Applying the canonical distributed Embodied Evolution algorithm in a collective indoor navigation task

Abstract

The automatic design of control systems for multi-robot teams that operate in real time is not affordable with traditional evolutionary algorithms mainly due to the huge computational requirements they imply. Embodied Evolution (EE) is an evolutionary paradigm that aims to address this problem through the embodiment of the individuals that make up the population in the physical robots. The interest for this type of evolutionary approach has been increasing steadily, leading to different algorithms and variations adapted to solve very specific practical cases. In a previous work, the authors started the implementation of a standard canonical EE algorithm that captures the more general principles of this paradigm and that can be applied to any distributed optimization problem. This canonical algorithm has been characterized already over a set of theoretical fitness landscapes corresponding to representative examples of the basic casuistry found in collective tasks. The current paper goes one step ahead in this research line, and the canonical algorithm is applied here in a collective navigation task in which a fleet of Micro Aerial Vehicles (MAVs) has to gather red rocks in an indoor scenario. The objective is to confirm that the characterization conclusions are generalizable to a practical case and to show that the canonical algorithm can be configured to operate as a specific algorithm easily.