Evolutionary swarm formation: From simulations to real world robots

Abstract

Swarms of autonomous robots have become an interesting alternative for space and aerospace applications due to their versatility, robustness, and self-organising capability. Some of those applications, such as asteroid observation, convoy escort, and counter-drone systems, rely on stable formations achieved around a central point of interest. However, the use of different numbers of robots and the existence of a wide range of initial conditions contribute to make it a challenging problem. We propose in this research work a novel approach for self-organising a swarm of autonomous robots where the members’ movements depend only on their relative position (range and bearing) obtained from their respective radio beacons. An optimisation approach based on an evolutionary algorithm is proposed to calculate the optimal swarm’s parameters, e.g. speed and attracting/repelling forces, to achieve robust formations under different initial conditions and failure rates. Experiments are conducted using realistic simulations of six case studies featuring three, five, ten, fifteen, twenty, and thirty robots. The best valued configurations were tested on 420 scenarios showing that our proposal is robust since it has always achieved the desired circular formation. Finally, we have used real E-Puck2 robots to validate the swarm’s capability of self-organising around a central point of interest as well as its resilience to robot failure, obtaining successful circular formations in all the experiments.