PheroCom: Decentralised and asynchronous swarm robotics coordination based on virtual pheromone and vibroacoustic communication

TL;DR

This paper introduces PheroCom, a decentralized, asynchronous swarm robotics coordination model using virtual pheromones and vibroacoustic communication, validated through simulations demonstrating effective surveillance task performance.

Contribution

It presents a novel pheromone modeling approach enabling decentralized and asynchronous swarm coordination without central control.

Findings

Effective swarm coordination demonstrated in various environments.

Robots successfully performed surveillance tasks with high efficiency.

Model supports decentralization and asynchronous decision-making.

Abstract

Representation and control of the dynamics of stigmergic substances used by bio-inspired approaches is a challenge when applied to robotics. In order to overcome this challenge, this work proposes a model to coordinate swarms of robots based on the virtualisation and control of these substances in a local scope. The model presents a new pheromone modelling, which enables the decentralisation and asynchronicity of navigation decisions. Each robot maintains an independent virtual pheromone map, which is continuously updated with the robot's deposits and pheromone evaporation. Moreover, the individual pheromone map is also updated by aggregating information from other robots that are exploring nearby areas. Thus, individual and independent maps replace the need of a centralising agent that controls and distributes the pheromone information, which is not always practicable. Pheromone information propagation is inspired by ants' vibroacoustic communication, which, in turn, is characterised as an indirect communication through a type of gossip protocol. The proposed model was evaluated through an agent simulation software, implemented by the authors, and in the Webots platform. Experiments were carried out to validate the model in different environments, with different shapes and sizes, as well as varying the number of robots. The analysis of the results has shown that the model was able to perform the coordination of the swarm, and the robots have exhibited an expressive performance executing the surveillance task.