Adaptive-based, Scalable Design for Autonomous Multi-Robot Surveillance

Abstract : In this paper the problem of positioning a team of mobile robots for a surveillance task in a non-convex environment with obstacles is considered. The robots are equipped with global positioning capabilities (for instance they are equipped with GPS) and visual sensors able to monitor the surrounding environment. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. Due to the non-convex nature of the problem, an analytical solution cannot be obtained. The proposed method is based on a new cognitive-based, adaptive optimization algorithm (CAO). This method allows getting coordinated and scalable controls to accomplish the task, even when the obstacles are unknown. Extensive simulations are presented to show the efficiency of the proposed approach.
Type de document :
Communication dans un congrès
49th IEEE Conference on Decision and Control (CDC), Dec 2010, Atlanta, United States. 2010
Liste complète des métadonnées

Littérature citée [13 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00579310
Contributeur : Alessandro Renzaglia <>
Soumis le : mercredi 23 mars 2011 - 14:38:14
Dernière modification le : mercredi 11 avril 2018 - 01:55:51
Document(s) archivé(s) le : jeudi 8 novembre 2012 - 12:21:30

Fichier

coverage_cao_cdc.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00579310, version 1

Citation

Alessandro Renzaglia, Lefteris Doitsidis, Agostino Martinelli, Elias Kosmatopoulos. Adaptive-based, Scalable Design for Autonomous Multi-Robot Surveillance. 49th IEEE Conference on Decision and Control (CDC), Dec 2010, Atlanta, United States. 2010. 〈inria-00579310〉

Partager

Métriques

Consultations de la notice

255

Téléchargements de fichiers

116