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Evaluation of drag coefficient for a quadrotor model
No full textInternational audienceThis paper focuses on the quadrotor drag coefficient model and its estimation from flight tests. Precise assessment of such a model permits the use of a quadrotor as a sensor for wind estimation purposes without the need for additional onboard sensors. Firstly, the drag coefficient has been estimated in a controlled environment via wind generator and motion capture system. Later, the evolution of the coefficient is observed for various mass and fuselage shapes. Finally, an estimation method is proposed, based on the least-squares optimization, that evaluates the drag of the quadrotor directly from outdoor flight data. The latter leads the methodology towards easier adoption in other researchers’ systems without the need for complex and expensive flight testing facilities. The accuracy of the proposed method is presented both in simulation, based on a realistic flight dynamics model, and also for real outdoor flights
Automatic In Flight Conflict Resolution for Urban Air Mobility using Fluid Flow Vector Field based Guidance Algorithm
No full textInternational audienceIn this study, a vector field-based guidance algorithm is presented for tactical deconfliction in urban air mobility. The proposed guidance algorithm mimics fluid flow around obstacles to generate a vector field that guide vehicles through flight corridors while ensuring collision avoidance. The algorithm is flexible and can be tailored to fit the needs of current air traffic regulations. The conflict resolution and corridor following capability of the proposed method is evaluated through extensive flight test campaign. Flight tests are conducted in a scaled urban environment for two different scenarios: the conflict between of two medical drones in the same corridor and the conflict of an air taxi and a non-cooperative intruder. Both scenarios are tested on simple and complex shaped corridors using different corridor models, offering varying degrees of strictness and freedom of movement. Experimental results demonstrate the algorithm’s ability to resolve conflicts and ensure safe navigation within designated corridors. The proposed guidance algorithm shows promise as a tool for tactical deconfliction and autonomous navigation in urban airspace
Autoencoder Neural Networks for LPV Embedding of Nonlinear Systems
No full textInternational audienceIn this paper, the problem of automated generation of linear parameter-varying (LPV) state-space models is addressed. A deep neural network (DNN) is developed to embed the dynamical behavior of a nonlinear (NL) system into an LPV model with predefined number of scheduling variables which are the NL functions of the states. Leveraging the Autoencoder (AE) neural networks (NN) and using the input-output plant data, a scheduling NL mapping is defined. The developed LPV model depends affinely on the scheduling variables. Since the proposed method to derive LPV model is based on input-output plant data, the explicit NL equations of the plant are not required. The upper and lower bounds on the scheduling variables can be computed by solving convex optimization problems. The effectiveness of the proposed method is evaluated on a benchmark example
EPLO : Free-space optics emulator for satellite ground link
No full textInternational audienceThis paper presents the EPLO project (Emulateur Propagation Libre Optique), dedicated to modelling and emulating the atmospheric effects on a laser link used for ground-satellite communication. To test different modulation wave forms and their robustness to the atmospheric turbulence, an experimental bench to reproduce the front phase deformation and the effect of beam spreading is in development. This emulator generates various scenarios of turbulence. Due to the utilization of holographic methods, it also includes a beam wander effect. The turbulence patterns driving the bench are calculated through the development of Zernike polynomials, to reproduce the effect of weak or strong turbulences in terms of scintillation indexes, beam wandering, beam spreading, phase variation and the dynamic of the temporal evolution. This enables the simulation and the study of the wave front deformations when the laser passes through the atmosphere
Stratégies d'optimisation pour la réduction des interférences radars
No full textAs the number of vehicles using Advanced Driver Assistance Systems (ADAS) is increasing, so does the number of vehicles equipped with automotive radars. Indeed, market studies estimate that by 2030, 50% of vehicles will be equipped with automotive radars. This rapid growth in radar numbers will likely increase the risk of harmful interference as specifications from standardisation bodies (e.g., ETSI) provide requirements in terms of maximum and mean power, but do not mandate specific radar waveforms nor Common Channel Access Policies (CCAP). Nowadays, automotive radar interference mitigation is done primarily with signal processing techniques and parameter randomisation. These techniques work well today, as the number of radars is low, but they won't suffice in a situation where the majority of cars are equipped with radars. New interference mitigation techniques are becoming important to ensure the long-term correct operation of radars and upper-layer ADAS systems that depend on them in this complex environment. This thesis is devoted to studying the mitigation capabilities of today's methods in future environments with a lot more radars, and investigating new methods making use of the Vehicle-To-Everything (V2X) technology as a side communication channel and Artificial Intelligence (AI) to minimise the amount of interference and optimise the automotive radar band usage. As no data is available to study large-scale road traffic situations with automotive radars and V2X communications, the first part of this thesis focuses on the Python-based simulator that has been built to generate this data and investigate different mitigation techniques. The proposed simulator aims at reproducing what will happen with different mitigation methods in realistic future scenarios. This goal is achieved by simulating realistic scenarios using the Simulation of Urban MObility (SUMO) software, while making reasonable assumptions to lower the computation time. The lack of specifications regarding radar waveforms and CCAP makes optimisation of the radar band more complicated in complex environments. The aim of the second part of the thesis is to investigate the potential of current mitigation techniques in such an environment without CCAP. In addition, new methods using V2X data have been proposed. These methods based on radar orientation, or based on Genetic Algorithms (GA) have been implemented to improve the waveform parameters selection process. In the situation where a CCAP is implemented, the avoidance of interference becomes easier to deal with. The third part of this thesis investigates simple interference mitigation strategies in case of a CCAP where the radar is organised in orthogonal resources that radars must share. By introducing orthogonal resources, the problem is also translated into a dynamic graph K-coloring problem whose optimal solution is approximated with two proposed metaheuristics based on Simulated Annealing (SA) and GA. From these results is then proposed a new mitigation strategy, based on V2X and radar orientation to minimise the amount of interference in case of CCAP. Finally, the complexity of such a multi-agent problem makes AI an interesting candidate for interference mitigation. In the last part of the thesis, Reinforcement Learning (RL) using Artificial Neural Network (ANN) is investigated for radar waveform parameters selection based on V2X data, and Graph Neural Networks (GNN) are used for radar line-of-sights estimations.À mesure que le nombre de véhicules équipés de systèmes avancés d'aide à la conduite (ADAS) augmente, il en va de même pour le nombre de véhicules équipés de radars automobiles. En effet, les études de marché estiment qu'en 2030, 50 % des véhicules seront équipés de radars automobiles. Cette croissance rapide du nombre de radars risque d'augmenter le risque d'interférences nuisibles. En effet, les spécifications des organismes de régulation (par exemple, l'ETSI) établissent des exigences en termes de puissance maximale et moyenne, mais ne prescrivent ni de formes d'onde radar spécifiques ni de politiques d'accès au canal commun (CCAP), rendant la coordination de l'utilisation de la bande de fréquence difficile. Actuellement, la réduction des interférences des radars automobiles repose principalement sur des techniques de traitement du signal et de randomisation des paramètres. Ces techniques fonctionnent bien aujourd'hui, en raison du faible nombre de radars, mais elles ne seront pas suffisantes dans une situation où la majorité des voitures seront équipées de radars. De nouvelles techniques de réduction des interférences deviennent importantes pour garantir le bon fonctionnement à long terme des radars et des systèmes ADAS de couche supérieure qui en dépendent dans cet environnement complexe. Cette thèse est consacrée à l'étude des capacités de réduction des interférences des méthodes actuelles dans de futurs environnements comportant beaucoup plus de radars, et à l'exploration de nouvelles méthodes utilisant la technologie Vehicle-To-Everything (V2X) comme canal de communication et l'intelligence artificielle (IA) pour minimiser les interférences et optimiser l'utilisation de la bande de fréquence. Comme aucune donnée n'est disponible pour étudier des situations de trafic routier à grande échelle avec des radars automobiles et des communications V2X, la première partie de cette thèse se concentre sur le simulateur en Python qui a été développé pour générer ces données. Le simulateur proposé vise à reproduire ce qui se produira avec différentes méthodes de réduction des interférences dans des scénarios réalistes. Cet objectif est atteint en utilisant des scénarios réalistes générés à l'aide du logiciel Simulation of Urban MObility (SUMO), tout en émettant des hypothèses raisonnables pour réduire le temps de calcul. L'objectif de la deuxième partie de la thèse est d'étudier le potentiel des techniques actuelles de réduction d' interférences dans un environnement sans CCAP. De plus, de nouvelles méthodes utilisant des données V2X ont été proposées. Ces méthodes basées sur l'orientation radar, ou basées sur des algorithmes génétiques (GA), ont été mises en œuvre pour améliorer le processus de sélection des paramètres des formes d'onde radar. Dans la situation où un CCAP est mis en œuvre, l'évitement des interférences devient plus facile à gérer. La troisième partie de cette thèse explore des stratégies simples de réduction des interférences en cas de CCAP et où la bande de fréquence est organisé en ressources orthogonales que les radars doivent se partager. En introduisant des ressources orthogonales, le problème est également traduit en un problème de K-coloration de graphe dynamique dont la solution optimale est approximée avec deux métaheuristiques proposées basées sur le recuit simulé (SA) et le GA. À partir de ces résultats, une nouvelle stratégie de réduction des interférences est proposée, basée sur le V2X et l'orientation des radars, afin de minimiser la quantité d'interférence en cas de CCAP. Enfin, la complexité d'un tel problème multi-agents fait de l'IA un candidat intéressant. Dans la dernière partie de la thèse, l'apprentissage par renforcement (RL) utilisant un réseau neuronal artificiel (ANN) est étudié pour la sélection des paramètres des formes d'onde radar basée sur les données V2X, et les réseaux neuronaux graphiques (GNN) sont utilisés pour les estimations de ligne de vue entre radars
Curve clustering based on second order information: application to bad runway condition detection
No full textIn air transportation, a huge amount of data is continuously recorded such as radar tracks that may be used for improving flight as well as airport safety. However, all known statistical algorithms, even those based on functional data, are unable to distinguish between a safety critical flight and another one departing from standard behavior, but otherwise safe. It is the case in airport safety when radar measurements are used for detecting incidents on airport surface. In this paper, we propose a change of paradigm by switching from a functional data framework to a geometrical one by representing curves as points in a shape manifold. In this way, any intrinsic structure of the data that is amenable to geometry can be directly encoded in the representation space. Based on an extension of a classical distance between shapes, a new one is defined, that explicitly takes into account the second derivative and can be related to slippery. Its properties are investigated in a first part, then some results on datasets of synthetic and real trajectories are presented
The double task-switching protocol: An investigation into the effects of similarity and conflict on cognitive flexibility in the context of mental fatigue
No full textInternational audienceConsiderable fundamental studies have focused on the mechanisms governing cognitive flexibility and the associated costs of switching between tasks. Task-switching costs refer to the phenomenon that reaction times and accuracy decrease briefly following the switch from one task to another. However, cognitive flexibility also impacts day-to-day life in many complex work environments where operators have to perform several different tasks. One major difference between typical tasks examined in fundamental studies and real-world applications is that fundamental studies often rely on much more similar tasks, which is not the case for real-world applications. In the latter, operators may switch between vastly dissimilar tasks. Therefore, this behavioural study aims to test if task-switching costs are different for switches between similar and dissimilar tasks. The proposed protocol has participants switch between 2 pairs of two tasks each. Between pairs, there is more dissimilarity, while the two tasks within each pair are more similar. In addition, this study examines the impact of mental fatigue and interference in form of confounding information on cognitive flexibility. To induce mental fatigue the participants’ breaks between blocks will be limited. We expect that dissimilarity between tasks will result in greater task-switching costs
A Multivariate Functional Data Analysis of Aircraft Trajectories
No full textWhile advanced methods for functional data analysis have recently been developed in the literature, applications to aircraft trajectories have remained scarce, despite operational relevance. One reason is the practical difficulties affiliated with the multivariate nature of trajectories and associated physical constraints. Indeed, an aircraft trajectory usually involves three dimensions in space (longitude, latitude, altitude) but also weather values (say wind speed and direction), each dimension having its specificities. To name a few, smoothing altitude values requires to ensure both non-negativity and boundary constraints. Wind directions have support on the unit circle. Additional to constrained smoothing challenges, phase variations are to be taken into account as flights are never of the same duration. To tackle these issues, two smoothing methods respectively based on constrained splines and asymmetric kernels are implemented on real data. For each approach, two strategies to handle the circular nature of wind directions are compared. Registration is performed. A joint pointwise test is proposed to demonstrate that delayed flights have experienced less favorable wind conditions
Double-scale theory
No full textWe present a new interpretation of quantum mechanics, called the double-scale theory, which expends on the de Broglie-Bohm (dBB) theory. It is based, for any quantum system, on the simultaneous existence of two wave functions in the laboratory reference frame : an external wave function and an internal one. The external wave function is the wave function of the center-of-mass, as for the dBB theory : the wave is a field that pilots the center-of-mass of the quantum system. The external wave spreads out in space over time. Mathematically, the Schrödinger equation converges to the Hamilton-Jacobi statistical equations when the Planck constant tends towards zero and the Newton trajectories are therefore approximations of the dBB trajectories. The simultaneous existence of an internal wave function, in addition to the external one, is the original element of our theory. This internal wave corresponds to the interpretation proposed by Edwin Schrödinger for whom the particle is extended. Then, the internal wave remains confined in space. Its converges, when h→0, to a Dirac distribution. Moreover, the configuration space of dimension 3N of the internal wave function can be written as the product of N individual internal wave functions of dimension 3
« L’aéronautique toulousaine : des représentations sociales à sa patrimonialisation ou comment co-construire l’aviation de demain entre les experts du secteur et les habitants ?"
No full textCe rapport de recherche est issu d’une collaboration entre le LERASS (Laboratoire d’Études et de Recherches Appliquées en Sciences Sociales) et l’ENAC (École Nationale de l’Aviation Civile). Il est l’aboutissement d’une recherche exploratoire qui s’est déroulée du 1er avril au 30 septembre 2022 financée par la Maison des Sciences de l’Homme et de la Société de Toulouse (MSHS-T) avec la participation de deux stagiaires en sociologie de l’Université Toulouse Jean-Jaurès. Il s’agit dans cette recherche de repérer les représentations sociales de l’aéronautique toulousaine, d’interroger leurs liens avec le patrimoine aéronautique, cela auprès des professionnel.elle.s de l’aéronautique ainsi que des Toulousain.e.s. Le projet vise à explorer les conditions d’une co-construction de l’aéronautique de demain