probabilistic intent models for two typical vehicles in ETM operational environment. Several methods, including analytical methods for approximated solutions and a numerical method for exact solution, are presented and applied to compute the intent conflict probability for ETM operations. A comparison of these methods will be conducted in the final paper. Furthermore, simulations will be performed to verify the probabilistic intent model and verify the results of intent conflict probability from different methods.
This work presents a probabilistic operational intent model for vehicles operating in upper Class E airspace. A hybrid method is developed to calculate the intent conflict probability leveraging and extending past works on probabilistic conflict computation. Simulation results verify that the probabilistic intent model can accurately encompass the uncertain positions of each vehicle type, that are a result of wind prediction errors and vehicle performance. A comparison with past methods showed that the proposed hybrid method captures the intent conflict probability with better accuracy, especially for a larger look-ahead horizon, and computational time is reasonable for real-time applications. An example is presented to illustrate the use of the intent conflict probability in strategic planning applications.
This work presents a probabilistic operational intent model for vehicles operating in upper Class E airspace. A hybrid method is developed to calculate the intent conflict probability leveraging and extending past works on probabilistic conflict computation. Simulation results verify that the probabilistic intent model can accurately encompass the uncertain positions of each vehicle type, that are a result of wind prediction errors and vehicle performance. A comparison with past methods showed that the proposed hybrid method captures the intent conflict probability with better accuracy, especially for a larger look-ahead horizon, and computational time is reasonable for real-time applications. An example is presented to illustrate the use of the intent conflict probability in strategic planning applications.
This work proposes a negotiation model, built upon the sequential bargaining model, for strategic planning among high-altitude operations. The definition of cost/utility, the setup of time-dependent required cost, and the detailed negotiation structure and process are developed. The sensitivities of negotiation strategies or preferences, response time, and limited maneuverability are investigated to understand the behavior of the proposed negotiation model. Results show that the proposed model can serve the cooperative operation concept well: first, this model ensures an agreement can be reached within a predefined time window; second, operators can accurately express their priorities without exposing their private business information; third, the model encourages short response times and helps the negotiation process converge; finally, the limited and unbalanced maneuverability was found less of a concern for a fair negotiation due to the long lead time available for strategic planning.