Flocking-Segregative Swarming Behaviors using Gibbs Random Fields

TL;DR

This paper introduces a novel algorithm for heterogeneous robot swarms that achieves simultaneous flocking and segregation behaviors using local sensing, modeled through Gibbs Random Fields, without relying on global communication.

Contribution

It is the first to combine flocking and segregation behaviors in a swarm using only local sensing and Gibbs Random Fields, without global information or communication.

Findings

Successful simulation results demonstrating behavior emergence

Proof-of-concept experiments with real robots validating the approach

Performance comparison showing advantages over existing methods

Abstract

This paper presents a novel approach that allows a swarm of heterogeneous robots to produce simultaneously segregative and flocking behaviors using only local sensing. These behaviors have been widely studied in swarm robotics and their combination allows the execution of several complex tasks, ranging from surveillance and reconnaissance, to search and rescue, to transport, and to foraging. Although there are several works in the literature proposing different strategies to achieve these behaviors, to the best of our knowledge, this paper is the first to propose an algorithm that emerges simultaneously behaviors and do not rely on global information or communication. Our approach consists of modeling the swarm as a Gibbs Random Field (GRF) and using appropriate potential functions to reach segregation, cohesion and consensus on the velocity of the swarm. Simulations and proof-of-concept experiments using real robots are presented to evaluate the performance of our methodology in comparison to some of the state-of-the-art works that tackle segregative behaviors.