This thesis project is part of an internship at Visual Behavior. The company aims at producing computer vision models for robotics, helping the machine to better understand the world through the camera eye. The image holds many features that deep learning models are able to extract: navigable area, depth inference and object detection. Example of recent advances are the RAFTstereo model [1] to infer or refine depth features from stereo images, or the end-to-end Object detection model DETR [2]. The field of autonomous navigation can then benefit from these advanced features to propose better path planning methods. In particular, to help the deployment of ground robots in human crowded environments, the robots behavior must not only be safe but it must also look smart so as to inspire trust. This thesis proposes a local path planner based on the Dynamic Window Approach [3] using a scoring function inspired from the Velocity Obstacle method [4] so as to benefit from the flexibility of the first and the long-term anticipation of the second. The proposed method can induce a smart behavior by setting the robot on safe tracks from a long time horizon without increasing the time to reach a positional goal, compared to a closer-ranged strategy inspired from the DW4DO method [5]. This improves the robot’s ability to deal with several moving obstacles and to avoid engaging in already occupied corridors. The code produced in this thesis uses ROS and the gazebo simulator and is available in the following git page https://github.com/FloCoic oi/fc_thesis along with the minimal instructions to run the install and get started to quickly run a demo. / Detta examensarbete är en del av en praktik på Visual Behavior. Företaget har som mål att ta fram modeller för datorseende för robotar som hjälper maskinen att bättre förstå världen genom kamerans öga. Bilden innehåller många egenskaper som modeller för djupinlärning kan extrahera: navigerbart område, djupinferens och objektsdetektering. Exempel på nya framsteg är RAFT-stereo-modellen [1] för att härleda eller förfina djupegenskaper från stereobilder, eller ”end-to-end” objektdetektering modellen DETR [2]. Inom området autonom navigering kan man sedan dra nytta av dessa avancerade funktioner för att föreslå bättre metoder för vägplanering. För att underlätta användningen av markrobotar i miljöer med mycket människor måste robotarnas beteende inte bara vara säkert utan också se smart ut så att de väcker förtroende. I den här avhandlingen föreslås en lokal vägplanerare som bygger på Dynamic Window Approach [3] och som använder en poängfunktion inspirerad av Velocity Obstacle metoden [4] för att dra nytta av flexibiliteten hos den första metoden och den långsiktiga förutsebarheten hos den andra. Den föreslagna metoden kan framkalla ett smart beteende genom att sätta roboten på säkra spår på lång sikt utan att öka tiden för att nå ett positionsmål, jämfört med en strategi med närmare avstånd som inspirerats av DW4DOmetoden [5]. Detta förbättrar robotens förmåga att hantera flera rörliga hinder och att undvika att gå in i redan upptagna korridorer. Koden som produceras i denna avhandling använder ROS och gazebosimulatorn och finns tillgänglig på följande git-sida https://github.c om/FloCoicoi/fc_thesis tillsammans med minimala instruktioner för att köra installationen och komma igång för att snabbt köra en demo.

Robotics has been the subject of academic study from as early as 1948. For much of this time, study has focused on very specific applications in very well controlled environments. For example, the first commercial robots (1961) were introduced in order to improve the efficiency of production lines. The tasks undertaken by these robots were simple, and all that was required of a control algorithm was speed, repetitiveness and reliability in these environments. Now however, robots are being used to move around autonomously in increasingly unpredictable environments, and the need for robotic control algorithms that can successfully react to such conditions is ever increasing. In addition to this there is an ever-increasing array of robots available, the control algorithms for which are often incompatible. This can result in extensive redesign and large sections of code being re-written for use on different architectures. The thesis presented here is that a new generic approach can be created that provides robust high quality smooth paths and time-optimal path tracking to substantially increase applicability and efficiency of autonomous motion plans. The control system developed to support this thesis is capable of producing high quality smooth paths, and following these paths to a high level of accuracy in a robust and near time-optimal manner. The system can control a variety of robots in environments that contain 2D obstacles of various shapes and sizes. The system is also resilient to sensor error, spatial drift, and wheel-slip. In achieving the above, this system provides previously unavailable functionality by generically creating and tracking high quality paths so that only minor and clear adjustments are required between different robots and also be being capable of operating in environments that contain high levels of perturbation. The system is comprised of five separate novel component algorithms in order to cater for five different motion challenges facing modern robots. Each algorithm provides guaranteed functionality that has previously been unavailable in respect to its challenges. The challenges are: high quality smooth movement to reach n-dimensional goals in regions without obstacles, the navigation of 2D obstacles with guaranteed completeness, high quality smooth movement for ground robots carrying out 2D obstacle navigation, near time-optimal path tracking, and finally, effective wheel-slip detection and compensation. In meeting these challenges the algorithms have tackled adherence to non-holonomic constraints, applicability to a wide range of robots and tasks, fast real-time creation of paths and controls, sensor error compensation, and compensation for perturbation. This thesis presents each of the above algorithms individually. It is shown that existing methods are unable to produce the results provided by this thesis, before detailing the operation of each algorithm. The methodology employed is varied in accordance with each of the five core challenges. However, a common element of methodology throughout the thesis is that of gradient descent within a new type of potential field, which is dynamic and capable of the simultaneous creation of high-quality paths and the controls required to execute them. By relating global to local considerations through subgoals, this methodology (combined with other elements) is shown to be fully capable of achieving the aims of the thesis. It is concluded that the produced system represents a novel and significant contribution as there is no other system (to the author’s knowledge) that provides all of the functionality given. For each component algorithm there are many control systems that provide one or more of its features, but none that are capable of all of the features. Applications for this work are wide ranging as it is comprised of five component algorithms each applicable in their own right. For example, high quality smooth paths may be created and followed in any dimensionality of space if time optimality and obstacle avoidance are not required. Broadly speaking, and in summary, applications are to ground-based robotics in the areas of smooth path planning, time optimal travel, and compensation for unpredictable perturbation.

Cette thèse se situe dans la planification et le suivi de mouvement d’un humanoïde mobile à deux bras. Premièrement, MDH est utilisé pour la modélisation cinématique. Afin de surmonter les insuffisances de la méthode d’Euler-Lagrange qui nécessitent des calculs d’énergie et ses dérivées partielles, la méthode de Kane est utilisée. En plus, la stabilité physique est analysée et un contrôleur est conçu. Deuxièmement, un algorithme avancée MaxiMin NSGA-II est proposée pour concevoir l’orientation et la position optimales de la plate-forme mobile (PB) et la configuration optimale du manipulateur supérieur (MS) étant donnée uniquement la pose initiale et les positions et orientations souhaitées des EEs. Un algorithme à connexion directe combinant BiRRT et la gradient-descente est conçu pour réaliser la transition de la pose initiale à la pose optimale, et une méthode d'optimisation géométrique est conçue pour optimiser et cohérer le chemin. En outre, les motions en avant sont obtenues en attribuant des orientations pour MB indiquant ainsi l'intention du robot. Afin de résoudre le problème d'échec de l’algorithme hors ligne, un algorithme en ligne est proposé en estimant les motions des obstacles dynamiques. De plus, afin d'optimiser les via-poses, un algorithme basé sur les via-points des EEs et MOGA est proposé en optimisant quatre fonctions objectives. Enfin, le problème de suivi de motion est étudié étant donné les motions des EEs dans l'espace de tâche. Au lieu de contrôler la motion absolue, deux motions relatives sont introduites pour réaliser la coordination et la coopération entre MB et MS. De plus, une technique mWLN est proposée pour éviter les limites des joints. / This thesis focuses on the motion planning and tracking of a dual-arm mobile humanoid. First, MDH is used for kinematic modeling. The co-simulation via Simulink-Adams on prototype is realized to validate the effectiveness of RBFNN controller. In order to overcome the shortcomings of Euler-Lagrange’s formulations that require calculating energy and energy derivatives, Kane’s method is used. In addition, physical stability is analyzed based on Kane’s method and a controller is designed using back-stepping technique. Secondly, an improved MaxiMin NSGA-II is proposed to design the mobile base’s (MB) optimal position-orientation and the upper manipulator’s (UM) optimal configuration given only the initial pose and end-effectors’ (EEs) desired positions-orientations. A direct connect algorithm combining BiRRT and gradient-descent is designed to plan the transition from initial pose to optimal pose, and a geometric optimization method is designed to optimize and cohere the path. In addition, forward motions are obtained by assigning orientations for MB thus indicating robot’s intention. In order to solve the failure problem of offline algorithm, an online algorithm is proposed while estimating dynamic obstacles’ motions. In addition, in order to optimize via-poses, an algorithm based on EEs’ via-points and MOGA is proposed by optimizing four via-pose-based objective functions. Finally, the motion tracking problem is studied given EEs’ motions in the task space. Instead of controlling the absolute motion, two relative motions are introduced to realize the coordination and cooperation between MB and UM. In addition, an modulated WLN technique is proposed to avoid joints’ limits.

My work is aiming at establishing the bases of a semiotics of motion, in order to facilitate the programing of complex robotics systems. The objective is to build a symbolic model of the action, based on the analysis of the numerical functions that drive the motion (control and planning). The methodology comes from the well-known robotics concepts: motion-planning algorithms, control of redundant systems and task-function approach. The originality of the work is to consider the "task" as the unifying concept both to describe the motion and to control its execution. The document is organized in two parts. In the first part, the task-function control framework is extended to cover all the possible modalities of the robot. The objective is to absorb from the lowest-possible functional level the maximum of uncertainty factors. It is then not any more necessary to model these factors at the higher functional levels. This sensorimotor layer is then used as a basic action "vocabulary" that enables the system to be controlled with a higher-level interface. In the second part, this action vocabulary is used to provide a dedicated robotics programing language, to build motion-planning methods and to describe an observed movement. The proposed methods are generic and can be applied to a various systems, from robotics (redundant robots) to computer animation (virtual avatars). Nonetheless, the work is more specifically dedicated to humanoid robotics. Without forgetting other possible outlets, humanoid robotics provides a tangible applicative and experimental framework. It also leads toward the natural human motion, as presented in the end of the document.

Η παρούσα διδακτορική διατριβή έχει ως κύριο αντικείμενο μελέτης την μοντελοποίηση και έλεγχο ενός μικρορομποτικού βραχίονα αναλυόμενου σε σφαιρικά πεπερασμένα στοιχεία σε περιβάλλον με εξωτερικές δυνάμεις Van Der Waals και συνυπολογίζοντας την τριβή. Τα κύρια σημεία είναι η εισαγωγή των εξωτερικών δυνάμεων στο μοντέλο του μικρορομπότ, η δημιουργία προσαρμοστικού ελέγχου για την επίτευξη ακολουθίας τροχιάς με αναγνώριση και ακύρωση των ισχυρών μεταβαλλόμενων εξωτερικών δυνάμεων, η αναγνώριση της θέσης και η αποφυγή εμποδίων σε άγνωστο περιβάλλον κλίμακας μικρομέτρων και ο καθορισμός τροχιάς για προσέγγιση σημείων στον χώρο εργασίας του μικρορομπότ. Προτείνεται επίσης ένα σύστημα επενέργησης σε διάταξη τένοντα με νανοκαλώδια και γίνεται μελέτη της αντοχής του σε σχέση με τις μέγιστες δυνάμεις-ροπές που παρουσιάζονται κατά τον έλεγχο. Για την αναγνώριση των εξωτερικών δυνάμεων δοκιμάζονται διαφορετικά είδη εκτιμητών και εξετάζεται η απόδοσή τους στο συνολικό σύστημα. / The present PhD thesis has a key object the modeling and control of a micro robotic manipulator, represented by spherical particles in an environment with external Van Der Waals forces and taking friction into account. The main points are a) the insertion of the external forces in the micro robot model, b) the adaptive control used in order to follow a desired trajectory, with identification and cancellation of the external forces, the position identification and avoidance of obstacles in an unstructured micrometer scale environment and the trajectory planning towards a target point in the task space of the microrobot. Also a tendon like actuation system is proposed, using nanowires and its mechanical properties are studied in order to determine the viability of its use in relation to the required torques during the control process. For the external force identification scheme, various types of estimators are proposed and their efficiency in the system is studied.

A Robótica tem iniciado uma transição de Robótica Industrial para Robótica de Serviço, movendo-se em direção as necessidades diárias dos seres humanos. Para realizar essa transição, robôs necessitam de mais autonomia para executar tarefas em espaços dinâmicos ocupados por humanos, diferente dos ambientes controlados das fábricas. Nesta tese, é investigado um problema no qual um time de robôs completamente autônomos deve visitar certos locais em um ambiente interno usado por humanos a fim de executar algum tipo de tarefa. Este problema está relacionado a três importantes questões da Robótica e Inteligência Artificial (IA), que são: coordenação, localização e navegação. Para coordenar as visitas nos locais desejados, um escalonamento deve ser realizado para encontrar as rotas para os robôs. Tal escalonamento deve minimizar a distância total viajada pelo time e também balancear as rotas. Este problema pode ser modelado como sendo uma instância do Problema dos Múltiplos Caixeiros Viajantes (PMCV). Como este problema é classificado como NP-Difícil, é proposto o uso de algoritmos aproximados para encontrar soluções satisfatórias para o problema. Uma vez que as rotas estão computadas, os robôs necessitam de se localizar no ambiente para que eles tenham certeza de que estão visitando os lugares corretos. Muitas técnicas de localização não são muito precisas em ambientes internos devido a diferentes tipos de ruídos. Desta forma, é proposto uma combinação de duas delas. Nesta abordagem, um algoritmo de localização WiFi rastreia a localização global do robô, enquanto um algoritmo de localização Kinect estima sua posição atual dentro da área delimitada pela localização global. Depois de visitar um dado local de sua rota, o robô deve navegar em direção ao próximo. A navegação em ambientes internos ocupados por humanos é uma tarefa difícil, uma vez que muitos objetos móveis e dinâmicos podem ser encontrados no caminho. Para isso, o robô deve possuir controles reativos para evitar colidir com objetos dinâmicos, como pessoas, enquanto ele navega. Além disso, objetos móveis, como mobílias, são passíveis de serem movidos frequentemente, o que muda o mapa utilizado para planejar o caminho do robô. Para resolver estes problemas, é proposto um algoritmo de desvio de obstáculos e um planejador dinâmico de caminho para ambientes internos ocupados por humanos. Desta forma, esta tese contribui com uma série de algoritmos para os problemas de coordenação, localização e navegação. São introduzidos: Algoritmos Genéticos (AGs) multi-objetivo para resolver o Problema dos Múltiplos Caixeiros Viajantes, abordagens de localização que utilizam a técnica de Filtro de Partículas (FP) com dispositivos Kinect e WiFi, um Sistema Híbrido Inteligente (SHI) baseado em Lógica Fuzzy (LF) e Redes Neuronais Artificiais (RNA) para desvio de obstáculos e uma adaptação do algoritmo D*Lite que permite o robô replanejar caminhos de forma eficiente e requisitar auxílio humano se necessário. Todos os algoritmos são avaliados em robôs reais e simuladores, demonstrando seus desempenhos em resolver os problemas abordados nesta tese. / Robotics has started the transition from industrial into service robotics, moving closer towards humans daily needs. To accomplish this transition, robots require more autonomy to perform tasks in dynamic spaces occupied by humans, different from well controlled environments of factory floors. In this thesis, we investigate a problem in which a team of completely autonomous robots needs to visit certain locations in an indoor human environment in order to perform some kind of task. This problem is related to three important issues of Robotics and \ac{AI}, namely: coordination, localization and navigation. To coordinate the visits in the desired locations, a scheduling must be performed to find routes for the robots. Such scheduling needs to minimize the total distance traveled by the team and also to balance the routes. We model this problem as being an instance of the multiple Traveling Salesmen Problem (mTSP). Since it is classified as NP-Hard, we propose the use of approximation algorithms to find reasonable solutions to the problem. Once the routes are computed, the robots need to localize themselves in the environment so they can be sure that they are visiting the right places. Many localization techniques are not very accurate in indoor human environments due to different types of noise. Therefore, we propose the combination of two of them. In such approach, a WiFi localization algorithm tracks the global location of the robot while a Kinect localization algorithm estimates its current pose on that area. After visiting a given location of its route, the robot must navigate towards the next one. Navigation in indoor human environments is a challenging task as many moving and movable objects can be found in the way. The robot should be equipped with a reactive controller to avoid colliding with moving objects, like people, while it is navigating. Also, movable objects, such as furniture, are likely to be moved frequently, which changes the map used to plan the robot's path. To tackle these problems, we introduce an obstacle avoidance algorithm and a dynamic path planner for navigation in indoor human environments. We contribute a series of algorithms for the problems of coordination, localization, and navigation. We introduce: multi-objective Genetic Algorithms (GAs) to solve the mTSP, localization approaches that use Particle Filters (PFs) with Kinect and WiFi devices, a Hybrid Intelligent System (HIS) based on Fuzzy Logic (FL) and Artificial Neural Network (ANN) for obstacle avoidance, and an adaptation to the D*Lite algorithm that enables robots to replan paths efficiently and also ask for human assistance if it is necessary. All algorithms are evaluated on real robots and simulators, demonstrating their performances to solve the problems addressed in this thesis. / Tese (Doutorado)

La complexité inhérente à la coordination des mouvements d'un groupe de robots mobiles est traitée en investiguant plus avant les potentialités des architectures de contrôle comportementales dont le but est de briser la complexité des tâches à exécuter. En effet, les robots mobiles peuvent évoluer dans des environnements très complexes et nécessite de surcroît une coopération précise et sécurisée des véhicules pouvant rapidement devenir inextricable. Ainsi, pour maîtriser cette complexité, le contrôleur dédié à la réalisation de la tâche est décomposé en un ensemble de comportements/contrôleurs élémentaires (évitement d'obstacles et de collision entre les robots, attraction vers une cible, etc.) qui lient les informations capteurs (provenant de caméras, des capteurs locaux du robot, etc.) aux actionneurs des différentes entités robotiques. La tâche considérée est la navigation en formation en présence d'obstacles (statiques et dynamiques). La spécificité de l'approche théorique consiste à allier les avantages des architectures de contrôle comportementales à la méthode de la structure virtuelle où le groupe de robots mobiles suit un corps virtuel avec une dynamique (vitesse, direction) donnée. Ainsi, l'activation d'un comportement élémentaire en faveur d'un autre se fait en respectant les contraintes structurelles des robots (e.g. vitesses et accélérations maximales, etc.) en vue d'assurer le maximum de précision et de sécurité des mouvements coordonnés entre les différentes entités mobiles. La coopération consiste à se partager les places dans la structure virtuelle de manière distribuée et de façon à atteindre plus rapidement la formation désirée. Pour garantir les critères de performances visés par l'architecture de contrôle, les systèmes hybrides qui permettent de commander des systèmes continus en présence d'évènements discrets sont exploités. En effet, ces contrôleurs (partie discrète) permettent de coordonner l'activité des différents comportements (partie continue) disponibles au niveau de l'architecture, tout en offrant une analyse automaticienne rigoureuse de la stabilité de celle-ci au sens de Lyapunov. Chaque contribution est illustrée par des résultats de simulation. Le dernier chapitre est dédié à l'implémentation de l'architecture de contrôle proposée sur un groupe de robots mobiles Khepera III. / Inherent difficulty of coordinating a group of mobile robots is treated by investigating behavior-based architectures which aim to break task complexity. In fact, multi-robot navigation may become rapidly inextricable, specifically if it is made in hazardous and dynamical environment. The considered task is the navigation in formation in presence of (static and dynamic) obstacles. To overcome its complexity, it is proposed to divide the overall task into two basic behaviors/controllers (obstacle avoidance, attraction to a dynamical target). Applied control is chosen among these controllers according to sensors information (camera, local sensors, etc.). Theoretic approach combines behavior-based and the virtual structure strategy which considers the formation as a virtual body with a given dynamic (velocity, direction). Thus, activating a controller or another is accomplished while respecting structural robots constraints (e.g. maximal velocities and accelerations). The objective is to insure the highest precision and safety of the coordinated motion between the robots. These ones cooperate by optimizing the way of sharing their places in the formation in order to form it in a faster manner. To guarantee performance criteria of the control architecture, hybrid systems tolerating the control of continuous systems in presence of discrete events are explored. In fact, this control allows coordinating (by discrete part) the different behaviors (continuous part) of the architecture. A complete analysis of this architecture stability is also given thanks to Lyapunov-based theory. Every contribution is illustrated through simulation results. The last chapter is devoted to the implementation of the proposed control architecture on a group of Khepera III robots.

La génération de chemins lisses pour les voitures intelligentes est l’une des conditions les plus importantes pour faire accepter et faciliter la navigation autonome de ces véhicules. Cette thèse propose plusieurs méthodes de génération de chemins lisses pour les véhicules non-holonomes qui permet une continuité intrinsèque de la courbure de navigation et offre par ailleurs une flexibilité accrue pour diverses conditions aux limites. Le chemin de courbure continue est construit en composant plusieurs clothoids, comprenant notamment des segments de lignes et/ou d’arcs, et où chaque clothoid est obtenue par une régulation appropriée de ses paramètres. À partir de ces propriétés, le chemin obtenu est nommé pCCP (parametric Continuous Curvature Path). Le pCCP fournit un diagramme de courbure qui facilite une commande en orientation du véhicule, ce qui permet d'obtenir une évolution lisse de sa trajectoire. Le problème du pCCP local est défini par des configurations initiales et finales (caractérisées pour chacune par une posture et un angle de braquage). Le problème a été étendu pour être aussi général que possible en incluant plusieurs cas. La génération locale de pCCPs, pour des cibles statiques, est spécifiquement décrite, les problèmes ont été divisés en trois problèmes et chaque problème a été décomposé par la suite en plusieurs sous-classes possibles. Pour avoir une flexibilité importante des pCCPs proposés, des cibles dynamiques ont été considérées, obtenant ainsi le dynamic-pCCP (d-pCCP). Un cadre simple mais efficace pour analyser l'état futur de l'évitement des obstacles est appliqué en configuration 4D (3D avec l’ajout d’un axe temporel) en mettant en exergue deux manoeuvres d’évitement possibles, car les évolutions avant et arrière sont appliquées et validées avec plusieurs exemples. Selon une méthodologie similaire pour atteindre les critères de performance liés à la génération des pCCPs, le h-CCP (pour human-pCCP) est proposé en utilisant des modèles expérimentaux comportementaux d’échantillons de conducteurs humains. À partir de quelques sous-expériences, le modèle de conduite humain pour l’évitement d’obstacles, les changements de voie et les mouvements en virage sont extraits et ces modèles ont été inclus pour créer ainsi le h-CCP (obtenu d’une manière similaire au pCCP mais avec différents critères d’optimisation) qui permet d’améliorer considérablement le confort des passagers. / Smooth path generation for car-like vehicles is one of the most important requisite to facilitate the broadcast use of autonomous navigation. This thesis proposes a smooth path generation method for nonholonomic vehicles which has inherently continuity of curvature and having important flexibility for various boundary conditions. The continuous curvature path is constructed by composing multiple clothoids including lines and/or arc segments, and where each clothoid is obtained by parameter regulation. From those properties the path is named pCCP (parametric Continuous Curvature Path) and provides curvature diagram which facilitates a smooth steering control for path following problem. Local pCCP problem is defined by initial and final tuple configurations (vehicles posture and steering angle). The problem is expanded to be as general as possible by including several cases. The local pCCP generation for steady target pose is specifically described, where the problem is divided into three problems and each problem is also decomposed into several sub-cases. To give more flexibility to the proposed pCCP, dynamic target is considered to obtain dynamic-pCCP (d-CCP). A simple but efficient framework to analyze the future status of obstacle avoidance is applied in 4D (3D with the addition of time axis) configuration and two avoidance maneuvers as front and rear avoidance are applied and validated with several examples. Under the similar methodology in performance criteria of pCCP generation, the human-CCP (h-CCP) is derived from experimental patterns of human driver samples. From several subexperiments, human driving pattern for obstacle avoidance, lane change and cornering motion are extracted and those pattern were included to make the h-CCP (which is obtained with similar way as pCCP but with different optimization criteria) to enhance considerably the passenger comfort.

La complexité associée à la coordination d’un groupe de robots mobiles est traitée dans cette thèse en investiguant plus avant les potentialités des architectures de commande multi-contrôleurs dont le but est de briser la complexité des tâches à exécuter. En effet, les robots mobiles peuvent évoluer dans des environnements très complexes et nécessitent de surcroît une coopération précise et sécurisée pouvant rapidement devenir inextricable. Ainsi, pour maîtriser cette complexité, le contrôleur dédié à la réalisation d’une tâche est décomposé en un ensemble de comportements/contrôleurs élémentaires (évitement d’obstacles et de collision entre les robots, attraction vers une cible, planification, etc.) qui lient les informations capteurs (provenant des capteurs locaux du robot, etc.) aux actionneurs des différentes entités robotiques. La tâche considérée dans cette thèse correspond à la navigation d’un groupe de robots mobiles dans des environnements peu ou pas connus en présence d’obstacles (statiques et dynamiques). La spécificité de l’approche théorique consiste à allier les avantages des architectures multi-contrôleurs à ceux des systèmes multi-agents et spécialement les modèles organisationnels afin d’apporter un haut niveau de coordination entre les agents/robots mobiles. Le groupe de robots mobiles est alors coordonné suivant les différentes normes et spécifications du modèle organisationnel. Ainsi, l’activation d’un comportement élémentaire en faveur d’un autre se fait en respectant les contraintes structurelles des robots en vue d’assurer le maximum de précision et de sécurité des mouvements coordonnés entre les différentes entités mobiles. La coopération se fait à travers un agent superviseur (centralisé) de façon à atteindre plus rapidement la destination désirée, les événements inattendus sont gérés quant à eux individuellement par les agents/robots mobiles de façon distribuée. L’élaboration du simulateur ROBOTOPIA nous a permis d’illustrer chacune des contributions de la thèse par un nombre important de simulations. / The difficulty of coordinating a group of mobile robots is adressed in this thesis by investigating control architectures which aim to break task complexity. In fact, multi-robot navigation may become rapidly inextricable, specifically if it is made in hazardous and dynamical environment requiring precise and secure cooperation. The considered task is the navigation of a group of mobile robots in unknown environments in presence of (static and dynamic) obstacles. To overcome its complexity, it is proposed to divide the overall task into a set of basic behaviors/controllers (obstacle avoidance, attraction to a dynamical target, planning, etc.). Applied control is chosen among these controllers according to sensors information (camera, local sensors, etc.). The specificity of the theoretical approach is to combine the benefits of multi-controller control architectures to those of multi-agent organizational models to provide a high level of coordination between mobile agents-robots systems. The group of mobile robots is then coordinated according to different norms and specifications of the organizational model. Thus, activating a basic behavior in favor of another is done in accordance with the structural constraints of the robots in order to ensure maximum safety and precision of the coordinated movements between robots. Cooperation takes place through a supervisor agent (centralized) to reach the desired destination faster ; unexpected events are individually managed by the mobile agents/robots in a distributed way. To guarantee performance criteria of the control architecture, hybrid systems tolerating the control of continuous systems in presence of discrete events are explored. In fact, this control allows coordinating (by discrete part) the different behaviors (continuous part) of the architecture. The development of ROBOTOPIA simulator allowed us to illustrate each contribution by many results of simulations.

Plusieurs laboratoires de robotique à travers le monde travaillent sur le développement de stratégies innovantes pour la navigation autonome de véhicules élémentaires ou en convoi. Dans ce contexte, nos travaux de thèse s’inscrivent principalement dans le cadre de la navigation en formation d’un groupe de véhicules dans des environnements structurés. La complexité de ces systèmes multi-robots ne permet pas l’utilisation directe de techniques classiques de perception et/ou de contrôle/commande. Nos travaux ont consisté à décomposer le contrôle global, dédié à la réalisation de la tâche complexe, en un ensemble de comportements/contrôleurs élémentaires précis et fiables (e.g., évitement d’obstacles, suivi de trajectoire, attraction vers une cible, navigation en formation, etc.). Ces comportements lient les différentes informations fournies par les capteurs aux actions des véhicules. Pour garantir les critères de performances imposés à notre architecture de contrôle/commande (e.g., stabilité, robustesse et/ou borner les erreurs maximales), les potentialités des systèmes hybrides ont été considérées. Cette architecture de contrôle a été validée, dans un premier temps, sur des véhicules pris individuellement, en utilisant notamment une stratégie de navigation sûre et flexible utilisant des points de passage. Cette navigation permet au véhicule d’effectuer différentes manœuvres entre ces points de passage (pour éviter par exemple des obstacles dans l’environnement) et ce sans avoir à planifier/re-planifier des trajectoires globales dans l’environnement. Une loi de commande spécifique, permettant une attraction stable (au sens de Lyapunov) et précise vers des cibles statiques ou dynamiques a été par ailleurs développée. Cette loi de commande garantit la convergence du véhicule vers chaque point de passage tout en garantissant des trajectoires sûres. Par ailleurs, un algorithme nommé OMWS (pour Optimal Multi-criteria Waypoint Selection) a été proposé pour sélectionner les configurations optimales des points de passage dans l’environnement. Cet algorithme permet de garantir des mouvements sûrs et fiables du véhicules en tenant compte des contraintes et incertitudes liées à la navigation du véhicule. Par la suite, l’architecture de contrôle/commande proposée a été étendue aux systèmes multi-robots en utilisant la combinaison d’une approche leader-suiveur et comportementale. Un important aspect de la navigation multi-robots est la reconfiguration dynamique de la formation en fonction du contexte de la navigation (e.g., passer d’une configuration triangle vers ligne si la largeur de la voie de navigation ne suffisait pas). Ainsi, des stratégies de reconfiguration dynamique ont été proposées, permettant de garantir la sureté de la formation même au moment des transitions entre configurations. Il est à noter par ailleurs que des métriques spécifiques ont été proposées pour quantifier la fiabilité et la robustesse des stratégies multi-robots proposées. Plusieurs simulations et expérimentations avec des véhicules urbains (VIPALABs) nous ont permis de confirmer la viabilité et efficacité des architectures de contrôle/commande proposées pour la navigation en formation d’un groupe de VIPALABs. / Beyond the interest of robotics laboratories for the development of dedicated strategies for single vehicle navigation, several laboratories around the world are more and more involved in the general challenging field of cooperative multi-robot navigation. In this context, this work deals with the navigation in formation of a group of Unmanned Ground Vehicles (UGVs) dedicated to structured environments. The complexity of this Multi-Robot System (MRS) does not permit the direct use of neither classical perception nor control techniques. To overcome this problem, this work proposes to break up the overall control dedicated to the achievement of the complex task into a group of accurate and reliable elementary behaviors/controllers (e.g., obstacles avoidance, trajectory tracking, target reaching, navigation in formation, formation reconfiguration, etc.). These behaviors are linked to different information given by the sensors to the actions of vehicles. To guarantee the performances criteria (e.g., stability, convergence, state errors) aimed by the control architecture, the potentialities of hybrid controllers (which controlling continuous systems in the presence of discrete events) are considered. This control architecture is validated for a single vehicle to perform safe and flexible autonomous navigation using an appropriate strategy of navigation through suitable set of waypoints. This flexible navigation allows different vehicle maneuvers between waypoints (e.g., target reaching or obstacle avoidance) without using any trajectory planning nor replanning. The designed control law based on Lyapunov synthesis guarantees the convergence to assigned waypoint while performing safe trajectories. Furthermore, an algorithm to select suitable waypoints’ positions, named Optimal Multi-criteria Waypoint Selection (OMWS), in structured environments while taking into account the safe and reliable vehicle movements, and vehicle constraints and uncertainties is proposed. Subsequently, the control architecture is extended to Multi-Robot Formation (MRF) using a combination of Leader-Follower and behavior-based approaches. An important cooperative MRS issues in this thesis is the dynamic reconfiguration of the formation according to the context of navigation (e.g., to pass from a triangle configuration towards a line if the width of the navigation way is not sufficient). The proposed Strategy for Formation Reconfiguration (SFR) guarantees the stability and the safety of the MRS at the time of the transitions between configuration (e.g., line towards square, triangle towards line, etc.). Therefore, a safe, reactive and dynamic MRF is obtained. Moreover, the degrees of multi-robot safety, stability and reliability of the system are quantified via suitable metrics. Simulations and experiments using urban vehicles (VIPALABs) of the Institut Pascal laboratory allow to perform exhaustive experiments of the proposed control architecture for the navigation in formation of a group of UGVs.