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Decentralized Multi-target Exploration and Connectivity Maintenance with a Multi-robot System

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Nestmeyer,  T
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Franchi,  A
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Robuffo Giordano,  P
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Nestmeyer, T., Franchi, A., Bülthoff, H., & Robuffo Giordano, P. (2015). Decentralized Multi-target Exploration and Connectivity Maintenance with a Multi-robot System. In RSS 2015 Workshop: Reviewing the review process (pp. 1-8).


Cite as: http://hdl.handle.net/11858/00-001M-0000-002A-4511-D
Abstract
This paper presents a novel distributed control strategy that enables multi-target exploration while ensuring a time-varying connected topology in both 2D and 3D cluttered environments. Flexible continuous connectivity is guaranteed by gradient descent on a monotonic potential function applied on the algebraic connectivity (or Fiedler eigenvalue) of a generalized interaction graph. Limited range, line-of-sight visibility, and collision avoidance are taken into account simultaneously by weighting of the graph Laplacian. Completeness of the multi-target visiting algorithm is guaranteed by using a decentralized adaptive leader selection strategy and a suitable scaling of the exploration force based on the direction alignment between exploration and connectivity force and the traveling efficiency of the current leader. Extensive MonteCarlo simulations with a group of several quadrotor UAVs show the practicability, scalability and effectiveness of the proposed method.