Open-ended evolution as a means to self-organize heterogeneous multi-robot systems in real time

This work deals with the application of multi-robot systems to real tasks and, in particular, their coordination through interaction based control systems. Within this field, the practical solutions that have been implemented in real robots mainly use strongly coordinated architectures and assignment strategies because of reliability and fault tolerance issues when addressing problems in reality. Emergent approaches have also been proposed with limited success, basically due to the unpredictability of the behaviors obtained. Here, an emergent approach, called r-ASiCo, is presented containing a procedure to produce predictable solutions and thus avoiding the typical problems associated with these techniques. The r-ASico algorithm is the real time version of the Asynchronous Situated Co-evolution algorithm (ASiCo), which exploits natural open-ended evolution to generate emergent complex collective behaviors and deals with systems made up of a huge number of elements and nonlinear interactions. The goal of r-ASiCo is to design the global behavior desired for the robot team as a collective entity and allow the emergence of behaviors through the interaction of the team members using social rules they learn to implement. To this end, r-ASiCo manages a series of features that are inherent to natural evolution based methods such as energy exchange and mating selection procedures, together with a technique to guide the evolution towards a design objective, the principled evaluation function selection procedure. Hence, this paper presents the components and operation of r-ASiCo and illustrates its application through a collective cleaning task example. It was implemented using 8 e-puck robots in two different real scenarios and its results complemented with those of a 30 e-puck case. The results show the capabilities of r-ASiCo to create a self-organized and adaptive multi-robot system configuration that is tolerant to environmental changes and to failures within the robot team.

Research highlights
► Multi-robot coordination through an embodied coevolutionary algorithm.
► Open-ended lifelong evolution in real robots.
► Self-organization and adaptation to dynamic environments.
► Heterogeneous populations.