Behavioral Learning of Aircraft Landing Sequencing Using a Society of Probabilistic Finite State Machines

Air Traffic Control (ATC) is a complex safety critical environment. A tower controller would be making many decisions in real-time to sequence aircraft. While some optimization tools exist to help the controller in some airports, even in these situations, the real sequence of the aircraft adopted by the controller is significantly different from the one proposed by the optimization algorithm. This is due to the very dynamic nature of the environment. The objective of this paper is to test the hypothesis that one can learn from the sequence adopted by the controller some strategies that can act as heuristics in decision support tools for aircraft sequencing. This aim is tested in this paper by attempting to learn sequences generated from a well-known sequencing method that is being used in the real world. The approach relies on a genetic algorithm (GA) to learn these sequences using a society Probabilistic Finite-state Machines (PFSMs). Each PFSM learns a different sub-space; thus, decomposing the learning problem into a group of agents that need to work together to learn the overall problem. Three sequence metrics (Levenshtein, Hamming and Position distances) are compared as the fitness functions in GA. As the results suggest, it is possible to learn the behavior of the algorithm/heuristic that generated the original sequence from very limited information

A Formal Framework for Modeling and Prediction of Aircraft Operability using SysML

Aircraft operability characterizes the ability of anaircraft to meet operational requirements in terms of reliability, availability, risks and costs. Airlines policy must cope with operational decision-making and maintenance planning to handle the impacts of any event that generates a maintenance demand during operations. Aircraft operability is therefore considereda major requirement by each airline. The subject reaches a complexity level that deserves investigations in a Model-Based System Engineering (MBSE) approach enabling abstractions, as well as simulation and formal verification of models. In this paper, aircraft operability is modeled using Finite State Machines(FSM) supported by SysML. Simulation and model checking techniques are used to evaluate the impact of an event on airline operations using operability Key Performance Indicators (KPIs)such as reliability, availability and cost. The modeling frameworkis demonstrated on a case study of air-conditioning pack. This kind of operability analysis helps to project the potential impactof aircraft design on airline operations early in the aircraft development

Review of Current State of Artificial Intelligence/Machine Learning and Other Advanced Techniques Related to Air-to-Air Collision Risk Models (CRM) in the Terminal Airspace

693KA9-20-D-00004DTFACT-14-D-00004Collision Risk Models (CRM) are used by regulatory safety agencies to determine the safe separation minima and monitor the air-to-air collision risk level of an airspace. CRMs estimate the expected number of aircraft collisions and "total" risk for a given air traffic concept-of-operation (e.g., parallel approaches). The fidelity of the models, and assumptions used in the models, are determined by the required confidence interval required for the safety analysis, the capabilities of current analytical and simulation methods, availability of empirical data sets, and the capabilities of computational resources. This paper provides an overview of the state-of-the-art CRMs for terminal area operations. Opportunities to apply recently developed artificial intelligence/machine learning (AI/ML), and data analytics methods such as analytical and rare-event simulation methods, availability of empirical data sets, and leverage available computational resources are identified

USING MODELING TO PREDICT THE EFFECTS OF AUTOMATION ON MEDEVAC PILOT COGNITIVE WORKLOAD

The Holistic Situational Awareness - Decision Making (HSA-DM) program is researching ways to aid pilots via avionics essential to the Future Vertical Lift (FVL) rotor-wing platform. As pilots manage the new avionics that FVL will bring to the battlefield, automation assistance will be essential. This study’s goal is to determine to what extent automation reduces pilot cognitive workload particularly when performing communication tasks. The quantitative analysis is based on cognitive walkthroughs with active-duty helicopter pilots. Pilot interviews were also conducted to assess how tasks are completed, and more importantly, to ascertain the cognitive workload associated with those tasks. This information is implemented into computer models of a routine helicopter flight to accurately predict pilot workload during a mission. These models also predict which tasks would add the most value to pilots and FVL if automated mission tasks were implemented. The research indicates that by automating communication tasks for the pilot and copilot, workload is reduced to an optimal level. Based on these findings, monitor radio nets, adjust volume, input channel, select channel, and send JVMF messages should be automated. In addition, this analysis establishes a cost-effective, valid, and repeatable framework for future workload studies on automated tasks in FVL.Major, United States Army ReserveMajor, United States ArmyCaptain, United States ArmyMajor, United States ArmyMajor, United States ArmyApproved for public release. Distribution is unlimited

Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems

Recent successes combine reinforcement learning algorithms and deep neural networks, despite reinforcement learning not being widely applied to robotics and real world scenarios. This can be attributed to the fact that current state-of-the-art, end-to-end reinforcement learning approaches still require thousands or millions of data samples to converge to a satisfactory policy and are subject to catastrophic failures during training. Conversely, in real world scenarios and after just a few data samples, humans are able to either provide demonstrations of the task, intervene to prevent catastrophic actions, or simply evaluate if the policy is performing correctly. This research investigates how to integrate these human interaction modalities to the reinforcement learning loop, increasing sample efficiency and enabling real-time reinforcement learning in robotics and real world scenarios. This novel theoretical foundation is called Cycle-of-Learning, a reference to how different human interaction modalities, namely, task demonstration, intervention, and evaluation, are cycled and combined to reinforcement learning algorithms. Results presented in this work show that the reward signal that is learned based upon human interaction accelerates the rate of learning of reinforcement learning algorithms and that learning from a combination of human demonstrations and interventions is faster and more sample efficient when compared to traditional supervised learning algorithms. Finally, Cycle-of-Learning develops an effective transition between policies learned using human demonstrations and interventions to reinforcement learning. The theoretical foundation developed by this research opens new research paths to human-agent teaming scenarios where autonomous agents are able to learn from human teammates and adapt to mission performance metrics in real-time and in real world scenarios.Comment: PhD thesis, Aerospace Engineering, Texas A&M (2020). For more information, see https://vggoecks.com

Developments in predictive displays for discrete and continuous tasks

The plan of the thesis is as follows: The introductory chapters review the literature pertaining to human prediction and predictive control models (Chapter 1), and to engineering aspects of predictive displays (Chapter 2). Chapter 3 describes a fundamental study of predictive display parameters in a laboratory scheduling task, Chapter 4 attempts to verify these findings using test data from an actual job shop scheduling problem. Chapter 5 branches into the area of continuous control with a pilot study of predictive displays in a laboratory simulated continuous stirred-tank chemical reactor. Chapter 6 uses the experience gained in the pilot study as the basis for a comprehensive study of predictive display parameters in a further laboratory study of a simplified dual-meter monitoring and control task, and Chapter 7 attempts to test the optimal design in a part-simulated semi-batch chemical reactor using real plant and experienced operators in an industrial setting. The results of the experimental programme are summarized for convenience in Chapter 8. Chapter 9 draws together the threads from the various experiments and discusses the findings in terms of a general hierarchical model of an operator's control and monitoring behaviour. Finally, Chapter 10 presents conclusions and recommendations from the programme of research, together with suggestions for further work

Aeronautical engineering: A continuing bibliography with indexes (supplement 277)

This bibliography lists 467 reports, articles, and other documents introduced into the NASA scientific and technical information system in Mar. 1992. Subject coverage includes: the engineering and theoretical aspects of design, construction, evaluation, testing, operation, and performance of aircraft (including aircraft engines); and associated aircraft components, equipment, and systems. It also includes research and development in ground support systems, theoretical and applied aspects of aerodynamics, and general fluid dynamics

12th EASN International Conference on "Innovation in Aviation & Space for opening New Horizons"

Epoxy resins show a combination of thermal stability, good mechanical performance, and durability, which make these materials suitable for many applications in the Aerospace industry. Different types of curing agents can be utilized for curing epoxy systems. The use of aliphatic amines as curing agent is preferable over the toxic aromatic ones, though their incorporation increases the flammability of the resin. Recently, we have developed different hybrid strategies, where the sol-gel technique has been exploited in combination with two DOPO-based flame retardants and other synergists or the use of humic acid and ammonium polyphosphate to achieve non-dripping V-0 classification in UL 94 vertical flame spread tests, with low phosphorous loadings (e.g., 1-2 wt%). These strategies improved the flame retardancy of the epoxy matrix, without any detrimental impact on the mechanical and thermal properties of the composites. Finally, the formation of a hybrid silica-epoxy network accounted for the establishment of tailored interphases, due to a better dispersion of more polar additives in the hydrophobic resin

A Visual Language for Composable Simulation Scenarios

Modeling and Simulation plays an important role in how the Air Force trains and fights, Scenarios are used in simulation to give users the ability to specify entities and behaviors that should be simulated by a model: however, building and understanding scenarios can be a difficult and time-consuming process, furthermore, as composable simulations become more prominent, the need for a common descriptor for simulation scenarios has become evident. In order to reduce the complexity of creating and understanding simulation scenarios, a visual language was created, The research on visual languages presented in this thesis examines methods of visually specifying the high-level behavior of entities in scenarios and how to represent the hierarchy of the entities in scenarios. Through a study of current behavior specification techniques and the properties of mission-level simulation scenarios, Simulation Behavior Specification Diagrams (SBSD) were developed to represent the behavior of entities in scenarios, Additionally, the information visualization technique of treemaps was adapted to represent the hierarchy of entities in scenarios, After completing case studies on scenarios for the OneSAF simulation model, SBSDs and the application of treemaps to scenarios was considered successful, SBSD diagrams accurately represented the behavior of entities in the simulation scenarios and through software can be converted into code for use by simulation models, The treemap displayed the hierarchy of the entities along with information about the relative size of the entities when applied to simulation scenarios

ESMD Space Grant Faculty Project

No abstract availabl