Large-Scale Unmanned Aerial Systems Traffic Density Prediction and Management

In recent years, the applications of Unmanned Aerial Systems (UAS) has become more and more popular. We envision that in the near future, the complicated and high density UAS traffic will impose significant burden to air traffic management. Lot of works focus on the application development of individual Small Unmanned Aerial Systems (sUAS) or sUAS management Policy, however, the study of the UAS cluster behaviors such as forecasting and managing of the UAS traffic has generally not been addressed. In order to address the above issue, there is an urgent need to investigate three research directions. The first direction is to develop a high fidelity simulator for the UAS cluster behavior evaluation. The second direction to study real time trajectory planning algorithms to mitigate the high dense UAS traffic. The last direction is to investigate techniques that rapidly and accurately forecast the UAS traffic pattern in the future. In this thesis, we elaborate these three research topics and present a universal paradigm to predict and manage the traffic for the large-scale unmanned aerial systems. To enable the research in UAS traffic management and prediction, a Java based Multi-Agent Air Traffic and Resource Usage Simulation (MATRUS) framework is first developed. We use two types of UAS trajectories, Point-to-Point (P2P) and Man- hattan, as the case study to describe the capability of presented framework. Various communication and propagation models (i.e. log-distance-path loss) can be integrated with the framework to model the communication between UASs and base stations. The results show that MATRUS has the ability to evaluate different sUAS traffic management policies, and can provide insights on the relationships between air traf- fic and communication resource usage for further studies. Moreover, the framework can be extended to study the effect of sUAS Detect-and-Avoid (DAA) mechanisms, implement additional traffic management policies, and handle more complex traffic demands and geographical distributions. Based on the MATRUS framework, we propose a Sparse Represented Temporal- Spatial (SRTS) UAS trajectory planning algorithm. The SRTS algorithm allows the sUAS to avoid static no-fly areas (i.e. static obstacles) or other areas that have congested air traffic or communication traffic. The core functionality of the routing algorithm supports the instant refresh of the in-flight environment making it appropri- ate for highly dynamic air traffic scenarios. The characterization of the routing time and memory usage demonstrate that the SRTS algorithm outperforms a traditional Temporal-Spatial routing algorithm. The deep learning based approach has shown an outstanding success in many areas, we first investigated the possibility of applying the deep neural network in predicting the trajectory of a single vehicle in a given traffic scene. A new trajectory prediction model is developed, which allows information sharing among vehicles using a graph neural network. The prediction is based on the embedding feature, which is derived from multi-dimensional input sequences including the historical trajectory of target and neighboring vehicles, and their relative positions. Compared to other existing trajectory prediction methods, the proposed approach can reduce the pre- diction error by up to 50.00%. Then, we present a deep neural network model that extracts the features from both spatial and temporal domains to predict the UAS traffic density. In addition, a novel input representation of the future sUAS mission information is proposed. The pre-scheduled missions are categorized into 3 types according to their launching times. The results show that our presented model out- performs all of the baseline models. Meanwhile, the qualitative results demonstrate that our model can accurately predict the hot spot in the future traffic map

Path Planning in the Local-Level Frame for Small Unmanned Aircraft Systems

In this chapter, we propose a 3D path planning algorithm for small unmanned aircraft systems (UASs). We develop the path planning logic using a body fixed relative coordinate system which is the unrolled, unpitched body frame. In this relative coordinate system, the ownship is fixed at the center of the coordinate system, and the detected intruder is located at a relative position and moves with a relative velocity with respect to the ownship. This technique eliminates the need to translate the sensor’s measurements from local coordinates to global coordinates, which saves computation cost and removes the error introduced by the transformation. We demonstrate and validate this approach using predesigned encounter scenarios in the Matlab/Simulink environment

RADAR Based Collision Avoidance for Unmanned Aircraft Systems

Unmanned Aircraft Systems (UAS) have become increasingly prevalent and will represent an increasing percentage of all aviation. These unmanned aircraft are available in a wide range of sizes and capabilities and can be used for a multitude of civilian and military applications. However, as the number of UAS increases so does the risk of mid-air collisions involving unmanned aircraft. This dissertation aims present one possible solution for addressing the mid-air collision problem in addition to increasing the levels of autonomy of UAS beyond waypoint navigation to include preemptive sensor-based collision avoidance. The presented research goes beyond the current state of the art by demonstrating the feasibility and providing an example of a scalable, self-contained, RADAR-based, collision avoidance system. The technology described herein can be made suitable for use on a miniature (Maximum Takeoff Weight \u3c 10kg) UAS platform. This is of paramount importance as the miniature UAS field has the lowest barriers to entry (acquisition and operating costs) and consequently represents the most rapidly increasing class of UAS

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

A Novel Collision Avoidance Logic for Unmanned Aerial Vehicles Using Real-Time Trajectory Planning

An effective collision avoidance logic should prevent collision without excessive alerting. This requirement would be even more stringent for an automatic collision avoidance logic, which is probably required by Unmanned Aerial Vehicles to mitigate the impact of delayed or lost link issues. In order to improve the safety performance and reduce the frequency of false alarms, this thesis proposes a novel collision avoidance logic based on the three-layer architecture and a real-time trajectory planning method. The aim of this thesis is to develop a real-time trajectory planning algorithm for the proposed collision avoidance logic and to determine the integrated logic’s feasibility, merits and limitations for practical applications. To develop the trajectory planning algorithm, an optimal control problem is formulated and an inverse-dynamic direct method along with a two stage, derivative-free pattern search method is used as the solution approach. The developed algorithm is able to take into account the flyability of three dimensional manoeuvres, the robustness to the intruder state uncertainty and the field-of-regard restriction of surveillance sensors. The testing results show that the standalone executable of the algorithm is able to provide a flyable avoidance trajectory with a maximum computation time less than 0.5 seconds. To evaluate the performance of the proposed logic, an evaluation framework for Monte Carlo simulations and a baseline approach for comparison are constructed. Based on five Monte Carlo simulation experiments, it is found that the proposed logic should be feasible as 1) it is able to achieve an update rate of 2Hz, 2) its safety performance is comparable with a reference requirement from another initial feasibility study, and 3) despite a 0.5 seconds computation latency, it outperforms the baseline approach in terms of safety performance and robustness to sensor and feedback error

Multi-agent Collision Avoidance Using Interval Analysis and Symbolic Modelling with its Application to the Novel Polycopter

Coordination is fundamental component of autonomy when a system is defined by multiple mobile agents. For unmanned aerial systems (UAS), challenges originate from their low-level systems, such as their flight dynamics, which are often complex. The thesis begins by examining these low-level dynamics in an analysis of several well known UAS using a novel symbolic component-based framework. It is shown how this approach is used effectively to define key model and performance properties necessary of UAS trajectory control. This is demonstrated initially under the context of linear quadratic regulation (LQR) and model predictive control (MPC) of a quadcopter. The symbolic framework is later extended in the proposal of a novel UAS platform, referred to as the ``Polycopter" for its morphing nature. This dual-tilt axis system has unique authority over is thrust vector, in addition to an ability to actively augment its stability and aerodynamic characteristics. This presents several opportunities in exploitative control design. With an approach to low-level UAS modelling and control proposed, the focus of the thesis shifts to investigate the challenges associated with local trajectory generation for the purpose of multi-agent collision avoidance. This begins with a novel survey of the state-of-the-art geometric approaches with respect to performance, scalability and tolerance to uncertainty. From this survey, the interval avoidance (IA) method is proposed, to incorporate trajectory uncertainty in the geometric derivation of escape trajectories. The method is shown to be more effective in ensuring safe separation in several of the presented conditions, however performance is shown to deteriorate in denser conflicts. Finally, it is shown how by re-framing the IA problem, three dimensional (3D) collision avoidance is achieved. The novel 3D IA method is shown to out perform the original method in three conflict cases by maintaining separation under the effects of uncertainty and in scenarios with multiple obstacles. The performance, scalability and uncertainty tolerance of each presented method is then examined in a set of scenarios resembling typical coordinated UAS operations in an exhaustive Monte-Carlo analysis

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

Real-Time Obstacle and Collision Avoidance System for Fixed-Wing Unmanned Aerial Systems

The motivation for the research presented in this dissertation is to provide a two-fold solution to the problem of non-cooperative reactive mid-air threat avoidance for fixed-wing unmanned aerial systems. The first phase is an offline UAS trajectory planning designed for an altitude-specific mission. The second phase leans on the results produced during the first phase to provide intelligent, real-time, reactive mid-air threat avoidance logic. That real-time operating logic provides a given fixed-wing UAS with local threat awareness so it can get a feel for the danger represented by a potential threat before using results produced during the first phase to require aircraft rerouting. The first original contribution of this research is the Advanced Mapping and Waypoint Generator (AMWG), a piece of software which processes publicly available elevation data in order to only retain the information necessary for a given altitude-specific flight mission. The AMWG is what makes systematic offline trajectory possible. The AMWG first creates altitude groups in order to discard elevations points which are not relevant to a specific mission because of the altitude flown at. Those groups referred to as altitude layers can in turn be reused if the original layer becomes unsafe for the altitude range in use, and the other layers are used for altitude re-scheduling in order to update the current altitude layer to a safer layer. Each layer is bounded by a lower and higher altitude, within which terrain contours are considered constant according to a conservative approach involving the principle of natural erosion. The AMWG then proceeds to obstacle contours extraction using threshold and edge detection vision algorithms. A simplification of those obstacle contours and their corresponding free space zones counterparts is performed using a fixed -tolerance Douglas-Peucker algorithm. This simplification allows free space zones to be described by vectors instead of point clouds, which enables UAS point location. The resulting geometry is then processed through a vertical trapezoidal decomposition where for each vertex defining a contour a vertical line is drawn, and the results of this decomposition is a set of trapezoidal cells. The cells corresponding to obstacle contours are then removed from the original trapezoidal decomposition in order to solely retain the obstacle-free trapezoidal cells. After decomposition, cells sharing part of a common edge are considered from a graph theory perspective so it becomes possible to list all acyclic paths between two cells by applying a depth first search (DFS) algorithm. The final product of the AWMG is a network of connected free space trapezoidal cells with embedded connectivity information referred to as the Synthetic Terrain Avoidance (STA network). The walls of the trapezoidal cells are then extruded as the AWMG essentially approximates a three-dimensional world by considering it as a stratification of two-dimensional layers, but the real-time phase needs 3D support. Using the graph conceptual view and the depth first search algorithm, all the connected cell sequences joining the departure to the arrival cell can be listed, a capability which is used during aircraft rerouting. By connecting two adjacent cells' centroids to their common midpoint located on the shared edge, the resulting flying legs remain within the two cells. The next step for paths between two cells is to be converted into flyable paths, and the conversion uses main and fallback methods to achieve that. The preferred method is the closed-form Dubins paths method involving the design of sequences of arc circle-straight line-arc circle (CLC) in order to account for the minimum radius turn constrain of the UAS. An additional geometric transformation is developed and applied to the initial waypoints used in the Dubins method so the flying leg directions are respected which is not possible by using the Dubins method alone. When consecutive waypoints are too close from one another, a condition called the Dubins condition cannot be respected, and the UAS trajectory design switches to the numerical integration of a system of ordinary differential equations accounting for the minimum turning constraint. Using the Dubins method and the ODE method makes it possible for the AWMG to design flyable offline trajectories accounting for the lateral dynamic of the fixed-wing UAS. The second original contribution of this research is the development and demonstration of the Double Dispersion reduction RRT (DDRRT), an algorithm which employs two new developed logic schemes respectively referred to as Punctual Dispersion Reduction (PDR), and Spatial Dispersion Reduction exploration (SDR). The DDRRT is employed during the real-time in-flight phase where it initially assumes a perfect terrain and no unpredictable threat, consequently following a 100% adaptive goal biasing toward the next waypoint in its list. When a threat such as an unpredicted obstacle is detected, the (PDR) acknowledges the fact that the DDRRT tree branches have met an obstacle and the its goal-biasing toward the next waypoint is decreased. If the PDR keeps decreasing, the DDRRT develops awareness of its surrounding obstacles by relaxing its PDR and switching to SDR which has the effect of increasing the dispersion of its branches, but keeping their extension bounded by the cell containing the last good position of the UAS, Csafe. If a number of branches reach a limit proportional to the Csafe and its relative area, then the STA network is queried for alternative rerouting. The two phases provide real-time reactive mid - air threat avoidance scenarios with the ability for a UAS to develop local and realistic threat awareness before considering intelligent rerouting. Either the local exploration of the DDRRT is successful before reaching a maximum number of points, or the STA Network is required to find another route

Next generation flight management systems for manned and unmanned aircraft operations - automated separation assurance and collision avoidance functionalities

The demand for improved safety, efficiency and dynamic demand-capacity balancing due to the rapid growth of the aviation sector and the increasing proliferation of Unmanned Aircraft Systems (UAS) in different classes of airspace pose significant challenges to avionics system developers. The design of Next Generation Flight Management Systems (NG-FMS) for manned and unmanned aircraft operations is performed by addressing the challenges identified by various Air Traffic Management (ATM) modernisation programmes and UAS Traffic Management (UTM) system initiatives. In particular, this research focusses on introducing automated Separation Assurance and Collision Avoidance (SA&CA) functionalities (mathematical models) in the NG-FMS. The innovative NG-FMS is also capable of supporting automated negotiation and validation of 4-Dimensional Trajectory (4DT) intents in coordination with novel ground-based Next Generation Air Traffic Management (NG-ATM) systems. One of the key research contributions is the development of a unified method for cooperative and non-cooperative SA&CA, addressing the technical and regulatory challenges of manned and unmanned aircraft coexistence in all classes of airspace. Analytical models are presented and validated to compute the overall avoidance volume in the airspace surrounding a tracked object, supporting automated SA&CA functionalities. The scientific basis of this approach is to assess real-time measurements and associated uncertainties affecting navigation states (of the host aircraft platform), tracking observables (of the static or moving object) and platform dynamics, and translate them to unified range and bearing uncertainty descriptors. The SA&CA unified approach provides an innovative analytical framework to generate high-fidelity dynamic geo-fences suitable for integration in the NG-FMS and in the ATM/UTM/defence decision support tools