Nous observons ces dernières années un besoin grandissant dans l’industrie pour des robots capables d’interagir et de coopérer dans des environnements confinés. Cependant, aujourd’hui encore, la définition de trajectoires sûres pour les robots industriels doit être faite manuellement par l’utilisateur et le logiciel ne dispose que de peu d’autonomie pour réagir aux modifications de l’environnement. Cette thèse vise à produire une structure logicielle innovante pour gérer l’évitement d’obstacles en temps réel pour des robots manipulateurs évoluant dans des environnements dynamiques. Nous avons développé pour cela un algorithme temps réel de génération de trajectoires qui supprime de façon automatique l’étape fastidieuse de définition d’une trajectoire sûre pour le robot.La valeur ajoutée de cette thèse réside dans le fait que nous intégrons le problème de contrôle optimal dans le concept de hiérarchie de tâches pour résoudre un problème d’optimisation non-linéaire efficacement et en temps réel sur un système embarqué aux ressources limitées. Notre approche utilise une commande prédictive (MPC) qui non seulement améliore la réactivité de notre système mais présente aussi l’avantage de pouvoir produire une bonne approximation linéaire des contraintes d’évitement de collision. La stratégie de contrôle présentée dans cette thèse a été validée à l’aide de plusieurs expérimentations en simulations et sur systèmes réels. Les résultats démontrent l’efficacité, la réactivité et la robustesse de cette nouvelle structure de contrôle lorsqu’elle est utilisée dans des environnements dynamiques. / In the field of industrial robots, there is a growing need for having cooperative robots that interact with each other and share work spaces. Currently, industrial robotic systems still rely on hard coded motions with limited ability to react autonomously to dynamic changes in the environment. This thesis focuses on providing a novel framework to deal with real-time collision avoidance for robots performing tasks in a dynamic environment. We develop a reactive trajectory generation algorithm that reacts in real time, removes the fastidious optimization process which is traditionally executed by hand by handling it automatically, and provides a practical way of generating locally time optimal solutions.The novelty in this thesis is in the way we integrate the proposed time optimality problem in a task priority framework to solve a nonlinear optimization problem efficiently in real time using an embedded system with limited resources. Our approach is applied in a Model Predictive Control (MPC) setting, which not only improves reactivity of the system but presents a possibility to obtain accurate local linear approximations of the collision avoidance constraint. The control strategies presented in this thesis have been validated through various simulations and real-world robot experiments. The results demonstrate the effectiveness of the new control structure and its reactivity and robustness when working in dynamic environments.

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.

Coordenação de robôs móveis é um tópico importante de pesquisa dado que existem tarefas que podem ser desenvolvidas de forma mais eficiente e com menor custo por um grupo de robôs do que por um só robô. Nesta dissertação é apresentado um estudo sobre coordenação de robôs móveis para o problema de navegação em ambientes externos. Para isso, foi desenvolvido um sistema de localização utilizando os dados de odometria e do receptor GPS, e um sistema de desvio de obstáculos para planejar a trajetória livre de obstáculos. Os movimentos coordenados foram realizados em função de um líder e qualquer robô da formação pode assumir a liderança. A liderança é assumida pelo robô que ultrapassar a distância mínima a um obstáculo. Movimentos estáveis são gerados através de uma lei de controle descentralizada baseada nas coordenadas dos robôs. Para garantir a estabilidade da formação quando há alternância de líder ou remoção de robôs, foi feito controle tolerante a falhas para um grupo de robôs móveis. O controle tolerante a falhas é baseado em controle H \'INFINITO\' por realimentação da saída de sistemas lineares sujeitos a saltos Markovianos para garantir a estabilidade da formação quando um dos robôs é perdido durante o movimento coordenado. Os resultados do sistema de localização mostram que o uso de filtro robusto para a fusão de dados produz uma melhor estimativa da posição do robô móvel. Os resultados também mostram que o sistema de desvio de obstáculos é capaz de gerar uma trajetória livre de obstáculos em ambientes desconhecidos. E por fim, os resultados do sistema de coordenação mostram que o grupo de robôs mantém a formação desejada percorrendo a trajetória de referência na presença de distúrbios ou quando um robô sai da formação. / Coordination of mobile robots is an important topic of research because there are tasks that may be too difficult for a single robot to perform alone, these tasks can be performed more efficiently and cheaply by a group of mobile robots. This dissertation presents a study on the coordination of mobile robots to the problem of navigation in outdoor environments. To solve this problem, a localization system using data from odometry and GPS receiver, and an obstacle avoidance system to plan the collision-free trajectory, were developed. The coordinated motions are performed by the robots that follow a leader, and any robot of the formation can assume the leadership. The leadership is assumed by a robot when it exceeds the threshold distance to an obstacle. Stable motions are generated by a decentralized control law based on the robots coordinates. To ensure the stability formation when there is alternation of leader or one of the robots is removed, we made a fault tolerant control for a group of mobile robots. The fault tolerant approach is based on output feedback H \'INFINITE\' control of Markovian jump linear systems to ensure stability of the formation when one of the robots is lost during the coordinated motion. The results of the localization system show that the use of robust filter for data fusion produces a better estimation of the mobile robots position. The results also show that the obstacle avoidance system is capable of generating a path free from obstacles in unknown environments. Finally, the results of the coordination system show that the group of robots maintain the desired formation along the reference trajectory in the presence of disturbance or removal of one of them.

Ground Penetrating Radar (GPR) is a tool for mapping the subsurface in a non-invasive way. GPR surveys are currently carried out manually; a time-consuming, tedious and sometimes dangerous task. This report presents the high-level software components for an autonomous unmanned ground vehicle to conduct GPR surveys. The hardware system is a four-wheel drive, skid steering, battery operated vehicle with integrated GPR equipment. Autonomous surveys are conducted using lidar-inertial odometry with robust path planning, path following and obstacle avoidance capabilities. Evaluation shows that the vehicle is able to autonomously execute a planned survey with high accuracy and stops before collisions occur. This system enables high-frequency surveys to monitor the evolution of an area over time, allows one operator to monitor multiple surveys at once, and facilitates future research into novel survey patterns that are difficult to follow manually

The Micro Aerial Vehicless (MAVs) are gaining attention in numerous applications asthese platforms are cheap and can do complex maneuvers. Moreover, most of the commer-cially available MAVs are equipped with a mono-camera. Currently, there is an increasinginterest to deploy autonomous mono-camera MAVs with obstacle avoidance capabilitiesin various complex application areas. Some of the application areas have moving obstaclesas well as stationary, which makes it more challenging for collision avoidance schemes.This master thesis set out to investigate the possibility to avoid moving and station-ary obstacles with a single camera as the only sensor gathering information from thesurrounding environment.One concept to perform autonomous obstacle avoidance is to predict the time near-collision based on a Convolution Neural Network (CNN) architecture that uses the videofeed from a mono-camera. In this way, the heading of the MAV is regulated to maximizethe time to a collision, resulting in the avoidance maneuver. Moreover, another interestingperspective is when due to multiple dynamic obstacles in the environment there aremultiple time predictions for different parts of the Field of View (FoV). The method ismaximizing time to a collision by choosing the part with the largest time to collision.However, this is a complicated task and this thesis provides an overview of it whilediscussing the challenges and possible future directions. One of the main reason was thatthe available data set was not reliable and was not provide enough information for theCNN to produce any acceptable predictions.Moreover, this thesis looks into another approach for avoiding collisions, using objectdetection method You Only Lock Once (YOLO) with the mono-camera video feed. YOLOis a state-of-the-art network that can detect objects and produce bounding boxes in real-time. Because of YOLOs high success rate and speed were it chosen to be used in thisthesis. When YOLO detects an obstacle it is telling where in the image the object is,the obstacle pixel coordinates. By utilizing the images FoV and trigonometry can pixelcoordinates be transformed to an angle, assuming the lens does not distort the image.This position information can then be used to avoid obstacles. The method is evaluated insimulation environment Gazebo and experimental verification with commercial availableMAV Parrot Bebop 2. While the obtained results show the efficiency of the method. To bemore specific, the proposed method is capable to avoid dynamic and stationary obstacles.Future works will be the evaluation of this method in more complex environments with multiple dynamic obstacles for autonomous navigation of a team of MAVs. A video ofthe experiments can be viewed at:https://youtu.be/g_zL6eVqgVM.

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Det här projektet optimerar ett system som använder den statiska sökalgoritmen A* för att fåen autonom drönare att kunna undvika rörliga och målsökande hinder på sin färd emot enangiven måldestination. Optimeringen bygger på tidigare arbeten där bland annat ModelPredictive Control (MPC) har en stor påverkan på det implementerade systemet.Resultatet av projektet visar att det är möjligt att optimera ett system som använder sig av enstatisk planeringsalgoritm genom lokal planering inom det område drönaren har kunskap om.Ett högt planeringstempo där drönaren enbart följer första delen i den genererade planen,möjliggör att drönaren hela tiden kan anpassa sig till förändringar i omgivningen och undvikakollision. / This project optimizes a system that uses the static search algorithm A* to enable anautonomous drone to avoid moving and target-seeking obstacles on its way to a specifieddestination. The optimization is based on previous work where Model Predictive Control(MPC) has a major impact on the implemented system.The result of the project shows that it is possible to optimize a system using a static planningalgorithm through local planning in the area of which the drone has knowledge. A highplanning pace enables the drone to follow the first part of the generated plan, which meansthat the drone can constantly adapt to changes in the surroundings and avoid collisions.

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Obstacle avoidance is a well researched area, however most of the works only consider a 2D environment. Drones can move in three dimensions. It is therefore of interest to develop a system that ensures safe flight in these three dimensions. Obstacle avoidance is of highest importance for drones if they are intended to work autonomously and around humans, since drones are often fragile and have fast moving propellers that can hurt humans. This project is based on the obstacle restriction algorithm in 3D, and uses OctoMap to conveniently use the sensor data from multiple sensors simultaneously and to deal with their limited field of view. The results show that the system is able to avoid obstacles in 3D. / Hinderundvikande är ett utforskat område, dock för det mesta har forskningen fokuserat på 2D-miljöer. Eftersom drönare kan röra sig i tre dimensioner är det intressant att utveckla ett system som garanterar säker rörelse i 3D. Hinderundvikande är viktigt för drönare om de ska arbeta autonomt runt människor, eftersom drönare ofta är ömtåliga och har snabba propellrar som kan skada människor. Det här projektet är baserat på Hinderrestriktionsmetoden (ORM), och använder OctoMap för att använda information från många sensorer samtidigt och för att hantera deras begränsade synfält. Resultatet visar att systemet kan undvika hinder i 3D.

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.