[pt] Robôs híbridos equipados com pernas e rodas são uma solução promissora para uma locomoção versátil em terrenos acidentados. Eles combinam a velocidade e a eficiência das rodas com a capacidade das pernas de atravessar terrenos com obstáculos. Em geral, os desafios em locomoção para robôs híbridos envolvem planejamento de trajetória e sistemas de controle para o rastreamento da trajetória planejada. Esta tese se concentra, em particular, na tarefa de otimização de trajetória para robôs híbridos que navegam em terrenos acidentados. Para isso, propõe-se um algoritmo de planejamento que otimiza a posição e a orientação da base do robô e as posições e forças de contato nas rodas em uma formulação única, levando em consideração as informações do terreno e a dinâmica do robô. O robô é modelado como um único corpo rígido com massa e inércia concentrada no centro de massa, o que permite planejar movimentos complexos por longos horizontes de tempo e ainda manter uma baixa complexidade computacional para resolver a otimização de forma mais eficiente. O conhecimento do mapa do terreno permite que a otimização gere trajetórias para negociação de obstáculos de maneira dinâmica, em velocidades mais altas. Tais movimentos não podem ser gerados sem levar em consideração as informações do terreno. Duas formulações diferentes são apresentadas, uma que permite movimentos somente com as rodas, onde a negociação de obstáculos é permitida pelas pernas, e outra focada em movimentos híbridos dando passos e movendo as rodas, capazes de lidar com descontinuidades no perfil do terreno. A otimização é formulada como um NLP e as trajetórias obtidas são rastreadas por um controlador hierárquico que computa os comandos de atuação de torque para as juntas e as rodas do robô. As trajetórias são verificadas no robô quadrúpede ANYmal equipado com rodas não esterçáveis controladas por torque, em simulações e testes experimentais. O algoritmo proposto de otimização de trajetória permite que robôs com pernas e rodas naveguem por terrenos complexos, contendo, por exemplo, degraus, declives e escadas, enquanto negociam esses obstáculos com movimentos dinâmicos. / [en] Wheeled-legged robots are an attractive solution for versatile locomotion in challenging terrain. They combine the speed and efficiency of wheels with the ability of legs to traverse challenging terrain. In general, the challenges with wheeled-legged locomotion involve trajectory generation and motion control for trajectory tracking. This thesis focuses in particular on the trajectory optimization task for wheeled-legged robots navigating in challenging terrain. For this, a motion planning framework is proposed that optimizes over the robot’s base position and orientation, and the wheels’ positions and contact forces in a single planning problem, taking into account the terrain information and the robot dynamics. The robot is modeled as a single rigid-body, which allows to plan complex motions for long time horizons and still keep a low computational complexity to solve the optimization quickly. The knowledge of the terrain map allows the optimizer to generate feasible motions for obstacle negotiation in a dynamic manner, at higher speeds. Such motions cannot be discovered without taking into account the terrain information. Two different formulations allow for either purely driving motions, where obstacle negotiation is enabled by the legs, or hybrid driving-walking motions, which are able to overcome discontinuities in the terrain profile. The optimization is formulated as a Nonlinear Programming Problem (NLP) and the reference motions are tracked by a hierarchical whole-body controller that computes the torque actuation commands for the robot. The trajectories are verified on the quadrupedal robot ANYmal equipped with non-steerable torque-controlled wheels in simulations and experimental tests. The proposed trajectory optimization framework enables wheeled-legged robots to navigate over challenging terrain, e.g., steps, slopes, stairs, while negotiating these obstacles with dynamic motions.

Cette thèse porte sur l'utilisation de données de vols pour l'optimisation de trajectoires de montée vis-à-vis de la consommation de carburant.Dans un premier temps nous nous sommes intéressé au problème d'identification de modèles de la dynamique de l'avion dans le but de les utiliser pour poser le problème d'optimisation de trajectoire à résoudre. Nous commençont par proposer une formulation statique du problème d'identification de la dynamique. Nous l'interpretons comme un problème de régression multi-tâche à structure latente, pour lequel nous proposons un modèle paramétrique. L'estimation des paramètres est faite par l'application de quelques variations de la méthode du maximum de vraisemblance.Nous suggérons également dans ce contexte d'employer des méthodes de sélection de variable pour construire une structure de modèle de régression polynomiale dépendant des données. L'approche proposée est une extension à un contexte multi-tâche structuré du bootstrap Lasso. Elle nous permet en effet de sélectionner les variables du modèle dans un contexte à fortes corrélations, tout en conservant la structure du problème inhérente à nos connaissances métier.Dans un deuxième temps, nous traitons la caractérisation des solutions du problème d'optimisation de trajectoire relativement au domaine de validité des modèles identifiés. Dans cette optique, nous proposons un critère probabiliste pour quantifier la proximité entre une courbe arbitraire et un ensemble de trajectoires échantillonnées à partir d'un même processus stochastique. Nous proposons une classe d'estimateurs de cette quantitée et nous étudions de façon plus pratique une implémentation nonparamétrique basé sur des estimateurs à noyau, et une implémentation paramétrique faisant intervenir des mélanges Gaussiens. Ce dernier est introduit comme pénalité dans le critère d'optimisation de trajectoire dans l'objectif l'intention d'obtenir directement des trajectoires consommant peu sans trop s'éloigner des régions de validité. / This thesis deals with the use of flight data for the optimization of climb trajectories with relation to fuel consumption.We first focus on methods for identifying the aircraft dynamics, in order to plug it in the trajectory optimization problem. We suggest a static formulation of the identification problem, which we interpret as a structured multi-task regression problem. In this framework, we propose parametric models and use different maximum likelihood approaches to learn the unknown parameters.Furthermore, polynomial models are considered and an extension to the structured multi-task setting of the bootstrap Lasso is used to make a consistent selection of the monomials despite the high correlations among them.Next, we consider the problem of assessing the optimized trajectories relatively to the validity region of the identified models. For this, we propose a probabilistic criterion for quantifying the closeness between an arbitrary curve and a set of trajectories sampled from the same stochastic process. We propose a class of estimators of this quantity and prove their consistency in some sense. A nonparemetric implementation based on kernel density estimators, as well as a parametric implementation based on Gaussian mixtures are presented. We introduce the later as a penalty term in the trajectory optimization problem, which allows us to control the trade-off between trajectory acceptability and consumption reduction.

No description available.

Unmanned aerial vehicles (UAVs) are emerging as enablers for supporting many applications and services, such as precision agriculture, search and rescue, temporary network deployment, coverage extension, and security. UAVs are being considered for integration into emerging wireless networks as aerial users, aerial relays (ARs), or aerial base stations (ABSs). This dissertation proposes employing UAVs to contribute to physical layer techniques that enhance the security performance of advanced wireless networks and services in terms of availability, resilience, and confidentiality. The focus is on securing terrestrial cellular communications against eavesdropping with a cellular-connected UAV that is dispatched as an AR or ABS. The research develops mathematical tools and applies machine learning algorithms to jointly optimize UAV trajectory and advanced communication parameters for improving the secrecy rate of wireless links, covering various communication scenarios: static and mobile users, single and multiple users, and single and multiple eavesdroppers with and without knowledge of the location of attackers and their channel state information. The analysis is based on established air-to-ground and air-to-air channel models for single and multiple antenna systems while taking into consideration the limited on-board energy resources of cellular-connected UAVs. Simulation results show fast algorithm convergence and significant improvements in terms of channel secrecy capacity that can be achieved when UAVs assist terrestrial cellular networks as proposed here over state-of-the-art solutions. In addition, numerical results demonstrate that the proposed methods scale well with the number of users to be served and with different eavesdropping distributions. The presented solutions are wireless protocol agnostic, can complement traditional security principles, and can be extended to address other communication security and performance needs.

No description available.

[pt] Na última década, os robôs móveis com pernas ganharam notoriedade por sua capacidade de se movimentar com segurança em terrenos acidentados e superar obstáculos, como declives e escadas, podendo ser utilizados em mais aplicações em comparação com os robôs móveis com rodas. Novos desenvolvimentos que melhorem a robustez do planejamento de trajetória e o controle dinâmico de robôs com pernas são cruciais para o avanço desse campo. O objetivo deste trabalho é desenvolver um framework baseado em C++ e ROS Noetic que integre otimização de trajetória off-line para robôs com pernas com Model Predictive Control (MPC) on-line, considerando o mapa de elevação do terreno. A otimização de trajetória é baseada na biblioteca de código aberto TOWR (Trajectory Optimization for Walking Robots), que emprega uma função contínua para representar o mapa do terreno. Para tornála mais genérica, foi implementada uma interface que permite que mapas de elevação 2,5D sejam usados como representação do terreno. Além disso, as trajetórias geradas pelo TOWR são fornecidas como referências para um controlador MPC baseado na biblioteca de código aberto OCS2. As trajetórias otimizadas pelo MPC são então rastreadas por um Whole-Body Controller (WBC), que calcula os torques de atuação das juntas do robô. A estrutura é validada em simulações usando a dinâmica completa do robô, com diferentes tipos de terreno e sob perturbação externa. / [en] In the last decade, legged mobile robots have gained notoriety for their ability to move safely over rough terrain and overcome obstacles such as slopes and stairs, opening up new applications compared to wheeled mobile robots. New developments that improve the robustness of trajectory planning and dynamic control of legged robots are crucial for the advancement of this field. The aim of this work is to develop a framework based in C++ and ROS Noetic that integrates offline trajectory optimization for legged robots with online Model Predictive Control (MPC) while taking into account the elevation map of the terrain. The trajectory optimization is based on the open-source library TOWR (Trajectory Optimization for Walking Robots), which employs a continuous function to represent the map of the terrain. To make it more generic, an interface was implemented to allow 2.5D elevation maps to be used as terrain representation. Furthermore, the trajectories generated by TOWR are provided as references for a MPC implemented based on the open-source library OCS2. The trajectories optimized by the MPC are then tracked by a weighted Whole-Body Controller (WBC), which computes the actuation torques for the robot s joints. The framework is validated in simulations using the full dynamics of the robot, with different terrain types and under external disturbance.

Pour résoudre les problèmes environnementaux et énergétiques liés au nombre croissant de véhicules en circulation, deux approches sont envisageables : l'une est technologique et vise à améliorer les composants du véhicule ou son architecture, l'autre est comportementale et cherche à changer la manière d'utiliser les véhicules. Dans ce contexte, l'éco-conduite représente une méthode, applicable immédiatement, permettant à chaque conducteur de réduire sa consommation. L'objectif de cette thèse est donc l'analyse des gains potentiels de l'éco-conduite pour les différents types de véhicules existant : thermique, électrique et hybride. Ainsi, la première partie de ce travail se focalise sur une étude théorique visant à calculer les gains potentiels et à déterminer les règles d'éco-conduite, avant d'aborder dans un second temps une mise en situation plus réaliste et une intégration des algorithmes dans un système d'assistance pour le conducteur. En s'appuyant sur une modélisation énergétique des différents types de véhicules, la détermination et la comparaison du fonctionnement optimal se base sur l'optimisation du profil de vitesse pour des trajets connus. La programmation dynamique a été mise enoeuvre pour calculer la trajectoire optimale énergétique en tenant compte de la contrainte temporelle afin de ne pas pénaliser l'intérêt d'une conduite économe. Evidemment, l'intégration de l'éco-conduite doit, d'une part, tenir compte du trafic à proximité du véhicule et d'autre part, ne pas aboutir à une augmentation des émissions de polluants. Ainsi, en nous appuyant sur des modèles de suivi de véhicules (trafic), nous avons montré que les principes d'éco-conduite restent valables et conduisent de toute façon à des gains énergétiques. Concernant les contraintes d'émissions, des résultats expérimentaux nous ont conduit à adapter nos algorithmes pour répondre simultanément aux aspects écologiques et économiques. Enfin, les connaissances acquises ont été appliquées à la conception d'un système d'assistance testé sur un simulateur de conduite. / The transportation sector has been identified as one of many sources of today's energetic and environmental problems. With constantly increasing numbers of vehicles on the road, non-renewable fossil fuels are becoming scarce and expensive. In addition, due to the pollutant emissions of internal combustion engines, the transportation sector is a major producer of greenhouse gas emissions. To resolve these problems researcher are looking for technological solutions, such as more efficient components and alternative drive train technologies, on one hand. On the other hand, work is being done to ensure the most efficient utilization of available technological resources. Eco driving is one way to immediately reduce a driver's energy consumption. In this thesis the potential gains of eco driving for passenger vehicles will be discussed. The main objective of this work is to, first, identify and compare drive train specific, optimal vehicle operation. Secondly, the effect of real-life constraints on potential gains of eco driving is evaluated. In addition, an approach to integrate mathematical optimization algorithms in an advanced driver assist system for eco driving is proposed. Physical vehicle models are developed for three representative vehicles: the conventional, electric and power-split hybrid vehicle. Using real-life and standard drive cycles a baseline mission is defined by specifying trip and road constraint. Applying the dynamic programming algorithms the trajectory optimization problem is solved, minimizing energy consumption for the trip. The effect of traffic on potential gains of eco driving is discussed, considering a vehicle following situation. Integrating emission constraints in the optimization algorithm the environmental advantages of eco driving are discussed. Finally, the developed algorithms were integrated in a driver assist system. Experimental tests on a driving simulator were used to verify the effectiveness of the system, as well as driver acceptance.

Preliminary interplanetary trajectory designs frequently use simplified two-body orbital mechanics and linked conics methodology to model the complex trajectories in multi-body systems. Incorporating gravity assists provides highly efficient interplanetary trajectories, enabling otherwise infeasible spacecraft missions. Future missions may employ powered gravity assists, using a propulsive maneuver during the flyby, improving the overall trajectory performance. This dissertation provides a complete description and analysis of a new interplanetary trajectory design tool known as TRACT (TRAjectory Configuration Tool). TRACT is capable of modeling complex interplanetary trajectories, including multiple ballistic and/or powered gravity assists, deep space maneuvers, parking orbits, and other common maneuvers. TRACT utilizes an adaptable architecture of modular boundary value problem (BVP) algorithms for all trajectory segments. A bi-level optimization scheme is employed to reduce the number of optimization variables, simplifying the user provided trajectory information. The standardized optimization parameter set allows for easy use of TRACT with a variety of optimization algorithms and mission constraints. The dissertation also details new research in powered gravity assists. A review of literature on optimal powered gravity assists is presented, where many optimal solutions found are infeasible for realistic spacecraft missions. The need was identified for a mission feasible optimal powered gravity assist algorithm using only a single impulsive maneuver. The solution space was analyzed and a complete characterization was developed for solution types of the optimal single-impulse powered gravity assist. Using newfound solution space characteristics, an efficient and reliable optimal single-impulse powered gravity assist BVP algorithm was formulated. The mission constraints were strictly enforced, such as maintaining the closest approach above a minimum radius and below a maximum radius. An extension of the optimal powered gravity assist research is the development of a gravity assist BVP algorithm that utilizes an asymptote ΔV correction maneuver to produce ballistic gravity assist trajectory solutions. The efficient algorithm is tested with real interplanetary mission trajectory parameters and successfully converges upon ballistic gravity assists with improved performance compared to traditional methods. A hybrid approach is also presented, using the asymptote maneuver algorithm together with traditional gravity assist constraints to reach ballistic trajectory solutions more reliably, while improving computational performance.

The objective of this study was to design a launch vehicle capable of deploying a nanosatellite into a Sun-synchronous orbit at 500 km orbital altitude from the JAS 39E/F Gripen fighter aircraft. This was achieved by first performing theoretical calculations for the required nozzles and solid propellant grain configurations for the first two solid stages, followed by the necessary liquid propellant configuration for the third stage. Lastly, two methods were investigated in solving the trajectory ascent problem for the launch vehicle design. First, by stating the trajectory problem as an initial value problem while guessing a Sigmoidal steering law. Secondly, by stating the trajectory problem as a boundary value problem. The latter was solved by transcribing the trajectory problem into a nonlinear program where a parametric steering law was derived using a Sequential quadratic programming algorithm.Ultimately, resulting in a launch vehicle design with a gross lift-off mass of 1,289 kg, capable of launching an 8.4 kg payload into the targeted orbit, with suggested modifications to increase the possible payload mass to 12.9 kg. / Målet med den här studien var att designa en luftlanserad trestegsraket kapabel till att transportera en nanosatellit upp till en solsynkron omloppsbana på 500 km altitud från ett JAS 39E/F Gripen jaktflygplan. Det gjordes genom att först beräkna de nödvändiga dysorna och krutladdningsformerna för de två första stegen tillsammans med en flytande bränsledesign för det tredje steget. Två metoder undersöktes för bananalysen. Först genom att anta en Sigmoidal styrningsfunktion för pitchen, sedan genom att transkribera problemet till ett icke-linjärt program där en parametrisk styrlag togs fram genom att använda en Sequential quadratic programming algoritm. Slutligen presenterades en raketdesign med en total vikt på 1 289 kg, kapabel till att skjuta upp en nyttolast på 8,4 kg till den önskade omloppsbanan tillsammans med förslag som kan öka den möjliga nyttolasten till 12,9 kg.