The Playful Machine - Theoretical Foundation and Practical Realization of Self-Organizing Robots

Self-organization in the sense used in natural sciences means the spontaneous creation of patterns in space and/or time in dissipative systems consisting of many individual components. Central in this context is the notion of emergence meaning the spontaneous creation of structures or functions that are not directly explainable from the interactions between the constituents of the system. This chapter presents at first several examples of prominent self-organizing systems in nature with the aim to identify the underlying mechanisms. While self-organization in natural systems shares a common scheme, self-organization in machines is more diversified. An exception is swarm robotics because of the similarity to a system of many constituents interacting via local laws as encountered in physics (particles), biology (insects), and technology (robots). This chapter aims at providing a common basis for a translation of self-organization effects to single robots considered as complex physical systems consisting of many constituents that are constraining each other in an intensive manner. Self-organization is a ubiquitous phenomenon observed in many complex systems in the fields of physics, chemistry, computer science, economics, and biology. While synergetics provides a general theoretical framework for the wide field of such phenomena [60, 62, 182] there are many examples that are controversial and difficult to fit into a quantitative explanatory system [139]. This chapter will not try to shed new light onto this research field but instead work out by way of examples the most prominent features and underlying mechanisms of self-organizing systems in nature and machines. Once identified, these mechanisms may serve as a guiding principle for autonomous robot development. After identifying one cornerstone—self-amplification—by the example of spontaneous magnetization (Sect. 2.1.1), we are going to study systems as different as convection patterns (Sect. 2.1.2), reaction diffusion systems (Sect. 2.1.3), and Turing patterns (Sect. 2.1.2) in order to understand the role of symmetries and spontaneous symmetry breaking, the second corner stone of self-organization. After giving examples from biology (Sect. 2.1.4) that may help to understand the comprehensive nature of the general scheme, we will consider swarm robotics (Sect. 2.2.1) because of its close relationship to biology. Further fields of robotic research are related to self-organization only in a more distant way. We discuss in particular the research in artificial evolution in Sect. 2.2.2.