Evolutionary swarm robotics: a theoretical and methodological itinerary from individual neuro-controllers to collective behaviours

In the last decade, swarm robotics gathered much attention in the research community. By drawing inspiration from social insects and other self-organizing systems, it focuses on large robot groups featuring distributed control, adaptation, high robustness, and flexibility. Various reasons lay behind this interest in similar multi-robot systems. Above all, inspiration comes from the observation of social activities, which are based on concepts like division of labor, cooperation, and communication. If societies are organized in such a way in order to be more efficient, then robotic groups also could benefit from similar paradigms

Evolution of Neuro-Controllers for Robots' Alignment using Local Communication

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Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

A general architecture for robotic swarms

Swarms are large groups of simplistic individuals that collectively solve disproportionately complex tasks. Individual swarm agents are limited in perception, mechanically simple, have no global knowledge and are cheap, disposable and fallible. They rely exclusively on local observations and local communications. A swarm has no centralised control. These features are typifed by eusocial insects such as ants and termites, who construct nests, forage and build complex societies comprised of primitive agents. This project created the basis of a general swarm architecture for the control of insect-like robots. The Swarm Architecture is inspired by threshold models of insect behaviour and attempts to capture the salient features of the hive in a closely defined computer program that is hardware agnostic, swarm size indifferent and intended to be applicable to a wide range of swarm tasks. This was achieved by exploiting the inherent limitations of swarm agents. Individual insects were modelled as a machine capable only of perception, locomotion and manipulation. This approximation reduced behaviour primitives to a fixed tractable number and abstracted sensor interpretation. Cooperation was achieved through stigmergy and decisions made via a behaviour threshold model. The Architecture represents an advance on previous robotic swarms in its generality - swarm control software has often been tied to one task and robot configuration. The Architecture's exclusive focus on swarms, sets it apart from existing general cooperative systems, which are not usually explicitly swarm orientated. The Architecture was implemented successfully on both simulated and real-world swarms

Evolution of Neuro-Controllers for Robots\u27 Alignment using Local Communication

In this paper, we use artificial evolution to design homogeneous neural network controller for groups of robots required to align. Aligning refers to the process by which the robots managed to head towards a common arbitrary and autonomously chosen direction starting from initial randomly chosen orientations. The cooperative interactions among robots require local communications that are physically implemented using infrared signalling. We study the performance of the evolved controllers, both in simulation and in reality for different group sizes. In addition, we analyze the most successful communication strategy developed using artificial evolution