Abstract
Multirobot systems for exploring initially unknown environments are often subject to communication constraints, due to the limited range of their transmission devices and to mission requirements. In order to make decisions about where the robots should move, a communication map that encodes knowledge of the locations from which communication is possible is usually employed. Typically, simple line of sight or circle communication models (that are rather independent of the specific environment in which the exploration is carried out) are considered. In this paper, we make a step forward and present a multirobot system that learns and updates a communication map during the exploration mission. In particular, we propose methods to incrementally update vertices, corresponding to the locations visited by robots, and edges, corresponding to communication links, of a graph according to the measured power of radio-frequency signals and to the predictions made by a model based on Gaussian Processes. Experimental results obtained in simulation show that the proposed methods build and update rich communication maps specific for the environments being visited and that the availability of these maps can improve the exploration performance.
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Amigoni, F., Banfi, J., Basilico, N., Rekleitis, I., Quattrini Li, A. (2019). Online Update of Communication Maps for Exploring Multirobot Systems Under Connectivity Constraints. In: Correll, N., Schwager, M., Otte, M. (eds) Distributed Autonomous Robotic Systems. Springer Proceedings in Advanced Robotics, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-030-05816-6_36
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DOI: https://doi.org/10.1007/978-3-030-05816-6_36
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