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.

"The National Aeronautics and Space Administration (NASA) plans to send a human exploration team to Mars within the next 25 years. In support of this effort Hamilton Standard Space Systems International (HSSSI), current manufacturers of the Space Shuttle spacesuit, began exploring alternative solutions for supporting an astronaut during a Martian surface exploration. A design concept was developed by HSSSI to integrate a minimally equipped Martian spacesuit with a robotic support cart capable of providing life support assistance, communications, and independent navigational functions. To promote NASA's visionary efforts and increase university relations, HSSSI partnered with Worcester Polytechnic Institute (WPI) to develop a proof-of-concept robotic support cart system, FIDOE - Fully Independent Delivery of Expendables. As a proof-of-concept system, the primary goal of this project was to demonstrate the feasibility of current technologies utilized by FIDOE's communication and controls system for future Martian surface explorations. The primary objective of this project was to procure selected commercial-off-the-shelf components and configure these components into a functional robotic support cart. The design constraints for this project, in addition to the constraints imposed by the Martian environment and HSSSI's Martian spacesuit, were a one-year time frame and a $20,000 budget for component procurement. This project was also constrained by the protocols defined by the NASA demonstration test environment. The final design configuration comprised of 37 major commercial off-the-shelf components and three individual software packages that integrated together to provide FIDOE's communications and control capabilities. Power distribution was internally handled through a combination of a main power source and dedicated power supplies. FIDOE also provided a stowage area for handling assisted life support systems and geological equipment. The proof-of-concept FIDOE system proved that the current technologies represented by the selected components are feasible applications for a Mars effort. Specifically, the FIDOE system demonstrated that the chosen technologies can be integrated to perform assisted life support and independent functions. While some technologies represented by the proof-of-concept system may not adequately address the robustness issues pertaining to the Mars effort, e.g., voice recognition and power management, technology trends indicate that these forms of technology will soon become viable solutions to assisting an astronaut on a Martian surface exploration."

L'objectif de cette thèse est de définir puis d'étudier les performances de méthodes de guidage coopératif de véhicules aériens autonomes. L'intérêt du guidage coopératif est de confier une mission complexe à une flotte, plutôt qu'à un véhicule unique, afin de distribuer la charge de travail et d'améliorer les performances et la fiabilité. Les lois de guidage étudiées sont distribuées sur l'ensemble des véhicules afin d'une part, de répartir la charge de calcul et d'autre part, d'augmenter la fiabilité en éliminant la possibilité de perte de l'organe central de calcul de la commande.La première partie de la thèse porte sur les possibilités offertes par la règle des plus proches voisins. La loi de guidage développée consiste à ce que la commande de chaque véhicule soit élaborée en combinant les états des véhicules voisins. Afin de transmettre des consignes au groupe de véhicules, des objets dénommés agents virtuels sont introduits. Ceux-ci permettent de représenter des obstacles, d'indiquer une direction ou une cible au groupe de véhicules en utilisant des mécanismes déjà présent dans la loi de guidage.La seconde partie de la thèse porte sur les possibilités offertes par la commande prédictive. Ce type de commande consiste à employer un modèle du comportement du système afin de prédire les effets de la commande, et ainsi de déterminer celle qui minimise un critère de coût en respectant les contraintes du système. La loi de guidage développée emploi un critère de coût tenant compte et arbitrant entre les différents aspects de la mission (sécurité, progression de la mission, modération de la commande), et une procédure de recherche de la commande utilisant jeu prédéfinis de commandes candidates afin d'explorer l'espace de commande de manière efficace. Cette procédure, distincte des algorithmes d'optimisation habituels, génère une charge de calcul faible et constante, ne nécessite pas d'étape d'initialisation et est très peu sensible aux minima locaux. / The thesis objective is to define and study the performances of cooperative guidance methods of autonomous aerial vehicles. The interest of cooperative guidance is to entrust a complex mission to a fleet, instead of an isolated vehicle, to distribute the workload and improve performances and reliability. Studied guidance laws are distributed among all vehicles, on one hand to distribute the computation load, and on the other hand to remove the possibility to lose the centralized organ of command computation.The first part deals with the possibilities offered by the nearest neighbour rule. The developed guidance law consists in elaborating the command of each vehicle by combining the states of neighbour vehicles. To transmit instructions to the fleet of vehicles, objects denominated virtual agents are introduced. These allow figuring obstacles, indicating direction or target using existing mechanisms of the guidance law.The second part deals with the possibilities offered by model predictive control. This type of command consists in employing a behavioural model of the system to predict the control effects, and thus finding the one that minimises a cost criterion while respecting system's constraints. The developed guidance law uses a cost criterion that take into account and arbitrate between the several aspects of the mission (safety, mission evolution, control moderation), and a control search procedure based on a predefined set of candidate controls to explore the control space efficiently. This procedure, different from usual optimisation algorithms, generate a low and constant computation load, needs no initialisation step and is little sensitive to local minima.

The focus of this study was deep learning. A small, autonomous robot car was used for obstacle avoidance experiments. The robot car used a camera for taking images of its surroundings. A convolutional neural network used the images for obstacle detection. The available dataset of 31 022 images was trained with the Xception model. We compared two different implementations for making the robot car avoid obstacles. Mapping image classes to steering commands was used as a reference implementation. The main implementation of this study was to separate obstacle detection and steering logic in different modules. The former reached an obstacle avoidance ratio of 80 %, the latter reached 88 %. Different hyperparameters were looked at during training. We found that frozen layers and number of epochs were important to optimize. Weights were loaded from ImageNet before training. Frozen layers decided how many layers that were trainable after that. Training all layers (no frozen layers) was proven to work best. Number of epochs decided how many epochs a model trained. We found that it was important to train between 10-25 epochs. The best model used no frozen layers and trained for 21 epochs. It reached a test accuracy of 85.2 %.

Ce travail traite de l'évitement d'obstacles pour les robots mobiles à roues. D’abord, deux solutions sont proposées dans le cas d’un seul robot autonome. La première est une amélioration de la technique des champs de potentiel afin de contraster l’apparition de minima locaux. Le résultat se base sur l’application de la définition de l’ «Input-to-State Stability» pour des ensembles décomposables. Chaque fois que le robot mobile approche un minimum local l’introduction d’un contrôle dédié lui permet de l’éviter et de terminer la tâche. La deuxième solution se base sur l’utilisation de la technique du «Supervisory Control» qui permet de diviser la tâche principale en deux sous tâches : un algorithme de supervision gère deux signaux de commande, le premier en charge de faire atteindre la destination, le deuxième d’éviter les obstacles. Les deux signaux de commande permettent de compléter la mission en temps fini en assurant la robustesse par rapport aux perturbations représentant certaines dynamiques négligées. Les deux solutions ont été mises en service sur un robot mobile «Turtlebot 2». Pour contrôler une formation de type leader-follower qui puisse éviter collisions et obstacles, une modification de l’algorithme de supervision précédent a été proposée ; elle divise la tâche principale en trois sous-problèmes gérés par trois lois de commande. Le rôle du leader est adapté pour être la référence du groupe avec un rôle actif : ralentir la formation en cas de manœuvre d'évitement pour certains robots. La méthode proposée permet au groupe de se déplacer et à chaque agent d’éviter les obstacles, ou les collisions, de manière décentralisée / This dissertation work addresses the obstacle avoidance for wheeled mobile robots. The supervisory control framework coupled with the output regulation technique allowed to solve the obstacle avoidance problem and to formally prove the existence of an effective solution: two outputs for two objectives, reaching the goal and avoiding the obstacles. To have fast, reliable and robust results the designed control laws are finite-time, a particular class very appropriate to the purpose. The novelty of the approach lies in the easiness of the geometric approach to avoid the obstacle and on the formal proof provided under some assumptions. The solution have been thus extended to control a leader follower formation which, sustained from the previous result, uses two outputs but three controls to nail the problem. The Leader role is redesigned to be the reference of the group and not just the most advanced agent, moreover it has a active role slowing down the formation in case of collision avoidance manoeuvre for some robots. The proposed method, formally proven, makes the group move together and allow each agent to avoid obstacles or collision in a decentralized way. In addition, a further contribution of this dissertation, it is represented by a modification of the well known potential field method to avoid one of the common drawback of the method: the appearance of local minima. Control theory tools helps again to propose a solution that can be formally proven: the application of the definition of Input-to-State Stability (ISS) for decomposable sets allows to treat separate obstacles adding a perturbation which is able to move the trajectory away from a critic point

In this thesis, control and guidance algorithms for unmanned sea surface vehicles are studied. To design control algorithms of different complexity, first a mathematical model for an unmanned sea surface vehicle is derived. The dynamical and kinematical equations for a sea surface vehicle are obtained, and they are adapted to real life conditions with necessary additions and simplifications. The forces and torques effecting on the vehicle are investigated in detail. Control algorithms for under-actuated six degrees-of-freedom model are designed. PID and LQR controllers are implemented to attain desired surge speed and yaw position. The autopilots are designed and their performances are compared. Based on the autopilots, a guidance algorithm is implemented to achieve desired motions of the vehicle. An obstacle avoidance algorithm is proposed for safe motion among the obstacles. A next-point generation algorithm is designed to direct the vehicle to the most appropriate next way-point if the one ahead is missed. The effects of disturbances on the motion of the vehicle are studied thoroughly on simulation results. PID controller for an unmanned sea surface vehicle is implemented on ArduPilot Mega v1.4 cart controlling a Traxxas Spartan model boat. The performance of the controller is validated. Simulations and experimental results are provided.

The advantages of UAVs in the aviation arena have led to extensive research activities on autonomous technology of UAVs to achieve specific mission objectives. This thesis mainly focuses on the development of a mission-based guidance system. Among various missions expected of UAVs for future needs, autonomous formation flight (AFF) and obstacle avoidance within safe operation limits are investigated. In the design of an adaptive guidance system for AFF, the leader information except position is assumed to be unknown to a follower. Thus, the only measured information related to the leader is the line-of-sight range and angle. Adding an adaptive element with neural networks into the guidance system provides a capability to effectively handle leader's velocity changes. Therefore, this method can be applied to the AFF control systems that use passive sensing methods. The simulation and flight test results clearly show that the adaptive guidance control system is a promising solution for autonomous formation flight of UAVs. The successful flight evaluations using the GTMax rotary wing UAV also demonstrate unique maneuvering aspects associated with rotary wing UAVs in formation flight. In the design of an autonomous obstacle avoidance system, an integrated approach is proposed to resolve the conflict between aggressive maneuvering needed for obstacle avoidance and the constrained maneuvering needed for envelope protection. A time-optimal problem with obstacle and envelope constraints is used for an integrated approach for obstacle avoidance and envelope protection. The Nonlinear trajectory generator (NTG) is used as a real-time optimization solver. The computational complexity arising from the obstacle constraints is reduced by converting the obstacle constraints into a safe waypoint constraint along with an implicit requirement that the horizontal velocity during the avoidance maneuver must be non-negative. The issue of when to initiate a time-optimal avoidance maneuver is addressed by including a requirement that the vehicle must maintain its original flight path to the maximum extent possible. The simulation results using a rotary wing UAV demonstrate the feasibility of the proposed approach for obstacle avoidance with envelope protection.

<p>This thesis consists of four independent papers concerningthe control of mobile robots in the context of obstacleavoidance and formation keeping.</p><p>The first paper describes a new theoreticallyv erifiableapproach to obstacle avoidance. It merges the ideas of twoprevious methods, with complementaryprop erties, byusing acombined control Lyapunov function (CLF) and model predictivecontrol (MPC) framework.</p><p>The second paper investigates the problem of moving a fixedformation of vehicles through a partiallykno wn environmentwith obstacles. Using an input to state (ISS) formulation theconcept of configuration space obstacles is generalized toleader follower formations. This generalization then makes itpossible to convert the problem into a standard single vehicleobstacle avoidance problem, such as the one considered in thefirst paper. The properties of goal convergence and safetyth uscarries over to the formation obstacle avoidance case.</p><p>In the third paper, coordination along trajectories of anonhomogenuos set of vehicles is considered. Byusing a controlLyapunov function approach, properties such as boundedformation error and finite completion time is shown.</p><p>Finally, the fourth paper applies a generalized version ofthe control in the third paper to translate,rotate and expanda formation. It is furthermore shown how a partial decouplingof formation keeping and formation mission can be achieved. Theapproach is then applied to a scenario of underwater vehiclesclimbing gradients in search for specific thermal/biologicalregions of interest. The sensor data fusion problem fordifferent formation configurations is investigated and anoptimal formation geometryis proposed.</p><p><b>Keywords:</b>Mobile Robots, Robot Control, ObstacleAvoidance, Multirobot System, Formation Control, NavigationFunction, Lyapunov Function, Model Predictive Control, RecedingHorizon Control, Gradient Climbing, Gradient Estimation.</p>

This thesis consists of four independent papers concerningthe control of mobile robots in the context of obstacleavoidance and formation keeping. The first paper describes a new theoreticallyv erifiableapproach to obstacle avoidance. It merges the ideas of twoprevious methods, with complementaryprop erties, byusing acombined control Lyapunov function (CLF) and model predictivecontrol (MPC) framework. The second paper investigates the problem of moving a fixedformation of vehicles through a partiallykno wn environmentwith obstacles. Using an input to state (ISS) formulation theconcept of configuration space obstacles is generalized toleader follower formations. This generalization then makes itpossible to convert the problem into a standard single vehicleobstacle avoidance problem, such as the one considered in thefirst paper. The properties of goal convergence and safetyth uscarries over to the formation obstacle avoidance case. In the third paper, coordination along trajectories of anonhomogenuos set of vehicles is considered. Byusing a controlLyapunov function approach, properties such as boundedformation error and finite completion time is shown. Finally, the fourth paper applies a generalized version ofthe control in the third paper to translate,rotate and expanda formation. It is furthermore shown how a partial decouplingof formation keeping and formation mission can be achieved. Theapproach is then applied to a scenario of underwater vehiclesclimbing gradients in search for specific thermal/biologicalregions of interest. The sensor data fusion problem fordifferent formation configurations is investigated and anoptimal formation geometryis proposed. Keywords:Mobile Robots, Robot Control, ObstacleAvoidance, Multirobot System, Formation Control, NavigationFunction, Lyapunov Function, Model Predictive Control, RecedingHorizon Control, Gradient Climbing, Gradient Estimation. / QC 20111121

Η παρούσα διπλωματική ασχολείται με την πλοήγηση κινούμενου ρομπότ. Δεδομένου ενός χώρου με εμπόδια και στόχο, ασχολείται με την δημιουργία ενός αλγορίθμου για την οδήγηση του ρομπότ διαμέσου του χώρου στο στόχο, αποφεύγοντας τα εμπόδια κατά την κίνηση. Επικεντρώνεται σε δίτροχα ρομπότ και αναλύει βήμα βήμα την διαδικασία εύρεση μονοπατιού, δημιουργία τροχιάς και έλεγχο του ρομπότ. / The present thesis deals with the navigation of moving robots. Granted an area with obstacles and target, it deals with the creation of an algorithm for guiding the robot through space at target, avoiding obstacles during movement. It focuses on two-wheeled robots and analyzes step by step the process of finding a path, creating the trajectory and controlling the robot.