Thesis(M.S.)--Auburn University, 2005. / Abstract. Vita. Includes program. Includes bibliographic references.

Thesis--Auburn University, 2009. / Abstract. Vita. Includes bibliographical references (p. 45-46).

Thesis(M.S.)--Auburn University, 2006. / Abstract. Vita. Includes bibliographic references.

<p>This dissertation evaluates evolutionary methods for evolving cooperative teams of robots. Cooperative robotics is a challenging research area in the field of artificial intelligence. Individual and autonomous robots may by cooperation enhance their performance compared to what they can achieve separately. The challenge of cooperative robotics is that performance relies on interactions between robots. The interactions are not always fully understood, which makes the designing process of hardware and software systems complex. Robotic soccer, such as the RoboCup competitions, offers an unpredictable dynamical environment for competing robot teams that encourages research of these complexities. Instead of trying to solve these problems by designing and implement the behavior, the robots can learn how to behave by evolutionary methods. For this reason, this dissertation evaluates evolution of neural controllers for a team of two robots in a competitive soccer environment. The idea is that evolutionary methods may be a solution to the complexities of creating cooperative robots. The methods used in the experiments are influenced by research of evolutionary algorithms with single autonomous robots and on robotic soccer. The results show that robot teams can evolve to a form of cooperative behavior with simple reactive behavior by relying on self-adaptation with little supervision and human interference.</p>

Thesis (M.S.)--Auburn University, 2008. / Abstract. Vita. Includes bibliographical references (p. 41-44).

Thesis--Auburn University, 2009. / Abstract. Vita. Includes bibliographic references (p.44-49).

L'utilisation d'équipes de robots a pris de l'ampleur ces dernières années. Cela est dû aux avantages que peut offrir une équipe de robot par rapport à un robot seul pour la réalisation d'une même tâche. Cela s'explique aussi par le fait que ce type de plates-formes deviennent de plus en plus abordables et fiables. Ainsi, l'utilisation d'une équipe de véhicules aériens devient une alternative viable. Cette thèse se concentre sur le problème du déploiement d'une équipe de Micro-Véhicules Aériens (MAV) pour effectuer des missions de surveillance sur un terrain inconnu de morphologie arbitraire. Puisque la morphologie du terrain est inconnue et peut être complexe et non-convexe, les algorithmes standards ne sont pas applicables au problème particulier traité dans cette thèse. Pour y remédier, une nouvelle approche basée sur un algorithme d'optimisation cognitive et adaptatif (CAO) est proposée et évaluée. Une propriété fondamentale de cette approche est qu'elle partage les mêmes caractéristiques de convergence que les algorithmes de descente de gradient avec contraintes qui exigent une connaissance parfaite de la morphologie du terrain pour optimiser la couverture. Il est également proposé une formulation différente du problème afin d'obtenir une solution distribuée, ce qui nous permet de surmonter les inconvénients d'une approche centralisée et d'envisager également des capacités de communication limitées. De rigoureux arguments mathématiques et des simulations étendues établissent que l'approche proposée fournit une méthodologie évolutive et efficace qui intègre toutes les contraintes physiques particulières et est capable de guider les robots vers un arrangement qui optimise localement la surveillance. Finalement, la méthode proposée est mise en œuvre sur une équipe de MAV réels pour réaliser la surveillance d'un environnement extérieur complexe. / The use of multi-robot teams has gained a lot of attention in recent years. This is due to the extended capabilities that the teams offer compared to the use of a single robot for the same task. Moreover, as these platforms become more and more affordable and robust, the use of teams of aerial vehicles is becoming a viable alternative. This thesis focuses on the problem of deploying a swarm of Micro Aerial Vehicles (MAV) to perform surveillance coverage missions over an unknown terrain of arbitrary morphology. Since the terrain's morphology is unknown and it can be quite complex and non-convex, standard algorithms are not applicable to the particular problem treated in this thesis. To overcome this, a new approach based on the Cognitive-based Adaptive Optimization (CAO) algorithm is proposed and evaluated. A fundamental property of this approach is that it shares the same convergence characteristics as those of constrained gradient-descent algorithms, which require perfect knowledge of the terrain's morphology to optimize coverage. In addition, it is also proposed a different formulation of the problem in order to obtain a distributed solution, which allows us to overcome the drawbacks of a centralized approach and to consider also limited communication capabilities. Rigorous mathematical arguments and extensive simulations establish that the proposed approach provides a scalable and efficient methodology that incorporates any particular physical constraints and limitations able to navigate the robots to an arrangement that (locally) optimizes the surveillance coverage. The proposed method is finally implemented in a real swarm of MAVs to carry out surveillance coverage in an outdoor complex area.

This dissertation evaluates evolutionary methods for evolving cooperative teams of robots. Cooperative robotics is a challenging research area in the field of artificial intelligence. Individual and autonomous robots may by cooperation enhance their performance compared to what they can achieve separately. The challenge of cooperative robotics is that performance relies on interactions between robots. The interactions are not always fully understood, which makes the designing process of hardware and software systems complex. Robotic soccer, such as the RoboCup competitions, offers an unpredictable dynamical environment for competing robot teams that encourages research of these complexities. Instead of trying to solve these problems by designing and implement the behavior, the robots can learn how to behave by evolutionary methods. For this reason, this dissertation evaluates evolution of neural controllers for a team of two robots in a competitive soccer environment. The idea is that evolutionary methods may be a solution to the complexities of creating cooperative robots. The methods used in the experiments are influenced by research of evolutionary algorithms with single autonomous robots and on robotic soccer. The results show that robot teams can evolve to a form of cooperative behavior with simple reactive behavior by relying on self-adaptation with little supervision and human interference.

Imagine the following situation. You give your favorite robot, named Pippi, the task to fetch a heavy parcel that just arrived at your front door. While pushing the parcel back to you, she must travel through a door. Unfortunately, the parcel she is pushing is blocking her camera, giving her a hard time to see the door. If she cannot see the door, she cannot safely push the parcel through it. What would you as a human do in a similar situation? Most probably you would ask someone for help, someone to guide you through the door, as we ask for help when we need to park our car in a tight parking spot. Why not let the robots do the same? Why not let robots help each other? Luckily for Pippi, there is another robot, named Emil, vacuum cleaning the floor in the same room. Since Emil has a video camera and can view both Pippi and the door at the same time, he can estimate Pippi's position relative to the door and use this information to guide Pippi through the door by wireless communication. In that way he can enable Pippi to deliver the parcel to you. The goal of this thesis is to endow robots with the ability to help each other in a similar way. More specifically, we consider distributed robot systems in which: (1) each robot includes modular functionalities for sensing, acting and/or processing; and (2) robots can help each other by offering those functionalities. A functional configuration of such a system is any way to allocate and connect functionalities configuration among the robots. An interesting feature of a system of this type is the possibility to use different functional configurations to make the same set of robots perform different tasks, or to perform the same task under different conditions. In the above example, Emil is offering a perceptual functionality to Pippi. In a different situation, Emil could offer his motion functionality to help Pippi push a heavier parcel. In this thesis, we propose an approach to automatically generate, at run time, a functional configuration of a distributed robot system to perform a given task in a given environment, and to dynamically change this configuration in response to failures. Our approach is based on artificial intelligence planning techniques, and it is provably sound, complete and optimal. In order to handle tasks that require more than one step (i.e., one configuration) to be accomplished, we also show how methods for automatic configuration can be integrated with methods for task planning to produce a complete plan were each step is a configuration. For the scenario above, generating a complete plan before the execution starts enables Pippi to know before hand if she will be able to get the parcel or not. We also propose an approach to merge configurations, which enables concurrent execution of configurations, thus reducing execution time. We demonstrate the applicability of our approach on a specific type of distributed robot system, called Peis-Ecology, and show experiments in which configurations and sequences of configurations are automatically generated and executed on real robots. Further, we give an experiment where merged configurations are created and executed on simulated robots.

<p>The interest for cooperative robots has increased considerably in recent years and one of the research issues within this domain is how to evolve heterogeneity in a team. The research today is however either focusing on diversity in hardware (e.g. sensory system) or diversity of behaviour. This dissertation extends this research and presents experiments that attempts to 'co-evolve' heterogeneity at both the hardware level and the behavioural level. The results show that the team behaviour evolved depends on the complexity of the task where adding constraints or increasing the difficulty of the problem lead to better team behaviour.</p><p>Our belief was that the performance of the team should benefit from using robots that has been evolved at the hardware level together with the behavioural level. This, however, could not be proved to be true, but the idea that these two should be kept together in order to evolve heterogeneity in a team is still believed.</p>