Minimalist exploration strategies for robot swarms at the edge of chaos

TL;DR

This paper investigates how chaotic and edge-of-chaos random walk strategies, implemented via Boolean Networks, can enhance exploration efficiency in robot swarms, especially under constraints like limited sensors and variability among robots.

Contribution

It introduces a novel approach using Random Boolean Networks for chaotic exploration in robot swarms and demonstrates their effectiveness over traditional methods.

Findings

Chaotic Boolean Network controllers outperform Lévy-modulated random walks.

Chaotic dynamics maximize exploration effectiveness.

Evolutionary Robotics can optimize Boolean Network behaviors while preserving chaos.

Abstract

Effective exploration abilities are fundamental for robot swarms, especially when small, inexpensive robots are employed (e.g., micro- or nano-robots). Random walks are often the only viable choice if robots are too constrained regarding sensors and computation to implement state-of-the-art solutions. However, identifying the best random walk parameterisation may not be trivial. Additionally, variability among robots in terms of motion abilities-a very common condition when precise calibration is not possible-introduces the need for flexible solutions. This study explores how random walks that present chaotic or edge-of-chaos dynamics can be generated. We also evaluate their effectiveness for a simple exploration task performed by a swarm of simulated Kilobots. First, we show how Random Boolean Networks can be used as controllers for the Kilobots, achieving a significant performance improvement compared to the best parameterisation of a L\'evy-modulated Correlated Random Walk. Second, we demonstrate how chaotic dynamics are beneficial to maximise exploration effectiveness. Finally, we demonstrate how the exploration behavior produced by Boolean Networks can be optimized through an Evolutionary Robotics approach while maintaining the chaotic dynamics of the networks.