Ces travaux de recherche portent sur la commande et la planification de trajectoires pour la navigation de véhicules autonomes. Ils se situent dans le cadre d'un projet très ambitieux lancé par le laboratoire Heudiasyc sur la conduite autonome à grande vitesses (vitesse longitudinale supérieure à 5m/s ~= 18 km/h). Pour proposer des solutions à cette problématique, après avoir réalisé une large recherche bibliographique sur la commande et la planification des trajectoires des véhicules autonomes, plusieurs contributions ont été présentées. En ce qui concerne la commande des véhicules autonomes, un contrôleur latéral par mode glissant d'ordre supérieur a été proposé. Compte tenu de la ressemblance implicite entre le mode glissant et le principe d'immersion et d'invariance (I&I), deux contrôleurs utilisant le principe d'immersion et d'invariance ont été proposés par la suite pour améliorer les performances par rapport au mode glissant. Le développement de ces nouveaux contrôleurs nous a permis de garantir une stabilité robuste pour tous les gains positifs des contrôleurs I&I. Ce résultat nous a conduit à étudier les propriétés intrinsèques du système. Une étude des propriétés de passivité du système a révélé des caractéristiques de passivité intéressantes. Par la suite, nous avons développé un contrôleur robuste basé sur la passivité. Concernant la navigation, nous avons développé deux algorithmes de navigation basés sur la méthode des tentacules. Ceci dans le but d'améliorer la méthode de base. Les résultats de la simulation montrent que les algorithmes donnent de bons résultats vis-à-vis des objectifs attendus d'évitement d'obstacles et de suivi de la trajectoire globale de référence. Les algorithmes de commande et de planification de trajectoires développés ont été validés en simulation hors-ligne avec des données réelles après avoir été testés sur un simulateur réaliste. / My research focuses on trajectory planning and control of autonomous vehicles. This work is a part of an extremely ambitious project launched by the Heudiasyc laboratory about autonomous driving at high speed (longitudinal speed greater to 5m/s ~= 18 km/h). With regard to the control of autonomous vehicles at high speed, a lateral controler using higher-order sliding mode control is proposed. Given the implicit similarity between the sliding mode and the principle of immersion and invariance, two controllers using the principle of immersion and invariance have been subsequently proposed in order to improve the performance with respect to the sliding mode. The development of these new controllers shows very strong robust stability which leads us to study the intrinsic properties of the system. A study of the passivity properties of the system is also crried out, showing some interesting characteristics of the system. Hence, a robust passivity-based controller has been developed. Regarding the navigation, we have developed two navigation algorithms based on the tentacles method. Subsequently, a feasibility study of trajectory generation strategies for high speed driving is conducted. The outcome of the simulation proved that the algorithms gave out good results with respect to the expected ogjectives of obstacle avoidance and global reference path following. Control and motion planning algorithms developed were validated offline by simulation with real data. They have been also tested on a realistic simulator.

Na área de robótica móvel autônoma é importante que o robô siga uma trajetória livre de obstáculos. Estes podem ser desde obstáculos estáticos, como paredes e cadeiras em um ambiente interno, ou mesmo obstáculos móveis, como pessoas caminhando na calçada e carros passando pela rua, quando consideramos ambientes externos. No caso de um ambiente estático, o problema pode ser resolvido planejando uma trajetória livre de colisões, sendo que não é necessário um replanejamento se todos os obstáculos estáticos foram considerados. Para ambientes onde os obstáculos estão em constante movimento, é necessário um constante replanejamento da trajetória para que se evite colisões. Alternativamente, pode ser verificada a possibilidade de se manter na rota planejada, alterando apenas a velocidade de cruzeiro do robô para que este desvie dos obstáculos móveis. Este trabalho propõe uma metodologia para calcular uma velocidade de cruzeiro para o robô de forma a minimizar a probabilidade de colisão com os obstáculos detectados pelos seus sensores. A escolha da variação de velocidade para o robô considera a sua velocidade atual, e as velocidades estimadas para os obstáculos. A metodologia para resolução deste problema é apresentada considerando incertezas na posição do robô e obstáculos. São apresentados resultados de simulação que exemplificam a aplicação da metodologia. / Following a free path is an important issue in the area of autonomous mobile robotics. The obstacles can be anything from walls and chairs in an indoor environment, or they can also be people walking on the sidewalk and cars moving through the street. In the case of a static environment, the problem can be solved by planning a path free from collisions, thus it is not essential another path planning as all static obstacles were considered. For an environment were the obstacles are constantly moving, it is necessary an unceasing path replanning to avoid possible collisions. Alternatively, keeping the robot on the previously calculated path can be verified modifying the robot\'s traffic velocity to avoid moving obstacles. Our proposal is to calculate a velocity for the robot which minimizes its collision probability with moving obstacles detected by its sensors. Varying the robot\'s velocity takes into account its current velocity and the estimated velocities of obstacles. The methodology for solving this problem is presented regarding uncertainties in robots and obstacles\' positions. Results from simulations that exemplifies an application for the methodology are presented.

Navegação autônoma é um tópico abrangente cuja atenção por parte da comunidade de robôs móveis vemaumentando ao longo dos anos. O problema consiste em guiar um robô de forma inteligente por um determinado percurso sem ajuda humana. Esta dissertação apresenta um sistema de navegação para ambientes abertos baseado em visão estéreo. Uma câmera estéreo é utilizada na captação de imagens do ambiente e, utilizando o mapa de disparidades gerado por um método estéreo semi-global, dois métodos de detecção de obstáculos são utilizando para segmentar as imagens em regiões navegáveis e não navegáveis. Posteriormente esta classificação é utilizada em conjunto com um método de desvio de obstáculos, resultando em um sistema completo de navegação autônoma. Os resultados obtidos por está dissertação incluem a avaliação de dois métodos estéreo, esta sendo favorável ao método estéreo empregado (semi-global). Foram feitos testes visando avaliar a qualidade e custo computacional de dois métodos para detecção de obstáculos, um baseado em plano e outro baseado em cone. Tais testes deixaram claras as limitações de ambos os métodos e levaram a uma implementação paralela do método baseado em cone. Utilizando uma unidade de processamento gráfico, a versão paralelizada do método baseado em cone atingiu um ganho no tempo computacional de aproximadamente dez vezes. Por fim, os resultados demonstrarão o sistema completo em funcionamento, onde a plataforma robótica utilizada, um veículo elétrico, foi capaz de desviar de pessoas e cones alcançando seu objetivo seguramente / Autonomous navigation is a broad topic that has received increasing attention from the community of mobile robots over the years. The problem is to guide a robot in a smart way for a certain route without human help. This dissertation presents a navigation system for open environments based on stereo vision. A stereo camera is used to capture images of the environment and based on the disparity map generated by a semi-global stereo method, two obstacle detection methods are used to segment the images into navigable and non-navigable regions. Subsequently, this classification is employed in conjunction with a obstacle avoidance method, resulting in a complete autonomous navigation system. The results include an evaluation two stereo methods, this being favorable to the employed stereo method (semi-global). Tests were performed to evaluate the quality and computational cost of two methods for obstacle detection, a plane based one and a cone based. Such tests have left clear the limitations of both methods and led to a parallel implementation of the cone based method. Using a graphics processing unit, a parallel version of the cone based method reached a gain in computational time of approximately ten times. Finally, the results demonstrate the complete system in operation, where the robotic platform used, an electric vehicle, was able to dodge people and cones reaching its goal safely

Na área de robótica móvel autônoma é importante que o robô siga uma trajetória livre de obstáculos. Estes podem ser desde obstáculos estáticos, como paredes e cadeiras em um ambiente interno, ou mesmo obstáculos móveis, como pessoas caminhando na calçada e carros passando pela rua, quando consideramos ambientes externos. No caso de um ambiente estático, o problema pode ser resolvido planejando uma trajetória livre de colisões, sendo que não é necessário um replanejamento se todos os obstáculos estáticos foram considerados. Para ambientes onde os obstáculos estão em constante movimento, é necessário um constante replanejamento da trajetória para que se evite colisões. Alternativamente, pode ser verificada a possibilidade de se manter na rota planejada, alterando apenas a velocidade de cruzeiro do robô para que este desvie dos obstáculos móveis. Este trabalho propõe uma metodologia para calcular uma velocidade de cruzeiro para o robô de forma a minimizar a probabilidade de colisão com os obstáculos detectados pelos seus sensores. A escolha da variação de velocidade para o robô considera a sua velocidade atual, e as velocidades estimadas para os obstáculos. A metodologia para resolução deste problema é apresentada considerando incertezas na posição do robô e obstáculos. São apresentados resultados de simulação que exemplificam a aplicação da metodologia. / Following a free path is an important issue in the area of autonomous mobile robotics. The obstacles can be anything from walls and chairs in an indoor environment, or they can also be people walking on the sidewalk and cars moving through the street. In the case of a static environment, the problem can be solved by planning a path free from collisions, thus it is not essential another path planning as all static obstacles were considered. For an environment were the obstacles are constantly moving, it is necessary an unceasing path replanning to avoid possible collisions. Alternatively, keeping the robot on the previously calculated path can be verified modifying the robot\'s traffic velocity to avoid moving obstacles. Our proposal is to calculate a velocity for the robot which minimizes its collision probability with moving obstacles detected by its sensors. Varying the robot\'s velocity takes into account its current velocity and the estimated velocities of obstacles. The methodology for solving this problem is presented regarding uncertainties in robots and obstacles\' positions. Results from simulations that exemplifies an application for the methodology are presented.

O contínuo aumento da complexidade no controle de sistemas robóticos, bem como a aplicação de grupos de robôs auxiliando ou substituindo seres humanos em atividades críticas tem gerado uma importante demanda por soluções mais robustas, flexíveis, e eficientes. O desenvolvimento convencional de algoritmos especializados, constituídos de sistemas baseados em regras e de autômatos usados para coordenar estes conjuntos físicos em um ambiente dinâmico é um desafio extremamente complexo. Diversos modelos de desenvolvimento existem, entretanto, muitos desafios da área da robótica móvel autônoma continuam em aberto. Esta tese se insere no contexto da busca por soluções inteligentes a serem aplicadas em robôs móveis autônomos com o objetivo de permitir a operação destes em ambientes dinâmicos. Buscamos, com a investigação e aplicação de estratégias inteligentes por meio de aprendizado de máquina no funcionamento dos robôs, a proposta de soluções originais que permitam uma nova visão sobre a operação de robôs móveis em três dos desafios da área da robótica móvel autônoma, que são: localização, navegação e operações com grupos de robôs. As pesquisas sobre localização e coordenação de grupos apresentam investigação e propostas originais, buscando estender o estado da arte, onde apresentam resultados inovadores. A parte sobre navegação tem como objetivo principal ser um elo entre os conceitos de localização e coordenação de grupos, sendo o foco o desenvolvimento de um veículo autônomo com maior implicação em avanços técnicos. Relacionado com a coordenação de grupos de robôs, fizemos a escolha de trabalhar sobre uma aplicação modelada como o problema de combate a incêndios florestais. Buscamos desenvolver um ambiente de simulação realístico, onde foram avaliadas quatro técnicas para busca de iii estratégias de formação do grupo: Algoritmos Genéticos, Otimização por Enxame de Partículas, Hill Climbing e (iv) Simulated Annealing. Com base nas diversas avaliações realizadas pudemos mostrar quais das técnicas e conjuntos de parâmetros permitem a obtenção de resultados mais acurados que os demais. Além disso, mostramos como uma heurística baseada em populações anteriores pode auxiliar na tolerância a falhas da operação. Relacionado com a tarefa de navegação, apresentamos o desenvolvimento de um veículo autônomo de grande porte funcional para ambientes externos. Buscamos aperfeiçoar uma arquitetura para navegação autônoma, baseada em visão monocular e com capacidade de seguir pontos esparsos de GPS. Mostramos como a simulação e os usos de robôs de pequeno porte auxiliaram no desenvolvimento do veículo de grande porte e apresentamos como as redes neurais podem ser aplicadas nos modelos de navegação autônoma. Na investigação sobre localização, mostramos um método utilizando informação obtida de redes sem fio para prover informação de localização para robôs móveis. As informações obtidas da rede sem fio são utilizadas para aprendizado da posição de um robô móvel por meio de uma rede neural. Diversas avaliações foram realizadas buscando entender o comportamento do sistema com diferentes números de pontos de acesso, com uso de filtros, com diferentes topologias. Os resultados mostram que o modelo usando redes sem fio pode ser um possível método prático e barato para localização de robôs móveis. Esta tese aborda temas relevantes e propostas originais relacionadas com os objetivos propostos, apresentando métodos que provenham autonomia na coordenação de grupos e nas atividades individuais dos mesmos. A busca por altos graus de eficiência na resolução de tarefas em ambientes dinâmicos ainda é um campo que carece de soluções e de um aprofundamento nas pesquisas. Sendo assim, esta pesquisa buscou agregar diversos avanços científicos na área de pesquisa de robôs móveis autônomos e coordenação de grupos, por meio da aplicação de estratégias inteligentes / The constant increasing of the complexity in the control of robotic systems, as well as the application of groups of robots assisting or replacing human beings in critical activities has generated a significant demand for more robust, flexible and efficient solutions. The conventional development of specialized algorithms consisted of rule-based systems and automatas, used to coordinate these physical sets in a dynamic environment is an extremely complex challenge. Although several models of development of robotic issues are currently in use, many challenges in the area remain open. This thesis is related to the search for intelligent strategies to be applied in autonomous mobile robots in order to allow practical operations in dynamic environments. We seek, with the investigation of intelligent strategies by means of the use of machine learning in the robots, to propose original solutions to allow contributions in three challenges of the robotic research area: localization, navigation and coordination of groups of robots. The investigations about localization and groups of robots show novel and original proposals, where we sought to extend the state of the art. The navigation part has as its major objective to be a link between the subjects of localization and navigation, being its aim to help the deployment of a autonomous vehicle implying in greater technical advances. Related to the robotic group coordination, we have made the choice to work on an application modeled as a wildfire combat operation. We have developed a simulation environment in which we have evaluated four techniques to obtain strategies for the group formation: genetic algorithms, particle swarm optimization, hill climbing and simulated annealing. The v results showed that we can have very different accuracy with different techniques and sets of parameters. Furthermore, we show how a heuristic based on the use of past populations can assist in fault tolerant operation. Related to the autonomous navigation task, we present the development of a large autonomous vehicle capable of operating in outdoor environments. We sought to optimize an architecture for autonomous navigation based on monocular vision and with the ability to follow scattered points of GPS.We show how the use of simulation and small robots could assist in the development of large vehicle. Furthermore, we show how neural networks can be applied as a controller to autonomous navigation systems. In the investigation about localization, we presented a method using wireless networks to provide information about localization to mobile robots. The information gathered by the wireless network is used as input in an artificial neural network which learns the position of the robot. Several evaluations were carried out in order to understand the behavior of the proposed system, as using different topologies, different numbers of access points and the use of filters. Results showed that the proposed system, using wireless networks and neural networks, may be a useful and easy to use solution for localization of mobile robots. This thesis has addressed original and relevant topics related to the proposed objectives, showing methods to allow degrees of autonomy in robotic operations. The search for higher degrees of efficiency in tasks solving in dynamic environments is still a field that lacks solutions. Therefore, this study sought to add several scientific contributions in the autonomous mobile robots research area and coordination of groups, by means of the application of intelligent strategies

Arctic regions are thought to be more sensitive to climate change fluctuations, making weather data from these regions more valuable for climate modeling. Scientists have expressed an interest in deploying a robotic sensor network in these areas, minimizing the exposure of human researchers to the harsh environment, while allowing dense, targeted data collection to commence. For any such robotic system to be successful, a certain set of base navigational functionality must be developed. Further, these navigational algorithms must rely on the types of low-cost sensors that would be viable for use in a multi-agent system. A set of vision-based processing techniques have been proposed, which augment current robotic technologies for use in glacial terrains. Specifically, algorithms for estimating terrain traversability, robot localization, and terrain reconstruction have been developed which use data collected exclusively from a single camera and other low-cost robotic sensors. For traversability assessment, a custom algorithm was developed that uses local scale surface texture to estimate the terrain slope. Additionally, a horizon line estimation system has been proposed that is capable of coping with low-contrast, ambiguous horizons. For localization, a monocular simultaneous localization and mapping (SLAM) filter has been fused with consumer-grade GPS measurements to produce full robot pose estimates that do not drift over long traverses. Finally, a terrain reconstruction methodology has been proposed that uses a Gaussian process framework to incorporate sparse SLAM landmarks with dense slope estimates to produce a single, consistent terrain model. These algorithms have been tested within a custom glacial terrain computer simulation and against multiple data sets acquired during glacial field trials. The results of these tests indicate that vision is a viable sensing modality for autonomous glacial robotics, despite the obvious challenges presented by low-contrast glacial scenery. The findings of this work are discussed within the context of the larger arctic sensor network project, and a direction for future work is recommended.

An urban operation of unmanned aerial vehicles (UAVs) demands a high level of autonomy for tasks presented in a cluttered environment. While fixed-wing UAVs are well suited for long-endurance missions at a high altitude, enabling them to navigate inside an urban area brings another level of challenges. Their inability to hover and low agility in motion cause more difficulties on finding a feasible path to move safely in a compact region, and the limited payload allows only low-grade sensors for state estimation and control. We address the problem of achieving vision-based autonomous navigation for a small fixed-wing in an urban area with contributions to the following several key topics. Firstly, for robust attitude estimation during dynamic maneuvering, we take advantage of the line regularity in an urban scene, which features vertical and horizontal edges of man-made structures. The sensor fusion with gravity-related line segments and gyroscopes in a Kalman filter can provide driftless and realtime attitude for ight stabilization. Secondly, as a prerequisite to sensor fusion, we present a convenient self-calibration scheme based on the factorization method. Natural references such as gravity, vertical edges, and distant scene points, available in urban fields, are sufficient to find intrinsic and extrinsic parameters of inertial and vision sensors. Lastly, to generate a dynamically feasible motion plan, we propose a discrete planning method that encodes a path into interconnections of finite trim states, which allow a significant dimension reduction of a search space and result in naturally implementable paths integrated with ight controllers. The most probable path to reach a target is computed by the Markov Decision Process with motion uncertainty due to wind, and a minimum target observation time is imposed on the final motion plan to consider a camera's limited field-of-view. In this thesis, the effectiveness of our vision-based navigation system is demonstrated by what we call an "air slalom" task in which the UAV must autonomously search and localize multiple gates, and pass through them sequentially. Experiment results with a 1m wing-span airplane show essential navigation capabilities demanded in urban operations such as maneuvering passageways between buildings.

La robotique mobile autonome est un axe de recherche qui vise à donner à une machine la capacité de se mouvoir dans un environnement sans assistance ni intervention humaine. Cette thèse s’intéresse à la partie décisionnelle de la navigation robotique à savoir la planification de mouvement pour un robot mobile non-holonome, pour lequel, la prise en compte des contraintes cinématiques et non-holonomes est primordiale. Aussi, la nécessité de considérer la géométrie propre du robot et la bonne maîtrise de l’environnement dans lequel il évolue constituent des contraintes à assurer. En effet la planification de mouvement consiste à calculer un mouvement réalisable que doit accomplir le robot entre une position initiale et une position finale données. Selon la nature de l’environnement, notamment les obstacles qui s’y présentent, deux instances du problème se distinguent : la planification de chemin et la planification de trajectoire. L’objectif de cette thèse est de proposer de nouveaux algorithmes pour contribuer aux deux instances du problème de planification de mouvement. La méthodologie suivie repose sur des solutions génériques qui s’appliquent à une classe de systèmes robotiques plutôt qu’à une architecture particulière. Les approches proposées intègrent les B-splines Rationnelles non uniformes (NURBS) dans le processus de modélisation des solutions générées tout en s’appuyant sur la propriété de contrôle local, et utilisent les algorithmes génétiques pour une meilleure exploration de l’espace de recherche. / The mobile robotics is an area of research that aims to give a machine the ability to move in an environment without assistance or human intervention. This thesis focuses on the decisional part of robotic navigation, namely motion planning for a non-holonomic mobile robot, for which, the consideration of kinematic and non-holonomic constraints is paramount. Also, the need to consider the specific geometry of the robot and the good control of the environment in which it operates are constraints to insure. Indeed, motion planning is to calculate a feasible movement to be performed by the robot between an initial and a final given position. Depending on the nature of the environment, two instances of the problem stand out: the path planning and the trajectory planning. The objective of this thesis is to propose new algorithms to contribute to the two instances of motion planning problem. The followed methodology is based on generic solutions that are applicable to a class of robotic systems rather than a particular architecture. The proposed approaches include the Non-Uniform Rational B-Spline (NURBS) in the modeling process of the generated solutions while relying on the local control property. Also, they use genetic algorithms for better exploration of the search space.

L'auteur n'a pas fourni de résumé en français / This thesis focuses on semi-autonomous wheelchair navigation. We aim to design asystem respecting the following constraints.Safety: The system must avoid collisions with objects and specially with humans present in the scene.Usability: People with motor disabilities and elders often have problems using joysticks and other standard control devices. The use of more sophisticated and human-like ways of interacting with the robot must be addressed to improve the acceptance and comfort for the user. It is also considered that the user could just be able to move one finger and so the request of human intervention should be as reduced as possible to accomplish the navigation task.Compliance:} The robot must navigate securely among obstacles while reducing the frustration caused to the user by taking into account his intentions at different levels; final destination, preferred path, speed etc.Respect of social conventions: When moving, the robot may considerably disturb people around it, especially when its behavior is perceived as unsocial. It is thus important to produce socially acceptable motion to reduce disturbances. We will also addresses the issue of determining those places where the robot should be placed in order become part of an interacting group.In this work we propose to estimate the user's intention in order to reduce thenumber of necessary commands to drive a robotic wheelchair and deal withambiguous or inaccurate input interfaces. In this way, the wheelchair can be incharge of some part of the navigation task and alleviate the user involvement.The proposed system takes into account the user intention in terms of the finaldestination and desired speed. At each level, the method tries to favor themost ``reasonable'' action according to the inferred user intention.The user intention problem is approached by using a model of the user based onthe hypothesis that it is possible to learn typical destinations (those wherethe user spends most of his time) and use this information to enhance theestimation of the destination targeted by the user when he is driving therobotic wheelchair.A probabilistic framework is used to model the existent relationship betweenthe intention of the user and the observed command. The main originality of theapproach relies on modeling the user intentions as typical destinations and theuse of this estimation to check the reliability of a user's command to decidehow much preeminence it should be assigned by the shared controller whenmanaging the robot's speed.The proposed shared-control navigation system considers the direction of thecommands given by the user, the obstacles detected by the robot and the inferreddestination to correct the robot's velocity when necessary. This system is basedon the dynamic window approach modified to consider the input given by the user,his intention, the obstacles and the wheelchair's dynamic constraints tocompute the appropriate velocity command.All of the results obtained in this thesis have been implemented and validatedwith experiments, using both real and simulated data. Real data has beenobtained on two different scenarios; one was at INRIA's entry hall and the otherat the experimental apartment GERHOME.

Dans le cadre de cette thèse, nous abordons les problèmes liés à un robot autonome devant apprendre différentes tâches sensori-motrices dans des situations d’interaction et d’imitation. D’un point de vue théorique et dans le cadre des Interactions Homme-Machine (IHM), nous aborderons notamment les notions liées aux phénomènes d’entraînement rythmiques, de coordination interpersonnelle et de synchronisation intentionnelles et non-intentionnelles qui jouent un rôle important dans les interactions sociales. En particulier nous défendons l’hypothèse que des mécanismes d’entrainement rythmiques facilitent grandement l’apprentissage dans des situations d’interactions Homme-Robot. Leur prise en compte dans la conception de nouvelles IHMs est primordiale pour les rendre plus « intuitives ». A long terme, nous aimerions que le robot puisse utiliser ces signaux sociaux pour comprendre les intentions de son partenaire.L’utilisation d’une tâche complexe est ici nécessaire pour que le robot soit confronté à des situations d’échecs qui devront introduire des modifications de comportement non seulement de la part du partenaire mais aussi et surtout de la part du robot. Ces situations introduiront des modifications du rythme dans l’interaction au cours de l'apprentissage. Pour réussir ce pari, le robot devra être capable d’auto évaluer à la fois son comportement vis à vis de la tâche en cours d’exécution (et d’apprentissage) et son comportement d’interaction avec son partenaire.Nous proposons de travailler sur un « robot mobile compagnon » capable d’apprendre de manière interactive (avec son compagnon) à naviguer. L’utilisation de jeux d’interaction et d’imitation comme moyen d’apprentissage mais surtout de communication permet d’envisager de nouveaux principes d’IHM (Interface Homme-Machine) où l’interaction n’est plus considérée comme une « charge », elle devient au contraire plaisante. / In this thesis, we address the issues related to autonomous learning of different sensory-motor tasks using interaction and imitation. From a theoretical point of view and considering Human Machine Interaction (HMI), we will question the concepts linked to rhythmic entrainment, interpersonal coordination, and also intentional and unintentional synchronisations and their contribution to improve social interactions. Particularly, these mechanisms facilitate human-human interactions. Consequently, we defend the idea that taking them into account is essential to build more intuitive HMI.  In near future applications,  we would like to make the robot able to use these signals to improve its understanding the human partner intentions.Using a complex task is, then, necessary to confront the robot to failures that will introduce behavioral changes for both the human and the robot agents. These situations will introduce interaction rhythm modifications during learning phases. In this aim, the robot should be able to self assess its behavior regarding both the current task to learn and its interaction with its partner.We propose to work on a 'mobile companion robot' able to learn interactively (with its partner) to navigate. Using interaction games and imitation as a medium of learning and above all of communication, allows to consider new principles of HMI (Human Machine Interface) where the interaction is no longer considered as a 'load', it becomes rather pleasant.