<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>

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.

The aim of the master's thesis is the robotic table football device modification and the design of game strategies. The thesis was extended by the design of technology for the robotic table football safety and the design of the Human Machine Interface (HMI). In the introduction so far developed solutions and presentation of the B&R Automation company are described. The following chapters describe mechanical solution modification, electrical wiring diagram design, creating simulation in the MATLAB development environment, resolving security issues and the subsequent application of a solution into the robotic table football. The conclusion of the thesis is devoted to the visualization interface design.