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Multi-Robot 3D Coverage of Unknown Terrains

Identifieur interne : 000667 ( Main/Exploration ); précédent : 000666; suivant : 000668

Multi-Robot 3D Coverage of Unknown Terrains

Auteurs : Alessandro Renzaglia [France] ; Lefteris Doitsidis [Grèce] ; Agostino Martinelli [France] ; Elias Kosmatopoulos [Grèce]

Source :

RBID : Hal:hal-00675082

English descriptors

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.

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Le document en format XML

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<div type="abstract" xml:lang="en">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.</div>
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