Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots

TL;DR

This paper demonstrates the successful transfer of evolved neural network controllers from simulation to real aquatic surface robot swarms, achieving scalable, flexible, and robust collective behaviors in uncontrolled environments.

Contribution

First to validate that evolved controllers in simulation can effectively operate on real aquatic robots in uncontrolled settings.

Findings

Controllers transfer successfully from simulation to real robots

Controllers exhibit key swarm intelligence properties

Swarm performs environmental monitoring task

Abstract

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.