A Scalable Low-Cost-UAV Traffic Network (uNet)

This article proposes a new Unmanned Aerial Vehicle (UAV) operation paradigm to enable a large number of relatively low-cost UAVs to fly beyond-line-of-sight without costly sensing and communication systems or substantial human intervention in individual UAV control. Under current free-flight-like paradigm, wherein a UAV can travel along any route as long as it avoids restricted airspace and altitudes. However, this requires expensive on-board sensing and communication as well as substantial human effort in order to ensure avoidance of obstacles and collisions. The increased cost serves as an impediment to the emergence and development of broader UAV applications. The main contribution of this work is to propose the use of pre-established route network for UAV traffic management, which allows: (i) pre- mapping of obstacles along the route network to reduce the onboard sensing requirements and the associated costs for avoiding such obstacles; and (ii) use of well-developed routing algorithms to select UAV schedules that avoid conflicts. Available GPS-based navigation can be used to fly the UAV along the selected route and time schedule with relatively low added cost, which therefore, reduces the barrier to entry into new UAV-applications market. Finally, this article proposes a new decoupling scheme for conflict-free transitions between edges of the route network at each node of the route network to reduce potential conflicts between UAVs and ensuing delays. A simulation example is used to illustrate the proposed uNet approach.Comment: To be submitted to journal, 21 pages, 9 figure

Share the Sky: Concepts and Technologies That Will Shape Future Airspace Use

The airspace challenge for the United States is to protect national sovereignty and ensure the safety and security of those on the ground and in the air, while at the same time ensuring the efficiency of flight, reducing the costs involved, protecting the environment, and protecting the freedom of access to the airspace. Many visions of the future NAS hold a relatively near-term perspective, focusing on existing uses of the airspace and assuming that new uses will make up a small fraction of total use. In the longer term, the skies will be filled with diverse and amazing new air vehicles filling our societal needs. Anticipated new vehicles include autonomous air vehicles acting both independently and in coordinated groups, unpiloted cargo carriers, and large numbers of personal air vehicles and small-scale point-to-point transports. These vehicles will enable new capabilities that have the potential to increase societal mobility, transport freight at lower cost and with lower environmental impact, improve the study of the Earth s atmosphere and ecosystem, and increase societal safety and security by improving or drastically lowering the cost of critical services such as firefighting, emergency medical evacuation, search and rescue, border and neighborhood surveillance, and the inspection of our infrastructure. To ensure that uses of the airspace can continue to grow for the benefit of all, a new paradigm for operations is needed: equitably and safely sharing the airspace. This paper is an examination of such a vision, concentrating on the operations of all types of air vehicles and future uses of the National Airspace. Attributes of a long-term future airspace system are provided, emerging operations technologies are described, and initial steps in research and development are recommended

Multi‐agent Systems for Air Traffic Conflicts Resolution by Local Speed Regulation

Best Paper Award for Automation trackInternational audienceAir Traffic Flow Management (ATFM) aims at structuring traffic in order to reduce congestion in airspace. Congestion being linked to aircraft located at the same position at the same time, ATFM organizes traffic in the spatial dimension (e.g. route network) and/or in the time dimension (sequencing and merging in TMA, Miles-in-Trail for en-route airspace).The objective of this paper is to develop a methodology that allows the traffic to self-organize in the time dimension when demand is high. This structure disappears when the demand diminishes.In order to reach this goal, a multi-agent system has been developed. This algorithm regulates aircraft speed in order to reduce the number of conflicts, thus decreases overall traffic complexity, which becomes easier to manage by air traffic controllers. This algorithm was applied on realistic examples

Development of a Framework to Compare Low-Altitude Unmanned Air Traffic Management Systems

Presented at the AIAA SciTech 2021 ForumSeveral reports forecast a very high demand for Urban Air Mobility services such as package delivery and air taxi. This would lead to very dense low-altitude operations which cannot be safely accommodated by the current air traffic management system. Many different architectures for low-altitude air traffic management have been proposed in the literature, however, the lack of a common framework makes it difficult to compare strategies. The work presented here establishes efficiency, safety and capacity metrics, defines the components of an automated traffic management system architecture and introduces a preliminary framework to compare different alternatives. This common framework allows for the evaluation and comparison of different alternatives for unmanned traffic management. The framework is showcased on different strategies with different architectures. The impact of algorithmic choices and airspace architectures is evaluated. A decoupled approach to 4D trajectory planning is shown to scale poorly with agents density. The impact of segregating traffic by heading is shown to be very different depending on the algorithms and airspace access rules chosen

Human Computer Interactions in Next-Generation of Aircraft Smart Navigation Management Systems: Task Analysis and Architecture under an Agent-Oriented Methodological Approach

The limited efficiency of current air traffic systems will require a next-generation of Smart Air Traffic System (SATS) that relies on current technological advances. This challenge means a transition toward a new navigation and air-traffic procedures paradigm, where pilots and air traffic controllers perform and coordinate their activities according to new roles and technological supports. The design of new Human-Computer Interactions (HCI) for performing these activities is a key element of SATS. However efforts for developing such tools need to be inspired on a parallel characterization of hypothetical air traffic scenarios compatible with current ones. This paper is focused on airborne HCI into SATS where cockpit inputs came from aircraft navigation systems, surrounding traffic situation, controllers' indications, etc. So the HCI is intended to enhance situation awareness and decision-making through pilot cockpit. This work approach considers SATS as a system distributed on a large-scale with uncertainty in a dynamic environment. Therefore, a multi-agent systems based approach is well suited for modeling such an environment. We demonstrate that current methodologies for designing multi-agent systems are a useful tool to characterize HCI. We specifically illustrate how the selected methodological approach provides enough guidelines to obtain a cockpit HCI design that complies with future SATS specifications.This work was supported in part by Projects MINECO TEC2011-28626-C02-01/02, by program CENIT-ATLANTIDA (cofinanced by Indra and Boeing R&TE), and by ULPGC Precompetitive Research Project (ULPGC Own Program).Publicad

Decentralized aircraft landing scheduling at single runway non-controlled airports

The existing air transportation system is approaching a bottleneck because its dominant huband- spoke model results in a concentration of a large percentage of the air traffic at a few hub airports. Advanced technologies are greatly needed to enhance the transportation capabilities of the small airports in the U.S.A., and distribute the high volume of air traffic at the hub airports to those small airports, which are mostly non-controlled airports. Currently, two major focus areas of research are being pursued to achieve this objective. One focus concentrates on the development of tools to improve operations in the current Air Traffic Management system. A more long-term research effort focuses on the development of decentralized Air Traffic Management techniques. This dissertation takes the latter approach and seeks to analyze the degree of decentralization for scheduling aircraft landings in the dynamic operational environment at single runway noncontrolled airports. Moreover, it explores the feasibility and capability of scheduling aircraft landings within uninterrupted free-flight environment in which there is no existence of Air Traffic Control (ATC). First, it addresses the approach of developing static optimization algorithms for scheduling aircraft landings and, thus, analyzes the capability of automated aircraft landing scheduling at single runway non-controlled airports. Then, it provides detailed description of the implementation of a distributed Air Traffic Management (ATM) system that achieves decentralized aircraft landing scheduling with acceptable performance whereas a solution to the distributed coordination issues is presented. Finally real-time Monte Carlo flight simulations of multi-aircraft landing scenarios are conducted to evaluate the static and dynamic performance of the aircraft landing scheduling algorithms and operation concepts introduced. Results presented in the dissertation demonstrate that decentralized aircraft landing scheduling at single runway non-controlled airports can be achieved. It is shown from the flight simulations that reasonable performance of decentralized aircraft landing scheduling is achieved with successful integration of publisher/subscriber communication scheme and aircraft landing scheduling model. The extension from the non-controlled airport application to controlled airport case is expected with suitable amendment, where the reliance on centralized air traffic management can be reduced gradually in favor of a decentralized management to provide more airspace capacity, flight flexibility, and increase operation robustness

Conceptual-Level Analysis and Design of Unmanned Air Traffic Management Systems

There have been multiple announcements by different companies in the past couple years of package delivery by drone and air taxi projects. However, there are still many barriers to the deployment of high densities of aerial vehicles in low-altitude airspace over urban areas. Current Air Traffic Control Systems cannot handle the high density of traffic being forecast. Integrating these new types of on-demand air mobility in the National Airspace requires a fundamental change to the traffic management system. Many different concepts of operations for unmanned traffic management (UTM) systems have been proposed, but there is no common framework to evaluate and compare alternatives at a conceptual design stage. This might cause a locally optimal system to be chosen, resulting in lower safety and economic performance than what would have been possible if a more systematic approach to the design of UTM system had been followed. In this thesis, a systematic approach to the design of UTM systems is introduced. Based on the literature on conceptual design, a five step approach to the design of UTM systems is proposed. The steps of the approach are: define operating scenarios, generate UTM alternatives, select performance criteria, evaluate, and make decision. To generate UTM alternatives in a systematic manner, a matrix of alternatives should be created. However, this requires a system decomposition that does not currently exist for UTM systems. Here, a system decomposition into four subsystems is proposed: airspace structure, access control, preflight planning, and collision avoidance. For each subsystem, alternatives are identified using the literature. For the second step of the approach, operating scenarios for UTM are not well-defined. There are many external factors outside of the designer’s control, and different studies make different assumptions. Three different external factors, or components of an operating scenario, are identified: demand, static obstacles, and priority traffic. The impact of the different subsystems and external factors on the performance of a given UTM architecture cannot be found in the literature. Many studies evaluate a point design or fix assumptions to focus on a single subsystem. There is no available tool that allows to evaluate different UTM architectures while varying all the elements that have been presented here. To bridge that gap, an agent-based simulation was developed to allow the evaluation of the UTM systems generated using the matrix of alternatives in different operating scenarios. For the fourth step of the approach, performance criteria are selected from the aviation literature. To capture safety, the number of losses of separation and near-midair-collisions per flight hour are used. To measure the efficiency of the trajectories, a time and energy efficiency metrics are introduced. The capacity of the system is evaluated for a fixed overall density using the throughput, or number of vehicles completing a flight per minute. Finally, two simple multi-attribute decision making methods are selected from the literature. This allows to rank architectures based on their performance in a given scenario for a given set of weights representing a designer’s preferences. This thesis also proposes a novel 4D trajectory planning algorithm that relies on a local collision avoidance method. Experiments show that it performs well in terms of time efficiency and throughput when compared to a decoupled approach. The novel algorithm achieves a comparable performance to a global optimization algorithm in a nominal cruise scenario but is much more computationally efficient. The impact of the inclusion of certain subsystems and external factors on the outcome of the conceptual design stage is systematically evaluated in a series of experiments. Performance of different architectures is evaluated with and without the subsystem or external factor of interest. The experiments show that there are significant interactions between agents' autonomous behaviors, airspace structure, and external factors such as demand, static obstacles, and priority traffic. The decision tables obtained with and without the element of interest are compared, and weights are found such that the architecture rankings are different. This shows that neglecting these interactions or making simplifying assumptions may change the outcome of the conceptual design stage and result in the selection of an architecture that underperforms in terms of safety, capacity or efficiency. This is validated on two use cases, an air taxi scenario and a drone delivery scenario. In the air taxi scenario, using the proposed approach results in the selection of an alternative with a 25\% higher score than the alternative selected with a baseline approach. As a result of the work conducted in this thesis, the importance of including the autonomy, airspace structure, demand, static obstacles, and priority traffic in the early stage of UTM evaluation has been demonstrated. The necessity of including other subsystems or external factors can be evaluated by following the same process that was demonstrated in the thesis.Ph.D

Safety and Convergence Analysis of Intersecting Aircraft Flows under Decentralized Collision Avoidance

Safety is an essential requirement for air traffic management and control systems. Aircraft are not allowed to get closer to each other than a specified safety distance, to avoid any conflicts and collisions between aircraft. Forecast analysis predicts a tremendous increase in the number of flights. Subsequently, automated tools are needed to help air traffic controllers resolve air born conflicts. In this dissertation, we consider the problem of conflict resolution of aircraft flows with the assumption that aircraft are flowing through a fixed specified control volume at a constant speed. In this regard, several centralized and decentralized resolution rules have been proposed for path planning and conflict avoidance. For the case of two intersecting flows, we introduce the concept of conflict touches, and a collaborative decentralized conflict resolution rule is then proposed and analyzed for two intersecting flows. The proposed rule is also able to resolved airborn conflicts that resulted from resolving another conflict via the domino effect. We study the safety conditions under the proposed conflict resolution and collision avoidance rule. Then, we use Lyapunov analysis to analytically prove the convergence of conflict resolution dynamics under the proposed rule. The analysis show that, under the proposed conflict resolution rule, the system of intersecting aircraft flows is guaranteed to converge to safe, conflict free, trajectories within a bounded time. Simulations are provided to verify the analytically derived conclusions and study the convergence of the conflict resolution dynamics at different encounter angles. Simulation results show that lateral deviations taken by aircraft in each flow, to resolve conflicts, are bounded, and aircraft converged to safe and conflict free trajectories, within a finite time. The proposed rule is powerful when the pilots of the collaborating aircraft, resolving a potential conflict, are either humans or robots. However, when a human pilot is collaborating with a robot pilot for the first time, the robot control should optimize its control criteria to collaborate well with the human. Basically, the robot must adapt its output as it learns from the human. We study the situation of the Human-in-the-Loop, assuming that human will follow lateral maneuvers for conflict resolution. We model the human as an optimal controller that minimizes the risk of collision. Based on that model, we use differential game analysis to get an accurate estimate for the human control criteria. We propose a new algorithm, based on least square minimization, to estimate the Kalman gain of the human's model, and therefore accurately estimate his optimal control criteria. Simulations of this learned rule show that robot pilot can successfully learn from the human pilot actions, and both can cooperate successfully to resolve any conflicts between their aircraft