Self-Organising Swarms of Firefighting Drones: Harnessing the Power of Collective Intelligence in Decentralised Multi-Robot Systems

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Swarm Intelligence (SI) is concerned with the collective behaviour that emerges from decentralised self-organising systems, whilst Swarm Robotics (SR) is an approach to the self-coordination of large numbers of simple robots which emerged as the application of SI to multi-robot systems. Given the increasing severity and frequency of occurrence of wildfires and the hazardous nature of fighting their propagation, the use of disposable inexpensive robots in place of humans is of special interest. This paper demonstrates the feasibility and potential of employing SR to fight fires autonomously, with a focus on the self-coordination mechanisms for the desired firefighting behaviour to emerge. Thus, an efficient physics-based model of fire propagation and a self-organisation algorithm for swarms of firefighting drones are developed and coupled, with the collaborative behaviour based on a particle swarm algorithm adapted to individuals operating within physical dynamic environments of high severity and frequency of change. Numerical experiments demonstrate that the proposed self-organising system is effective, scalable and fault-tolerant, comprising a promising approach to dealing with the suppression of wildfires – one of the world’s most pressing challenges of our time.
Original languageEnglish
Pages (from-to)80-101
Number of pages22
JournalJournal of Computational Science
Volume34
DOIs
Publication statusPublished - May 2019

Fingerprint

Swarm Robotics
Collective Intelligence
Self-organizing Systems
Multi-robot Systems
Swarm Intelligence
Self-organizing
Swarm
Decentralized
Robot
Robots
Propagation
Particle Swarm Algorithm
Collective Behavior
Fires
Robotics
Self-organization
Dynamic Environment
Fault-tolerant
Demonstrate
Numerical Experiment

Keywords

  • Self-organisation
  • Particle swarm
  • Fire spread modelling
  • Swarm robotics
  • Autonomous unmanned aerial vehicles

Cite this

@article{da21595a162f4190b249c4d88071f2ed,
title = "Self-Organising Swarms of Firefighting Drones: Harnessing the Power of Collective Intelligence in Decentralised Multi-Robot Systems",
abstract = "Swarm Intelligence (SI) is concerned with the collective behaviour that emerges from decentralised self-organising systems, whilst Swarm Robotics (SR) is an approach to the self-coordination of large numbers of simple robots which emerged as the application of SI to multi-robot systems. Given the increasing severity and frequency of occurrence of wildfires and the hazardous nature of fighting their propagation, the use of disposable inexpensive robots in place of humans is of special interest. This paper demonstrates the feasibility and potential of employing SR to fight fires autonomously, with a focus on the self-coordination mechanisms for the desired firefighting behaviour to emerge. Thus, an efficient physics-based model of fire propagation and a self-organisation algorithm for swarms of firefighting drones are developed and coupled, with the collaborative behaviour based on a particle swarm algorithm adapted to individuals operating within physical dynamic environments of high severity and frequency of change. Numerical experiments demonstrate that the proposed self-organising system is effective, scalable and fault-tolerant, comprising a promising approach to dealing with the suppression of wildfires – one of the world’s most pressing challenges of our time.",
keywords = "Self-organisation, Particle swarm, Fire spread modelling, Swarm robotics, Autonomous unmanned aerial vehicles",
author = "Mauro Innocente and Paolo Grasso",
year = "2019",
month = "5",
doi = "10.1016/j.jocs.2019.04.009",
language = "English",
volume = "34",
pages = "80--101",
journal = "Journal of Computational Science",
issn = "1877-7503",
publisher = "Elsevier",

}

TY - JOUR

T1 - Self-Organising Swarms of Firefighting Drones: Harnessing the Power of Collective Intelligence in Decentralised Multi-Robot Systems

AU - Innocente, Mauro

AU - Grasso, Paolo

PY - 2019/5

Y1 - 2019/5

N2 - Swarm Intelligence (SI) is concerned with the collective behaviour that emerges from decentralised self-organising systems, whilst Swarm Robotics (SR) is an approach to the self-coordination of large numbers of simple robots which emerged as the application of SI to multi-robot systems. Given the increasing severity and frequency of occurrence of wildfires and the hazardous nature of fighting their propagation, the use of disposable inexpensive robots in place of humans is of special interest. This paper demonstrates the feasibility and potential of employing SR to fight fires autonomously, with a focus on the self-coordination mechanisms for the desired firefighting behaviour to emerge. Thus, an efficient physics-based model of fire propagation and a self-organisation algorithm for swarms of firefighting drones are developed and coupled, with the collaborative behaviour based on a particle swarm algorithm adapted to individuals operating within physical dynamic environments of high severity and frequency of change. Numerical experiments demonstrate that the proposed self-organising system is effective, scalable and fault-tolerant, comprising a promising approach to dealing with the suppression of wildfires – one of the world’s most pressing challenges of our time.

AB - Swarm Intelligence (SI) is concerned with the collective behaviour that emerges from decentralised self-organising systems, whilst Swarm Robotics (SR) is an approach to the self-coordination of large numbers of simple robots which emerged as the application of SI to multi-robot systems. Given the increasing severity and frequency of occurrence of wildfires and the hazardous nature of fighting their propagation, the use of disposable inexpensive robots in place of humans is of special interest. This paper demonstrates the feasibility and potential of employing SR to fight fires autonomously, with a focus on the self-coordination mechanisms for the desired firefighting behaviour to emerge. Thus, an efficient physics-based model of fire propagation and a self-organisation algorithm for swarms of firefighting drones are developed and coupled, with the collaborative behaviour based on a particle swarm algorithm adapted to individuals operating within physical dynamic environments of high severity and frequency of change. Numerical experiments demonstrate that the proposed self-organising system is effective, scalable and fault-tolerant, comprising a promising approach to dealing with the suppression of wildfires – one of the world’s most pressing challenges of our time.

KW - Self-organisation

KW - Particle swarm

KW - Fire spread modelling

KW - Swarm robotics

KW - Autonomous unmanned aerial vehicles

UR - http://www.scopus.com/inward/record.url?scp=85067264480&partnerID=8YFLogxK

U2 - 10.1016/j.jocs.2019.04.009

DO - 10.1016/j.jocs.2019.04.009

M3 - Article

VL - 34

SP - 80

EP - 101

JO - Journal of Computational Science

JF - Journal of Computational Science

SN - 1877-7503

ER -