Specialization analysis of embodied evolution for robotic collective tasks

The objective of this work is to analyze embodied evolution based algorithms in coordinated multi-robot tasks that require specialization. This type of algorithm performs a Darwinian open-ended evolution where the individuals that make up the population are embodied in the physical robots and situated in an environment. The robots interact autonomously in an asynchronous fashion, leading to a complex dynamic system in continuous evolution with dependencies among parameters that make theoretical studies of specialization quite difficult in real cases. Consequently, the aim here is to perform a theoretical analysis of this type of embodied evolution based algorithms, establishing a set of canonical parameters that define their operation. A generic algorithm of this type is designed that allows us to formally study the relevance of the canonical parameters. In this paper this study concentrates on specialization for the construction of heterogeneous robotic teams. The conclusions obtained in the theoretical framework are confirmed in a real multi-robot collective gathering task using one of the many real embodied evolution based algorithms and showing that two canonical parameters are the most relevant in terms of specialization for this type of algorithms. Some insights into how to adjust these canonical parameters in a real problem are provided.

► Studying specialization emergence in multi-robot tasks through evolution.
► Formalization of Embodied Evolution for Collective Tasks (EECT).
► Establishment of a canonical set of parameters and development of a generic EECT.
► Analysis of the relevant parameters in specialization.
► Theoretical conclusions contrasted in a real multi-robot collective gathering task.