This thesis deals with the design and implementation of autonomous robot with using of the platform ROS. Its goal is to get to know the ROS and use it to implement autonomous control of real robot Leela.

For mobile robots which operate in human environments it is not sufficient to simply travel to their target destination as quickly as possible. Instead, mobile robots in human environments need to travel to their destination safely,  keeping a comfortable distance to humans and not colliding with any obstacles along the way. As the number of possible human-robot interactions is very large, defining a rule-based navigation approach is difficult in such highly dynamic environments. Current approaches solve this task by predicting the trajectories of humans in the scene and then planning a collision-free path. However, this requires separate components for detecting and predicting human motion and does not scale well to densely populated environments. Therefore, this work investigates the use of deep reinforcement learning for the navigation of mobile robots in human environments. This approach is based on recent research on utilizing deep neural networks in reinforcement learning to successfully play Atari 2600 video games on human level. A deep convolutional neural network is trained end-to-end from one-dimensional laser scan data to command velocities. Discrete and continuous action space implementations are evaluated in a simulation and are shown to outperform a Social Force Model baseline approach on the navigation problem for mobile robots in human environments.

Robot path planning and dynamic obstacle avoidance are defined as a problem that robots plan a feasible path from a given starting point to a destination point in a nonlinear dynamic environment, and safely bypass dynamic obstacles to the destination with minimal deviation from the trajectory. Path planning is a typical sequential decision-making problem. Dynamic local observable environment requires real-time and adaptive decision-making systems. It is an innovation for the robot to learn the policy directly from demonstration trajectories to adapt to similar state spaces that may appear in the future. We aim to develop a method for directly learning navigation behavior from demonstration trajectories without defining the environment and attention models, by using the concepts of Generative Adversarial Imitation Learning (GAIL) and Sequence Generative Adversarial Network (SeqGAN). The proposed SeqGAIL model in this thesis allows the robot to reproduce the desired behavior in different situations. In which, an adversarial net is established, and the Feature Counts Errors reduction is utilized as the forcing objective for the Generator. The refinement measure is taken to solve the instability problem. In addition, we proposed to use the Rapidly-exploring Random Tree* (RRT*) with pre-trained weights to generate adequate demonstration trajectories in dynamic environment as the training data, and this idea can effectively overcome the difficulty of acquiring huge training data.

The main objective of my research is to improve the control structure for the new Wheelchair Mounted Robotic Arm (WMRA) to include new algorithms for optimized task execution; that is, making the WMRA a modular task oriented mobile manipulator. The main criterion to be optimized is the fashion in which the wheelchair approaches a final target as well as the starting and final orientation of the wheelchair. This is a novel approach in non-holonomic wheeled manipulators that will help in autonomously executing complex activities of daily living (ADL) tasks. The WMRA is a 9 degree of freedom system, which provides 3 degrees of kinematic redundancy. A single control structure is used to control the WMRA system, which gives much more flexibility to the system. The combination of mobility and manipulation expands the workspace that a mobile base attains to a manipulator. This approach opens a broad field of applications: from maintenance and storage to rehabilitation robotics. This structure is based on optimization algorithms that can resolve redundancy based on several subtasks: maximizing the manipulability measure, minimizing the joint velocities (hence minimizing the energy), and avoiding joint limits. This work utilizes redundancy to control 2 separate trajectories, a primary trajectory for the end-effector and an optimized secondary trajectory for the wheelchair. Even though this work presents results and implementation in the WMRA system, this approach offers expandability to many wheeled base mobile manipulators in different types of applications. The WMRA usage was simulated in a virtual environment, by developing a test setting for sensors and task performance. The different trajectories and tasks can be shown in a virtual world created not only for illustration purposes, but to provide training to the users once the system is ready for use.

Afin de permettre une navigation autonome d'un robot dans un environnement, le robot doit être capable de percevoir son environnement. Dans la littérature, d'une manière générale, les robots perçoivent leur environnement en utilisant des capteurs de type sonars, cameras et lidar 2D. L'introduction de nouveaux capteurs, nommés lidar 3D, tels que le Velodyne HDL-64E S2, a permis aux robots d'acquérir plus rapidement des données 3D à partir de leur environnement. La première partie de cette thèse présente une technique pour la calibrage des capteurs lidar 3D. La technique est basée sur la comparaison des données lidar à un modèle de vérité de terrain afin d'estimer les valeurs optimales des paramètres de calibrage. La deuxième partie de la thèse présente une technique pour la localisation et la détection de fermeture de boucles pour les robots autonomes. La technique est basée sur l'extraction et l'indexation des signatures de petite-taille à partir de données lidar 3D. Les signatures sont basées sur les histogrammes de l'information de normales de surfaces locale extraite à partir des données lidar en exploitant la disposition des faisceaux laser dans le dispositif lidar / In order to autonomously navigate in an environment, a robot has to perceive its environment correctly. Rich perception information from the environment enables the robot to perform tasks like avoiding obstacles, building terrain maps, and localizing itself. Classically, outdoor robots have perceived their environment using vision or 2D lidar sensors. The introduction of novel 3D lidar sensors such as the Velodyne device has enabled the robots to rapidly acquire rich 3D data about their surroundings. These novel sensors call for the development of techniques that efficiently exploit their capabilities for autonomous navigation.The first part of this thesis presents a technique for the calibration of 3D lidar devices. The calibration technique is based on the comparison of acquired 3D lidar data to a ground truth model in order to estimate the optimal values of the calibration parameters. The second part of the thesis presents a technique for qualitative localization and loop closure detection for autonomous mobile robots, by extracting and indexing small-sized signatures from 3D lidar data. The signatures are based on histograms of local surface normal information that is efficiently extracted from the lidar data. Experimental results illustrate the developments throughout the manuscript

Cette thèse s'intéresse au problème de la navigation autonome au long cours de robots mobiles à roues dans des environnements dynamiques. Elle s'inscrit dans le cadre du projet FUI Air-Cobot. Ce projet, porté par Akka Technologies, a vu collaborer plusieurs entreprises (Akka, Airbus, 2MORROW, Sterela) ainsi que deux laboratoires de recherche, le LAAS et Mines Albi. L'objectif est de développer un robot collaboratif (ou cobot) capable de réaliser l'inspection d'un avion avant le décollage ou en hangar. Différents aspects ont donc été abordés : le contrôle non destructif, la stratégie de navigation, le développement du système robotisé et de son instrumentation, etc. Cette thèse répond au second problème évoqué, celui de la navigation. L'environnement considéré étant aéroportuaire, il est hautement structuré et répond à des normes de déplacement très strictes (zones interdites, etc.). Il peut être encombré d'obstacles statiques (attendus ou non) et dynamiques (véhicules divers, piétons, ...) qu'il conviendra d'éviter pour garantir la sécurité des biens et des personnes. Cette thèse présente deux contributions. La première porte sur la synthèse d'un asservissement visuel permettant au robot de se déplacer sur de longues distances (autour de l'avion ou en hangar) grâce à une carte topologique et au choix de cibles dédiées. De plus, cet asservissement visuel exploite les informations fournies par toutes les caméras embarquées. La seconde contribution porte sur la sécurité et l'évitement d'obstacles. Une loi de commande basée sur les spirales équiangulaires exploite seulement les données sensorielles fournies par les lasers embarqués. Elle est donc purement référencée capteur et permet de contourner tout obstacle, qu'il soit fixe ou mobile. Il s'agit donc d'une solution générale permettant de garantir la non collision. Enfin, des résultats expérimentaux, réalisés au LAAS et sur le site d'Airbus à Blagnac, montrent l'efficacité de la stratégie développée. / This thesis is directed towards the autonomous long range navigation of wheeled robots in dynamic environments. It takes place within the Air-Cobot project. This project aims at designing a collaborative robot (cobot) able to perform the preflight inspection of an aircraft. The considered environment is then highly structured (airport runway and hangars) and may be cluttered with both static and dynamic unknown obstacles (luggage or refueling trucks, pedestrians, etc.). Our navigation framework relies on previous works and is based on the switching between different control laws (go to goal controller, visual servoing, obstacle avoidance) depending on the context. Our contribution is twofold. First of all, we have designed a visual servoing controller able to make the robot move over a long distance thanks to a topological map and to the choice of suitable targets. In addition, multi-camera visual servoing control laws have been built to benefit from the image data provided by the different cameras which are embedded on the Air-Cobot system. The second contribution is related to obstacle avoidance. A control law based on equiangular spirals has been designed to guarantee non collision. This control law, based on equiangular spirals, is fully sensor-based, and allows to avoid static and dynamic obstacles alike. It then provides a general solution to deal efficiently with the collision problem. Experimental results, performed both in LAAS and in Airbus hangars and runways, show the efficiency of the developed techniques.

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