Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 80-101 |
| Number of pages | 22 |
| Journal | Journal of Computational Science |
| Volume | 34 |
| DOIs | |
| Publication status | Published - May 2019 |
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Keywords
- Self-organisation
- Particle swarm
- Fire spread modelling
- Swarm robotics
- Autonomous unmanned aerial vehicles
Cite this
Self-Organising Swarms of Firefighting Drones: Harnessing the Power of Collective Intelligence in Decentralised Multi-Robot Systems. / Innocente, Mauro; Grasso, Paolo.
In: Journal of Computational Science, Vol. 34, 05.2019, p. 80-101.Research output: Contribution to journal › Article
}
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 -