Flight Trajectory Optimization Through Genetic Algorithms for Lateral and Vertical Integrated Navigation

Patrón, Roberto Salvador Félix; Botez, Ruxandra Mihaela

doi:10.2514/1.i010348

Cited by 30 publications

(9 citation statements)

References 20 publications

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“…The flight trajectory method proposed in ref. (34) is similar to that presented in ref. (33), with the difference that the lateral and vertical flight profiles are optimised simultaneously.…”

Section: Introductionsupporting

confidence: 91%

New flight trajectory optimisation method using genetic algorithms

Dancila

Botez

2021

Aeronaut. j.

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This paper presents a new flight trajectory optimisation method, based on genetic algorithms, where the selected optimisation criterion is the minimisation of the total cost. The candidate flight trajectories evaluated in the optimisation process are defined as flight plans with two components: a lateral flight plan (the set of geographic points that define the flight trajectory track segments) and a vertical flight plan (the set of data that define the altitude and speed profiles, as well as the points where the altitude and/or speed changes occur). The lateral components of the candidate flight plans are constructed by selecting a set of adjacent nodes from a routing grid. The routing grid nodes are generated based on the orthodromic route between the flight trajectory’s initial and final points, a selected maximum lateral deviation from the orthodromic route and a selected grid node step size along and across the orthodromic route. Two strategies are investigated to handle invalid flight plans (relative to the aircraft’s flight envelope) and to compute their flight performance parameters. A first strategy is to assign a large penalty total cost to invalid flight profiles. The second strategy is to adjust the invalid flight plan parameters (altitude and/or speed) to the nearest limit of the flight envelope, with priority being given to maintaining the planned altitude. The tests performed in this study show that the second strategy is computationally expensive (requiring more than twice the execution time relative to the first strategy) and yields less optimal solutions. The performance of the optimal profiles identified by the proposed optimisation method, using the two strategies regarding invalid flight profile performance evaluation, were compared with the performance data of a reference flight profile, using identical input data: initial aircraft weight, initial and final aircraft geographic positions, altitudes and speed, cost index, and atmospheric data. The initial and final aircraft geographic positions, and the reference flight profile data, were retrieved from the FlightAware web site. This data corresponds to a real flight performed with the aircraft model used in this study. Tests were performed for six Cost Index values. Given the randomness of the genetic algorithms, the convergence to a global optimal solution is not guaranteed (the solution may be non-optimal or a local optima). For a better evaluation of the performance of the proposed method, ten test runs were performed for each Cost Index value. The total cost reduction for the optimal flight plans obtained using the proposed method, relative to the reference flight plan, was between 0.822% and 3.042% for the cases when the invalid flight profiles were corrected, and between 1.598% and 3.97% for the cases where the invalid profiles were assigned a penalty total cost.

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Section: Introductionsupporting

confidence: 91%

New flight trajectory optimisation method using genetic algorithms

Dancila

Botez

2021

Aeronaut. j.

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“…[11] Flight Trajectory Optimization Through Genetic Algorithms for Lateral and Vertical Integrated Navigation. [12] Knowledge Unit 2. Airspace sectorisation…”

Section: Example Of Research Topicsmentioning

confidence: 99%

Body of Knowledge in Research of Air Traffic Management: Case Study in Indonesi

Ruseno¹,

Sadono

2019

Angkasa

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Air Traffic Management (ATM) is a discipline that manages the movement of flights in the air and on the ground. Its functions are to maintain the safety level required by the authority and to provide the capacity required by the airlines. The challenges in ATM come from the in-balance between the market growth and the available infrastructure and also the gap between the regulations and the technology. The research in ATM provides the solutions by introducing new methods or technologies to cope with those challenges. The effort in organizing and cataloging the Body of Knowledge (BOK) in research of ATM is presented in this report. This BOK is a complete set of research concepts and activities in term of managing the air traffic to improve the air transportation safety and its capacity. Considering the latest publications in the last 5 years, the BOK consists of five areas of operation which are enroute, arrival, terminal, departure and connectivity. Each area of operations consists of several knowledge units that contain several research topics. In this report, a special attention is given to the development of ATM research in Indonesian. It characterizes with the challenges that commonly faced in Indonesia such as high density air traffic, in-sufficient infrastructure/technology, in-balance demand and supply during peak hours and major disruption by natural disasters (volcanoes and earth quakes).The report summarizes that the trend of ATM research in Indonesia is in the knowledge unit of capacity optimization. Additionally, it recommends to explore research activities by implementing new air traffic concepts such as the trajectory based operation and the integrated of departure and arrival management to improve capacity, efficiency and safety.

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“…Such tasks might include aircraft trajectory optimization [2] and its application into flight management systems [3], which aim to reduce operational costs [4], fuel consumption, and adverse environmental side effects [5]. A variety of algorithms, such as genetic algorithm (GA) [6], particle swarm optimization (PSO) [7], ant colony [8], bee colony [9], beam search [10], and harmony search [11] have been employed to solve aircraft trajectory optimization problems. However, the main aim of avionics control systems is aerial collision avoidance [12], where ATP contributes to solving encounter scenarios efficiently.…”

Section: Introductionmentioning

confidence: 99%

New Reliability Studies of Data-Driven Aircraft Trajectory Prediction

Hashemi¹,

Botez²,

Grigorie

2020

Aerospace

Self Cite

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Two main factors, including regression accuracy and adversarial attack robustness, of six trajectory prediction models are measured in this paper using the traffic flow management system (TFMS) public dataset of fixed-wing aircraft trajectories in a specific route provided by the Federal Aviation Administration. Six data-driven regressors with their desired architectures, from basic conventional to advanced deep learning, are explored in terms of the accuracy and reliability of their predicted trajectories. The main contribution of the paper is that the existence of adversarial samples was characterized for an aircraft trajectory problem, which is recast as a regression task in this paper. In other words, although data-driven algorithms are currently the best regressors, it is shown that they can be attacked by adversarial samples. Adversarial samples are similar to training samples; however, they can cause finely trained regressors to make incorrect predictions, which poses a security concern for learning-based trajectory prediction algorithms. It is shown that although deep-learning-based algorithms (e.g., long short-term memory (LSTM)) have higher regression accuracy with respect to conventional classifiers (e.g., support vector regression (SVR)), they are more sensitive to crafted states, which can be carefully manipulated even to redirect their predicted states towards incorrect states. This fact poses a real security issue for aircraft as adversarial attacks can result in intentional and purposely designed collisions of built-in systems that can include any type of learning-based trajectory predictor.

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Flight Trajectory Optimization Through Genetic Algorithms for Lateral and Vertical Integrated Navigation

Cited by 30 publications

References 20 publications

New flight trajectory optimisation method using genetic algorithms

New flight trajectory optimisation method using genetic algorithms

Body of Knowledge in Research of Air Traffic Management: Case Study in Indonesi

New Reliability Studies of Data-Driven Aircraft Trajectory Prediction

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