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Strategic Framework for NASA’s Space Technology Mission Directorate

In October 2016, NASA’s Space Technology Mission Directorate (STMD) began adopting a new strategic framework that focuses investment prioritization and communication on impacts, outcomes, and challenges. The structure of the framework has been modeled after one successfully pioneered by NASA’s Aeronautics Research Mission Directorate over the past five years, incorporating lessons learned and changes where appropriate. The Framework is driven by two major factors: (a) dialogue with the community and (b) analysis of the overarching trends shaping the course of civilian space research. These factors are captured in the Framework as Mega-Drivers, which represent major axes of change within the space industry and are characterized by a collection of industry trends and projections. In response to these Mega-Drivers, STMD has developed its understanding of the vision for the future of civilian space relative to STMD space research, captured in five Strategic Thrusts that represent the major lines of investment within STMD’s portfolio. Within each of these Strategic Thrusts, multiple measureable, community-level goals have been established that STMD chooses to pursue as part of a joint effort across the community. STMD chose these goals based upon their potential impact, refers to them as Outcomes within the Framework. These Outcomes are decomposed into the products and/or capabilities that will be delivered by STMD, represented in the framework as Technical Challenges. This paper will further describe the framework structure and the progress that has been in defining each of the above elements to date.

Earle, Kevin D.↗

Strategic Implications of Phobos as a Staging Point for Mars Surface Missions

As human exploration endeavors begin to set sights beyond low Earth orbit to the surface of the Moon, exploration of the surface of Mars continues to serve as the “horizon destination” to help focus development and research efforts. One Mars exploration strategy often discussed is the notion of utilizing the moons of Mars, namely Phobos, as an exploration destination prior to Mars surface missions. The premise behind this is that staging missions from Mars’ moons as well as exploring the moons themselves would be less costly and risky. However, understanding potential advantages of Phobos staging and exploration must be done in the context of the overall end-to-end Mars surface exploration needs, goals, objectives, campaign approach, and systems required. This paper examines the strategic implications of utilizing the moons of Mars as a potential location for exploration of Mars. Operational concepts utilizing both Phobos and Mars orbital strategies will be examined to understand the architectural impacts of this staging strategy. The strategic implications of each operational concept are assessed to determine the overall key challenges and strategic links to other exploration destinations. Results from this analysis indicate that, if the objective is to conduct Mars surface missions, utilizing Phobos as an exploration destination adds little benefit toward the goal of exploration of Mars.

Drake, Bret G.↗

Strategic Framework for NASA’s Space Technology Mission Directorate

In October 2016, NASA’s Space Technology Mission Directorate (STMD) began adopting a new strategic framework that focuses investment prioritization and communication on impacts, outcomes, and challenges. The structure of the framework has been modeled after one successfully pioneered by NASA’s Aeronautics Research Mission Directorate over the past five years, incorporating lessons learned and changes where appropriate. The Framework is driven by two major factors: (a) dialogue with the community and (b) analysis of the overarching trends shaping the course of civilian space research. These factors are captured in the Framework as Mega-Drivers, which represent major axes of change within the space industry and are characterized by a collection of industry trends and projections. In response to these Mega-Drivers, STMD has developed its understanding of the vision for the future of civilian space relative to STMD space research, captured in five Strategic Thrusts that represent the major lines of investment within STMD’s portfolio. Within each of these Strategic Thrusts, multiple measurable, community-level goals have been established that STMD chooses to pursue as part of a joint effort across the community. STMD chose these goals based upon their potential impact, refers to them as Outcomes within the Framework. These outcomes are decomposed into the products and/or capabilities that will be delivered by STMD, represented in the framework as Technical Challenges. This paper will further describe the framework structure and the progress that has been in defining each of the above elements to date.

Kevin D Earle↗

A Decade of NASA Strategic Astrophysics Technology Investments: Technology Maturation, Infusion, and Other Benefits

NASA’s Astrophysics Division (APD) funds development of cutting-edge technology to enable its missions to achieve ambitious and groundbreaking science goals. These technology development efforts are managed by the Physics of the Cosmos (PCOS), Cosmic Origins (COR), and Exoplanet Exploration (ExE) Programs. The NASA Strategic Astrophysics Technology (SAT) Program was established in 2009 as a new technology maturation program to fill the gap in the Technology Readiness Level (TRL) range from 3 to 6. Since program inception, 100 SAT grants have been openly competed and awarded, along with dozens of direct-funded projects, leading to a host of technologies advancing their TRLs and/or being infused into space and suborbital missions and ground-based projects. We present the portfolio distribution in terms of specific technology areas addressed, including optics, detectors, coatings, coronagraphs, starshades, lasers, electronics, cooling systems, and micro-thruster subsystems. We show an analysis of the rate of TRL advances, infusion success stories, and other benefits such as training the future astrophysics workforce, including students and postdoctoral fellows hired by projects. Finally, we present APD’s current strategic technology maturation priorities for investment, enabling a range of future strategic astrophysics missions

NASA, astrophysics, technology development, optics↗

Strategic and Tactical Functions in an Autonomous Air Traffic Management System

This paper evaluates, by means of fast-time simulation, performance of a candidate system for autonomous air traffic management. Advancing towards autonomy in air traffic management may be necessary in order for new air vehicle types such as electric Vertical Take Off and Landing (eVTOL) to operate safely and efficiently in airspace shared with conventional traffic. To account for uncertain prediction, autonomous air traffic management was divided into two integrated and coordinated subsystems: strategic scheduling, performed at predeparture, and tactical conflict detection and resolution, performed throughout the flight. The conflict detection and resolution subsystem contained a second tactical scheduling function that applied to flights operating in the airspace near the destination airport. This paper compares and contrasts the two subsystems and uses fast-time simulation to demonstrate the comparisons. A scenario of 54 flights inbound to Newark Liberty International Airport was simulated multiple times with different parameters. The scenario was created using flight plans recorded from the National Airspace System on a low weather, average traffic day in April 2018. Whereas the routes were not changed, the departure times of the flights were modified to increase arrival rates at the Newark runway and arrival meter fixes. Results of the simulations showed that the autonomous air traffic management system was able to safely manage the traffic, even with prediction uncertainty. In addition, they showed the importance of including flight holding maneuvers, in addition to path stretching, in conflict detection and resolution and of coordinating strategic and tactical scheduling. Finally, a tradeoff between absorbing the delay calculated by strategic scheduling on the ground versus in the air showed that taking most of the delay on the ground is cost effective for a simple idealized cost function. However, taking a little of the delay in the air prevented throughput on the runway from dropping for short periods due to trajectory prediction uncertainty.

air traffic control↗

Strategic and Tactical Functions in an Autonomous Air Traffic Management System

This paper evaluates, by means of fast-time simulation, performance of a candidate system for autonomous air traffic management. Advancing towards autonomy in air traffic management may be necessary in order for new air vehicle types such as electric Vertical Take Off and Landing (eVTOL) to operate safely and efficiently in airspace shared with conventional traffic. To account for uncertain prediction, autonomous air traffic management was divided into two integrated and coordinated subsystems: strategic scheduling, performed at predeparture, and tactical conflict detection and resolution, performed throughout the flight. The conflict detection and resolution subsystem contained a second tactical scheduling function that applied to flights operating in the airspace near the destination airport. This paper compares and contrasts the two subsystems and uses fast-time simulation to demonstrate the comparisons. A scenario of 54 flights inbound to Newark Liberty International Airport was simulated multiple times with different parameters. The scenario was created using flight plans recorded from the National Airspace System on a low weather, average traffic day in April 2018. Whereas the routes were not changed, the departure times of the flights were modified to increase arrival rates at the Newark runway and arrival meter fixes. Results of the simulations showed that the autonomous air traffic management system was able to safely manage the traffic, even with prediction uncertainty. In addition, they showed the importance of including flight holding maneuvers, in addition to path stretching, in conflict detection and resolution and of coordinating strategic and tactical scheduling. Finally, a tradeoff between absorbing the delay calculated by strategic scheduling on the ground versus in the air showed that taking most of the delay on the ground is cost effective for a simple idealized cost function. However, taking a little of the delay in the air prevented throughput on the runway from dropping for short periods due to trajectory prediction uncertainty.

air traffic control↗

National Campaign (NC)-1 Strategic Conflict Management Simulation (X4) Final Report

Urban Air Mobility (UAM) enables highly automated, cooperative, passenger or cargo-carrying air transportation services in and around urban areas. UAM is a subset of the Advanced Air Mobility (AAM) concept under development by the National Aeronautics and Space Administration (NASA), the Federal Aviation Administration (FAA), and industry. The Strategic Conflict Management (SCM) Simulation, dubbed “X4”, was conducted between July 2021 and June 2022 by NASA with the FAA and industry to evolve the Provider of Services for UAM (PSU) that will be needed to ensure initial UAM operations can scale in the National Airspace System (NAS). The FAA UAM Concept of Operations (ConOps) v1 [1] served as an initial guiding document for the X4 airspace management system design to ensure the architecture supports testing of services provided by third-party service providers. To that end, the X4 architecture leveraged concepts and technologies developed for Unmanned Aircraft System (UAS) Traffic Management (UTM) while also developing and testing new capabilities and services needed for UAM. The architecture included an initial prototype of the FAA-Industry Exchange Protocol (FIDXP), third-party services such as the PSUs, and Discovery and Synchronization Service (DSS), and other new services such as Demand-Capacity Balancing (DCB) to facilitate UAM strategic conflict management. During X4, NASA led discussions and collaborated with seven industry airspace partners to develop initial airspace management concepts for UAM that drove what would be tested and evaluated during the simulations. The initial capabilities defined for PSU leveraged UTM UAS Service Supplier (USS) as a starting point and evolved to meet UAM requirements. These discussions were also an opportunity for NASA to collaborate with industry to develop a set of initial Community-Based Rules (CBRs). These CBRs enabled how UAM traffic would be cooperatively managed among UAM operators. The collaborative, iterative process of developing CBRs with industry during X4 provided insight into the challenges involved and identified the need for a suitable and effective forum for future CBR development. In parallel with these discussions, NASA conducted a series of seven software sprints and two collaborative simulations with the airspace partners that built up in complexity. Over the course of the year, all seven partners successfully completed all the sprints by demonstrating the required capabilities. They also participated in the two collaborative simulations to demonstrate how multiple PSUs could work together in a collaborative, more complex environment with higher traffic density. The X4 simulation accomplished NASA's objectives and helped advance the development of the seven participating PSUs. The lessons learned provided insight into key elements of the UAM Notional Architecture from the FAA ConOps v1 [1] and UTM technologies when applied to UAM: - Having a Concept of Use (ConUse) defined prior to the activity would accelerate the time and effort from concept development to testing. - While the UAM Notional Architecture provided a starting point for a federated architecture that support services provided by third-party providers, the USS and PSU differed in their capability definitions for operational intent submission and sharing, conformance monitoring, airspace authorization, strategic conflict management, airspace constraints and dynamic replanning. - While existing DSS developed for UTM provided a way for PSU and other UAM services (such as DCB) to discover relevant operations from each other, additional complexity and challenges were found during X4 testing and illuminated the need for a more suitable solution for UAM. Industry can leverage the results of this demonstration to accelerate UAM requirements, CBRs, and standards development. The FAA and other government municipalities and agencies will be able to leverage results to inform future policies and identify additional gaps that require further analysis, moving toward operationalization of UAM.

Urban Air Mobility↗

Roadmap to Cooperative Operating Practices for Strategic Conflict Detection and Resolution in the Upper Class E Traffic Management Concept

As the governing body of flight operations in the highly anticipated emergent area of Upper Class E airspace (60,000 ft and above), the Federal Aviation Administration (FAA) has recognized the potential for a possible extensible traffic management system for new entrants into this domain. Following the successes with Unmanned Aircraft Systems (UAS) Traffic Management (UTM) and Advanced / Urban Air Mobility (AAM / UAM) Traffic Management programs, FAA put forth an initial concept of operations for supporting the start of the Upper Class E Traffic Management (ETM) concept. Like UTM and AAM / UAM, ETM is envisioned to also be a community-based, industry driven cooperative management concept. However, tailoring it to be adaptable to the atmospheric communication, navigation, and surveillance deficits, as well as the diverse vehicle and mission profiles operating in the ETM environment will be the challenge. As such, the National Aeronautics and Space Administration (NASA) Ames Research Center has been investigating several technologies that will help enable industry in the development of this new type of cooperative operating environment. These technologies are being prototyped and will be tested in a collaborative evaluation of an initial ETM system in late 2023. The evaluation will concentrate on building out ETM system technologies that will inform the participants regarding operational intent sharing, strategic conflict detection, and the resultant deconfliction process. In addition to the technical aspects, key roles and responsibilities will need to be defined. This will be done through exploring community-agreed upon Cooperative Operating Practices (COPs) that include procedures and capabilities to aid in timely, strategic conflict identification and resolution to be developed during the evaluation. As an initial step to the evaluation, the ETM research team at NASA Ames solicited industry feedback on various aspects of ETM operations from subject matter experts. A virtual tabletop walkthrough session was held over a two-day period to follow a roadmap through the functional steps needed to build COPs, focusing on strategic conflict detection and resolution.

Upper Class E Traffic Management (ETM)↗

Modeling the Strategic Behavior of an Active Distribution Network in the ISO Markets

With increasing integration of distributed energy resources (DERs), active distribution networks (ADNs) can actively participate in the electricity markets by dispatching their DERs, which can change the existing electricity market paradigm. It is essential to investigate the strategic behaviors of ADNs and their DER dispatch when they participate in the wholesale market as price-makers. This paper proposes a bi-level optimization model to study the strategic behavior of an ADN in both energy and reserve markets. The optimal scheduling of DERs in the ADN is modeled as the upper level problem and the joint energy and reserve market-clearing of the ISO is modeled as the lower-level problem. The two-level optimization models exchange bidding information and energy/reserve prices with each other. The proposed bi-Ievel optimization problem is converted to a mathematical programming with equilibrium constraints (MPEC) by using Karush-Kuhn Tucker (KKT) conditions and strong duality theory. Further, the MPEC problem is reformulated as a computationally-solvable mixed integer second order cone programming (MISOCP) model. The simulation results on an illustrative case demonstrate the impact of the strategic bidding of the ADN on the day-ahead energy and reserve market prices.

Xue, Yaosuo↗

A Portfolio Analysis Tool for Measuring NASAs Aeronautics Research Progress toward Planned Strategic Outcomes

Description of a tool for portfolio analysis of NASA's Aeronautics research progress toward planned community strategic Outcomes is presented. The strategic planning process for determining the community Outcomes is also briefly described. Stakeholder buy-in, partnership performance, progress of supporting Technical Challenges, and enablement forecast are used as the criteria for evaluating progress toward Outcomes. A few illustrative examples are also presented.

Portfolio Analysis↗

Cyber-Informed Engineering for Strategic Planning

The CIE Guide for Engaging Organizational Leadership: Board and C-Suite describes how to apply CIE at the senior-level of organizational management. This guidance integrates CIE concepts into theory and practice of guiding coalition leadership to improve permeation of CIE concepts throughout organizational culture. This guide incorporates feedback from CIE community of practice volunteers in a study of how business administrative and strategic planning and management guidance and course materials can be applied to CIE implementation throughout an organization's principles and processes. This guide defines the interests guiding senior leadership, managers and supervisors, and technicians and workers involved in creating and operating and maintaining systems, and cultural/ historical/ political assumptions or influences shaped the landscape that could facilitate or impede development of CIE. The included guidance for organizational strategic management and leadership can be imbedded into contract guidance based on the CIE implementation guide and lessons learned from industry application.

97 MATHEMATICS AND COMPUTING↗

Energy Leaders: The Catalyst for Strategic Energy Management

This study investigates the crucial role energy leaders play in driving strategic energy management (SEM) and accelerating cost savings within a manufacturing organization and consequently, the industrial sector. Whereas energy efficiency can be seen as an innovative business practice with irrefutable cost benefits, its effective implementation requires strategic leadership and a structured approach. This research analyzes data collected from 120 participants representing 71 companies attending the Energy Bootcamp events organized by the U.S. Department of Energy’s (DOE) Better Plants program. The collected data focused on the state of SEM implementation, the presence and responsibilities of energy leaders, and the formation and function of energy teams. The findings reveal a significant gap between the perceived importance of SEM and its actual adoption, highlighting the need for strong leadership to drive behavioral changes by championing energy efficiency initiatives. Results indicate that effective energy leaders possess a diverse skill set, including the ability to secure top management buy-in, foster a culture of energy consciousness, and collaborate across departments. This study emphasizes the importance of empowering energy leaders with clearly defined roles and responsibilities as well as the authority to build and lead cross-functional energy teams. Furthermore, integrating energy management into existing organizational structures and leveraging readily available resources are identified as key factors for successful implementation. This research underscores how dedicated leadership and effective SEM practices help achieve industrial energy efficiency goals, providing practical insights for organizations seeking to improve performance and contribute to a resilient future.

energy leader↗

Strategic Design of Long-Haul and Oceanic Aircraft Trajectories in Aviation Operations

Long-Haul Aircraft consume most of their fuel during the cruise phase of flight. The inefficiency in cruise flights compared to efficient routes varies is around 3 to 4. The efficiency of oceanic flights is low due to limited navigational and communication equipment, congestion and airspace restrictions. The availability of Automated Dependent Surveillance-Broadcast (ADS-B) and other improvements provides opportunity for better strategic planning of trajectories. Transatlantic flights between US and Europe constitute one of the busiest oceanic airspace regions in the world. This talk examines the benefits of a wind-optimal trajectory concept with a strategic de-confliction component compared to the current flight planning using the North Atlantic Tracks. The analysis is based on air traffic between US and Europe during July 2012. The potential fuel savings are in the range of (420-970) kg per flight for a Boeing 767-300, the most widely used aircraft between the city-pairs in this study. The talk also describes a global simulation of aviation operations combining flight plans and real air traffic data with historical commercial city-pair aircraft type and schedule data and global atmospheric data. The resulting capability extends the simulation and optimization functions of NASAs Future Air Traffic Management Concept Evaluation Tool (FACET) to global scale. This new capability is used to characterize the evolution of global air traffic, analyze fuel savings and seasonal variations in the long-haul wind-optimal traffic patterns in six major regions of the world.

strategic trajectory design↗

Report on Workshop on Artificial Intelligence in Strategic Planning and Science Prioritization

This report details the observations from a two-day virtual workshop, held May 12-13, 2020, focused on whether, and how, artificial intelligence (AI) could assist humans in strategic planning, specifically in science and technology prioritization. The participants identified several “key challenges” that AI might tackle in this area. To further understand the value of these key challenges the workshop then developed related test cases that would demonstrate specifically how AI/machine learning (ML) could provide assistance to humans. Approximately 40 subject matter experts (SMEs), with backgrounds in AI, strategic planning for science, and scientific data, were gathered for the conference. This report collates the details of the output of the workshop. The “best” test cases include (in no particular order):Use of AI to assist in selecting Decadal Survey priorities. * Use of AI to identify new, or previously unidentified, science topics for prioritization. * Using AI to better label and increase discoverability of scientific literature and proposals. * Use of AI to enhance current observation capabilities for scientific missions. * Using AI to mitigate biases in selection of proposal reviewers and membership of advisory committees. Examination of these test cases indicates that Natural Language Processing (NLP) is a common capability found in most of the ”best” (top-rated) test cases and is a valuable, multi-purpose tool which enables ML in this area.

strategic planning↗

Demand Capacity Balancing at Vertiports for Initial Strategic Conflict Management of Urban Air Mobility Operations

Demand Capacity Balancing (DCB) can be applied in strategic conflict management for safe Urban Air Mobility (UAM) operations. Even when the operational tempo is low, traffic demand can locally exceed the capacity at airspace resources like vertiports. This paper proposes a DCB algorithm to manage the UAM traffic demand strategically, given the capacity at vertiports. The DCB algorithm is evaluated with traffic scenarios at Dallas/Fort Worth urban area in terms of various metrics such as demand distribution changes, pre-departure delay, and the number of simultaneous operations in the air. With the same experiment setup, more extended studies are also conducted to investigate how the UAM flight scheduling based on the DCB algorithm is affected by various conditions that can occur in a practical UAM environment, including vertiport capacity changes, a slot size parameter in capacity constraint, differences in operational policy between operators like lead time for flight plan submission and cruise flight speed, and uncertainties in actual departure and arrival times.

Urban Air Mobility↗

Analysis of Strategic Conflict Management Approaches as Applied to Simulated UAM Operations

This report presents the results of a comparison analysis of candidate Strategic Conflict Management (SCM) strategies as applied to Urban Air Mobility (UAM) operations. The SCM strategies investigated included: no SCM, resource scheduling (RS), resource flow rates (FR), area-based flow rates (AR), and conflict detection and resolution (CR). The study evaluated each strategy against the same 3 levels of flight demand in a representative airspace construct for the Dallas/Fort Worth region. The study also accounted for different levels of uncertainty in operational planning and equivalent levels of trajectory following error. In the analysis, we compared the SCM strategies’ effectiveness in reducing the need for the tactical conflict management layer to act and looked at the metrics of unmitigated losses-of-separation (LOS), flight delays imposed by strategic planning, and throughput of the overall airspace. The study results indicated that the CR strategy was the most effective at reducing LOS and percentage of flights with LOS, even in the presence of trajectory error when uncertainty is accounted for in planning. Thus, if the objective is to reduce the number of actions that the tactical conflict management layer will have to take, in terms of conflicts that may need to be resolved, CR is the strategy to use. The FR strategy was found to be the worst at reducing LOS and percentage of flights with LOS. Thus, it is not very effective at reducing the actions required by a tactical conflict management layer. In the airspace tested, the demand was high enough to produce unacceptable levels of flight delay and reductions of throughput when the SCM strategies were implemented. This was especially evident at the higher levels of trajectory uncertainty and error. The need to account for expected levels of uncertainty becomes more and more important as the demand level increases and as the level of trajectory error increases. This is because the likelihood of flights with LOS increases as the density of operations increases and as the level of trajectory error increases. Accounting for uncertainty in the SCM strategies improves the strategy effectiveness with respect to LOS but increases delays and reduces throughput. Thus, there is a tradeoff between scalability and allowable levels of uncertainty. That is, we can implement an air traffic management construct that allows high levels of uncertainty, but we can expect that same system to have limits on scalability that may be evident even at small demand levels, such as those used in this study. Therefore, the results in this study indicate that an air traffic system should attempt to implement mechanisms appropriate for reducing uncertainty where possible in order to increase the chances for scalability. And this increased level of predictability of operations needs to be balanced with mechanisms for ensuring flexibility when operational conditions and plans need to change, even though those types of changes should be the exception rather than the rule under normal conditions. The study also introduced a trade space that could serve as a mechanism for selecting the appropriate SCM strategy in a trade-off between uncertainty and error, mean flight delay, and the LOS metrics (which this study equates to the potential for tactical conflict management actions). The optimal solution for a given airspace, demand level, and other factors, could likely be a combination of SCM strategies, although this study only compared the use of a single SCM strategy at a time. The CR strategy appeared to have the best opportunity for scalability by limiting the number of potential tactical actions to nearly zero.

Strategic Conflict Management↗

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the National Airspace System (NAS) is expected to skyrocket to millions, potentially congesting the airspace which increases the likelihood of separation violations and possibly incidents. Currently, flight infrastructure can only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay at one airport can send ripple effects throughout the system, causing more delays and missed connections. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is often a safety critical property for fixed-wing drones in the airspace. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. In this paper, the term drone is applied to both Unmanned Aerial Vehicle (UAV) and small Unmanned Aircraft System (UAS) vehicles operating autonomously. There exists a gamut of approaches to the merging and crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to these problems that is based on distributed cooperation between the drones and the infrastructure. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing drones to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them while remaining in the equilibrium state. The equilibrium state is defined as the state when a set of n aircraft move at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as the state when at least one aircraft cannot move at its maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach.

Distributed↗

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the National Airspace System (NAS) is expected to skyrocket to millions, potentially congesting the airspace which increases the likelihood of separation violations and possibly incidents. Currently, flight infrastructure can only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay at one airport can send ripple effects throughout the system, causing more delays and missed connections. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is often a safety critical property for fixed-wing drones in the airspace. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. In this paper, the term drone is applied to both Unmanned Aerial Vehicle (UAV) and small Unmanned Aircraft System (UAS) vehicles operating autonomously. There exists a gamut of approaches to the merging and crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to these problems that is based on distributed cooperation between the drones and the infrastructure. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing drones to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them while remaining in the equilibrium state. The equilibrium state is defined as the state when a set of n aircraft move at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as the state when at least one aircraft cannot move at its maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach.

Distributed↗