Η παρούσα διπλωματική εργασία ασχολείται με τον συνεργατικό έλεγχο μιας ομάδας αυτόνομων ρομπότ. Θεωρώντας ότι μόνο ένα ρομπότ έχει πλήρη γνώση για το μονοπάτι που επιθυμούμε να ακολουθήσουν τα ρομπότ, θα πρέπει τα υπόλοιπα παίρνοντας μετρήσεις από διάφορους αισθητήρες και ανταλλάσοντας πληροφορίες μεταξύ τους, να σχεδιάζουν και να ακολουθούν τέτοιες τροχιές ώστε να πετύχουν τον στόχο τους. Από τη στιγμή που ένα μοναδικό ρομπότ γνωρίζει το επιθυμητό μονοπάτι, πρόκειται για ένα leader-follower σχηματισμό όπου κάθε ρομπότ καλείται να ακολουθήσει αυτό που προηγείται. Προκειμένου τα ρομπότ να μπορούν να ακολουθήσουν μία τροχιά αναπτύχθηκε σε περιβάλλον LabVIEW ένας ελεγκτής παρακολούθησης τροχιάς. Για την ανταλλαγή πληροφοριών και μηνυμάτων συντονισμού μεταξύ των ρομπότ, αναπτύχθηκε ασύρματο δίκτυο ZigBee. Το πρόβλημα γνώσης σχετικά με το που βρίσκεται ο leader κάθε ρομπότ λύθηκε με τη χρήση αλγορίθμων που επεξεργάζονται τις μετρήσεις των ενσωματωμένων Sonar. Επίσης το πρόβλημα γνώσης του πραγματικού προσανατολισμού κάθε ρομπότ, αντιμετωπίστηκε με την κατασκευή ενός ψηφιακού μαγνητομέτρου. Για κάθε ρομπότ ξεχωριστά, αναπτύχθηκε ο συνεργατικός αλγόριθμος ο οποίος εξασφαλίζει ότι η ομάδα θα πετυχαίνει τον στόχο που έχει αρχικά τεθεί. Τέλος παρουσιάζονται πειραματικά αποτελέσματα για ομάδες δύο και τριών ρομπότ. / The present thesis elaborates on the cooperative control of mobile robots. Assuming that one robot has complete knowledge of the desired path the robotic platoon must follow, a coordination control scheme must be created, based on sensor measurements and platoon communication, that generates desired trajectories for each of the members of the platoon. From the moment a single robot has complete knowledge of the path, the coordination scheme is based on leader-follower formation control. A trajectory tracking controller was developed in LabVIEW, while a ZigBee based wireless network was implemented for the platoon communication. To find the relative position of the leader for each robot, a sonar based localization algorithm was created, with position measurements through the robot’s encoders and orientation given from a magnetometer. For each robot seperately, the coordination algorithm was developed, that ensures that the platoon will achieve the original goal.

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

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.

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.