HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Swarm Approaches for the Patrolling Problem, Information Propagation vs. Pheromone Evaporation

Hoang-Nam Chu Arnaud Glad Olivier Simonin 1 Francois Sempe Alexis Drogoul François Charpillet 1
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : This paper deals with the multi-agent patrolling problem in unknown environment using two collective approaches exploiting environmental dynamics. After specifying criteria of performances, we define a first algorithm based only on the evaporation of a pheromone dropped by reactive agents (EVAP). Then we present the model CLInG 7 proposed in 2003 which introduces the diffusion of the idleness of areas to visit. We systematically compare by simulations the performances of these two models on growing-complexity environments. The analysis is supplemented by a comparison with the theoretical optimum performances, allowing to identify topologies for which methods are the most adapted.
Document type :
Conference papers
Complete list of metadata
https://hal.inria.fr/inria-00172335
Contributor : Olivier Simonin Connect in order to contact the contributor
Submitted on : Friday, September 14, 2007 - 7:19:10 PM
Last modification on : Wednesday, February 2, 2022 - 3:53:48 PM

Identifiers

  • HAL Id : inria-00172335, version 1

Collections

INRIA | CNRS | INRIA2 | LORIA | UNIV-LORRAINE

Citation

Hoang-Nam Chu, Arnaud Glad, Olivier Simonin, Francois Sempe, Alexis Drogoul, et al.. Swarm Approaches for the Patrolling Problem, Information Propagation vs. Pheromone Evaporation. 19th IEEE International Conference on Tools with Artificial Intelligence - ICTAI 2007, Oct 2007, Patras, Greece. ⟨inria-00172335⟩

Export

BibTeX TEI DC DCterms EndNote Datacite

Share

Metrics

Record views

121

 

Contact                    Legal Notice                    Personal Data