Mobile robots are increasingly being used to perform tasks in unknown environments. The potential of robots to undertake such tasks lies in their ability to intelligently and efficiently locate and interact with objects in their environment. My research focuses on developing algorithms to plan paths for mobile robots in a partially known environment observed by an overhead camera. The environment consists of dynamic obstacles and targets. A new methodology, Extrapolated Artificial Potential Field, is proposed for real time robot path planning. An algorithm for probabilistic collision detection and avoidance is used to enhance the planner. The aim of the robot is to select avoidance maneuvers to avoid the dynamic obstacles. The navigation of a mobile robot in a real-world dynamic environment is a complex and daunting task. Consider the case of a mobile robot working in an office environment. It has to avoid the static obstacles such as desks, chairs and cupboards and it also has to consider dynamic obstacles such as humans. In the presence of dynamic obstacles, the robot has to predict the motion of the obstacles. Humans inherently have an intuitive motion prediction scheme when planning a path in a crowded environment. A technique has been developed which predicts the possible future positions of obstacles. This technique coupled with the generalized Voronoi diagram enables the robot to safely navigate in a given environment.

No description available.

Cette thèse s'inscrit dans le cadre du projet Cart-O-Matic mis en place pour participer au défi CAROTTE (CArtographie par ROboT d'un TErritoire) organisé par l'ANR et la DGA. Le but de ce défi est de construire une carte en deux et trois dimensions et de localiser des objets dans un environnement inconnu statique de type appartement. Dans ce contexte, l'utilisation de plusieurs robots est avantageuse car elle permet d'augmenter l'efficacité en temps de la couverture. Cependant, comme nous le montrons, le gain est conditionné par le niveau de coopération entre les robots. Nous proposons une stratégie de coopération pour une cartographie multirobot efficace. Une difficulté est la construction d'une carte commune, nécessaire, afin que chaque robot puisse connaître les zones de l'environnement encore inexplorées. Pour obtenir une bonne coopération avec un algorithme simple nous proposons une technique de déploiement fondée sur le choix d'une cible par chaque robot. L'algorithme proposé cherche à distribuer les robots vers différentes directions. Il est fondé sur le calcul partiel de champs de potentiels permettant à chaque robot de calculer efficacement son prochain objectif. En complément de ces contributions théoriques, nous décrivons le système robotique complet mis en oeuvre au sein de l'équipe Cart-O-Matic ayant permis de remporter la dernière édition du défi CAROTTE / This thesis is part of Cart-O-Matic project set up to participate in the challenge CARROTE (mapping of a territory) organized by the ANR and the DGA. The purpose of this challenge is to build 2D and 3D maps of a static unknown 'apartment-like' environment. In this context, the use of several robots is advantageous because it increases the time efficiency to discover fully the environment. However, as we show, the gain is determined by the level of cooperation between robots. We propose a cooperation strategy for efficient multirobot mapping. A difficulty is the construction of a common map, necessary so that each robot can know the areas of the environment which remain unexplored.For a good cooperation with a simple algorithm we propose a deployment technique based on the choice of a target by each robot. The proposed algorithm tries to distribute the robots in different directions. It is based on calculation of the partial potential fields allowing each robot to compute efficiently its next target. In addition to these theoretical contributions, we describe the complete robotic system implemented in the Cart-O-Matic team that helped win the last edition of the CARROTE challenge

Multi-vehicle systems offer many advantages in engineering applications such as increased efficiency and robustness. However, the disadvantage of multi-vehicle systems is that they require a high level of organization and coordination in order to successfully complete a task. Formation control is a field of engineering that addresses this issue, and provides coordination schemes to successfully implement multi-vehicle systems. Two approaches to group coordination were proposed in this work: geometric and self-organizing formations. A geometric reconfiguring formation was developed using the leader-follower method, and the self-organizing formation was developed using the velocity potential equations from fluid flow theory. Both formation controllers were first tested in simulation in MATLAB, and then implemented on the X80 mobile robot units. Various experiments were conducted to test the formations under difficult obstacle scenarios. The robots successfully navigated through the obstacles as a coordinated as a team using the self-organizing and geometric formation control approaches.

Submitted by Fernando Souza (fernandoafsou@gmail.com) on 2017-08-11T14:01:11Z No. of bitstreams: 1 arquivototal.pdf: 11420527 bytes, checksum: 9c4b07869f327f3f311a4d4c15f0210b (MD5) / Made available in DSpace on 2017-08-11T14:01:11Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 11420527 bytes, checksum: 9c4b07869f327f3f311a4d4c15f0210b (MD5) Previous issue date: 2017-02-16 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Anti-collision systems are based on sensing and estimating the mobile robot pose (coordinates and orientation), with respect to its environment. Obstacles detection, path planning and pose estimation are primordial to ensure the autonomy and safety of the robot, in order to reduce the risk of collision with objects and living beings that share the same space. For this, the use of RGB-D sensors, such as the Microsoft Kinect, has become popular in the last years, for being relative accurate and low cost sensors. In this work we propose a new 3D anti-collision algorithm based on Artificial Potential Field method, that is able to make a mobile robot pass between closely spaced obstacles, minimizing the oscillations during the cross. We develop our Unmanned Ground Vehicles (UGV) system on a ’Turtlebot 2’ platform, with which we perform the experiments. / Sistemas anti-colisão são baseados na percepção e estimação da pose do robô móvel (coordenadas e orientação), em referência ao ambiente em que ele se encontra. A detecção de obstáculos, planejamento de caminhos e estimação da pose são fundamentais para assegurar a autonomia e a segurança do robô, no intuito de reduzir o risco de colisão com objetos ou pessoas que dividem o mesmo espaço. Para isso, o uso de sensores RGB-Ds, tal como o Microsoft Kinect, vem se tornando popular nos últimos anos, por serem sensores relativamente precisos e de baixo custo. Nesse trabalho nós propomos um novo algoritmo anti-colisão 3D baseado no método de Campo Potencial Artificial, que é capaz de fazer um robô móvel passar em espaços estreitos, entre obstáculos, minimizando as oscilações, que é uma característica comum desse tipo de método, durante seu deslocamento. Foi desenvolvido um sistema para plataforma robótica ’Turtlebot 2’, o qual foi utilizado para realizar todos os experimentos.

In car racing simulation games, finishing first is the main goal. To reach that goal, it is required to go around a racing track, competing against other cars aiming for the same goal. Implementing a bot for doing so may have its difficulties, although using a technique called multi-agent systems combined with artificial potential field, let- ting the agents take care of subtasks like keeping the car on the track, minimize how much the car turns in a curvature and basics in navigation around the track, has showed that artificial potential fields very well fit the problem of driving a car in simulated car racing in a competitive way. / Mobiltelefon: 0707422666

Multi-vehicle systems offer many advantages in engineering applications such as increased efficiency and robustness. However, the disadvantage of multi-vehicle systems is that they require a high level of organization and coordination in order to successfully complete a task. Formation control is a field of engineering that addresses this issue, and provides coordination schemes to successfully implement multi-vehicle systems. Two approaches to group coordination were proposed in this work: geometric and self-organizing formations. A geometric reconfiguring formation was developed using the leader-follower method, and the self-organizing formation was developed using the velocity potential equations from fluid flow theory. Both formation controllers were first tested in simulation in MATLAB, and then implemented on the X80 mobile robot units. Various experiments were conducted to test the formations under difficult obstacle scenarios. The robots successfully navigated through the obstacles as a coordinated as a team using the self-organizing and geometric formation control approaches.

No description available.

The use of unmanned aerial vehicles (UAV), so-called drones, has been growingrapidly in the last decade. Today, they are used for, among other things, monitoring missions and inspections of places that are difficult for people to access. Toefficiently and robustly execute these types of missions, a swarm of drones maybe used, i.e., a collection of drones that coordinate together. However, this introduces new requirements on what solutions are used for control and navigation. Two important aspects of autonomous navigation of drone swarms are formationcontrol and collision avoidance. To manage these problems, we propose four different solution algorithms. Two of them use leader-follower control to keep formation, Artificial PotentialField (APF) for path planning and Control Barrier Function (CBF)/ExponentialControl Barrier Function (ECBF) to guarantee that the control signal is safe i.e.the drones keep the desired safety distance. The other two solutions use an optimal control problem formulation of a motion planning problem to either generate open-loop or closed-loop trajectories with a linear quadratic regulator (LQR)controller for trajectory following. The trajectories are optimized in terms of timeand formation keeping. Two different controllers are used in the solutions. Oneof which uses cascade PID control, and the other uses a combination of cascadePID control and LQR control. As a way to test our solutions, a scenario is created that can show the utilityof the presented algorithms. The scenario consists of two drone swarms that willtake on different missions executed in the same environment, where the droneswarms will be on a direct collision course with each other. The implementedsolutions should keep the desired formation while smoothly avoiding collisionsand deadlocks. The tests are conducted on real UAVs, using the open sourceflying development platform Crazyflie 2.1 from Bitcraze AB. The resulting trajectories are evaluated in terms of time, path length, formation error, smoothnessand safety.  The obtained results show that generating trajectories from an optimal control problem is superior compared to using APF+leader-follower+CBF/ECBF. However, one major advantage of the last-mentioned algorithms is that decision making is done at every time step making these solutions more robust to disturbancesand changes in the environment.

Den Kern der vorliegenden Arbeit bilden sog. sensorgeführte Roboterbewegungen, d. h. die Nutzung von Informationen externer Sensoren zur Regelung des Roboters. Da gängige Industrierobotersysteme üblicherweise positionsgeregelt sind und seitens der Robotersteuerung lediglich der Zugriff zu den Sollwerten der Lageregelkreise erlaubt wird, kann auch der Regelkreis der sensorgeführten Roboterbewegung nur über den Lageregelkreis geschlossen werden. Aus diesem Grunde werden hier nur positionsbasierte Regelungsansätze verfolgt. Die Kraft-/ Momentregelung gilt als eine der wichtigsten Varianten sensorgeführter Roboterbewegungen. Dementsprechend widmet sich auch ein großer Teil dieser Arbeit dem Thema, mit dem Ziel durch innovative und übersichtliche Regelalgorithmen die Akzeptanz der Kraft-/ Momentregelung in industriellen Produktionsprozessen zu erhöhen. Beginnend mit der eindimensionalen Kraftregelung führt der Weg dabei über Konzepte zur Konturenverfolgung und kraft-/ momentgeregelten Montageaufgaben hin zur Kooperation von Robotern. In einem weiteren Teil wird ein Konzept zur Kollisionsvermeidung zwischen Robotern und Hindernissen präsentiert. Es basiert auf dem Ansatz der virtuellen Potential- bzw. Kraftfelder. Dabei ruft das künstliche Feld eine Bewegung des Roboters hervor, die vom Hindernis weg führt. Um das Feld zu erzeugen, wird die Methode der künstlichen Punktladungen entwickelt. Diese werden auf der Oberfläche eines Hindernisses platziert und generieren dann das virtuelle Kraftfeld. Die Platzierung kann z. B. mithilfe der CAD-Daten des Hindernisses erfolgen. Bei bewegten Objekten müssen alle Ladungspositionen ständig aktualisiert werden. Für Lehr- und Präsentationszwecke ist das sog. inverse Pendel eine oft genutzte Regelstrecke. Sein Aufrichten und Stabilisieren ist auch mit Hilfe eines Industrieroboters möglich. Dazu beschäftigt sich ein Kapitel dieser Arbeit mit Fragen zur Modellbildung der Kombination inverses Pendel und Industrieroboter und mit Regelungskonzepten für das Aufschwingen und Balancieren. Letztendlichen wird in diesem Zusammenhang noch ein Visual-Servoing System präsentiert, dass den Neigungswinkel des Pendels mit einer Kamera bestimmt. Alle hier vorgestellten Konzepte und Algorithmen werden Anhand von praktischen Experimenten verifiziert. / This work deals with so-called sensor guided robot motions, which means using the data of external sensors to control the robot. The control loop of the sensor guided robot motion can be only closed around the position control loop, because industrial robot systems usually work position controlled and only access to the desired positions is enabled. For this reason here only position based control approaches are regarded. Force/torque control is a very important type of sensor guided robot motions. According to this, a good portion of this work deals with the subject of force/torque control. Thus, the acceptance of force/torque control in industrial production processes should be increased, by using innovative and clear control algorithms. For this purpose force control in one degree of freedom, contour-following, force/torque controlled assembling tasks and the cooperation between robots are discussed here in different chapters. Thereafter, a concept to collision avoidance between robots and obstacles is presented. It uses the approach of virtual potential/force fields. In this case the artificial field induces a robot motion away from the obstacle. The method of artificial charges is developed to generate this field. For this purpose virtual charges are placed on the surface of the obstacles. Placing of the charges can be performed using e.g. CAD data of the obstacles. Having moving obstacles charge positions must be updated continuously. The inverted pendulum is commonly used teaching students in control theory. The swinging up and the stabilization of the pendulum also can be performed by an industrial robot. One chapter of this work deals with modelling of the robot mounted inverted pendulum and control algorithms for its swinging up and its stabilization. Finally, in combination with the inverted pendulum a visual-servoing system is presented, which measures the pendulum inclination angle by camera. All concepts introduced in this work are verified by practical experiments.