Multi-Robot 3D Coverage of Unknown Terrains

Abstract : In this paper we study the problem of deploying a team of flying robots to perform surveillance coverage missions over an unknown terrain of arbitrary morphology. In such a mission, the robots should simultaneously accomplish two objectives: firstly, to make sure that the overall terrain is visible by the team and, secondly, that the distance between each point in the terrain and one of the robots is as small as possible. These two objectives should be efficiently fulfilled given the physical constraints and limitations imposed at the particular coverage application (i.e., obstacle avoidance, limited sensor capabilities, etc). As the terrain's morphology is unknown and it can be quite complex and non-convex, standard multi-robot coordination and control algorithms are not applicable to the particular problem treated in this paper. In order to overcome such a problem, a new approach that is based on the Cognitive-based Adaptive Optimization (CAO) algorithm is proposed and evaluated in this paper. Both rigorous mathematical arguments and extensive simulations on unknown terrains establish that the proposed approach provides an efficient methodology that can easily incorporate any particular constraints and quickly and safely navigate the robots to an arrangement that optimizes surveillance coverage.
Type de document :
Communication dans un congrès
50th IEEE Conference on Decision and Control (CDC), Dec 2011, Orlando, United States. 2011
Liste complète des métadonnées

Littérature citée [15 références]  Voir  Masquer  Télécharger
Contributeur : Alessandro Renzaglia <>
Soumis le : mercredi 29 février 2012 - 00:31:13
Dernière modification le : jeudi 11 octobre 2018 - 08:48:02
Document(s) archivé(s) le : lundi 26 novembre 2012 - 10:20:17


Fichiers produits par l'(les) auteur(s)


  • HAL Id : hal-00675082, version 1



Alessandro Renzaglia, Lefteris Doitsidis, Agostino Martinelli, Elias Kosmatopoulos. Multi-Robot 3D Coverage of Unknown Terrains. 50th IEEE Conference on Decision and Control (CDC), Dec 2011, Orlando, United States. 2011. 〈hal-00675082〉



Consultations de la notice


Téléchargements de fichiers