Multi-robot online sensing strategies for the construction of communication maps

This paper tackles the problem of constructing a communication map of a known environment using multiple robots. A communication map encodes information on whether two robots can communicate when they are at two arbitrary locations and plays a fundamental role for a multi-robot system deployment to...

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Bibliographic Details
Published in:Autonomous robots 2020-03, Vol.44 (3-4), p.299-319
Main Authors: Quattrini Li, Alberto, Penumarthi, Phani Krishna, Banfi, Jacopo, Basilico, Nicola, O’Kane, Jason M., Rekleitis, Ioannis, Nelakuditi, Srihari, Amigoni, Francesco
Format: Article
Language:English
Subjects:
Artificial Intelligence
Communication
Computer Imaging
Computer simulation
Construction methods
Control
Data collection
Engineering
Environmental monitoring
Finite element method
Gaussian process
Mechatronics
Multiple robots
Pattern Recognition and Graphics
Performance enhancement
Radio equipment
Robotics
Robotics and Automation
Robots
Special Issue on Multi-Robot and Multi-Agent Systems
Vision
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Summary:This paper tackles the problem of constructing a communication map of a known environment using multiple robots. A communication map encodes information on whether two robots can communicate when they are at two arbitrary locations and plays a fundamental role for a multi-robot system deployment to reliably and effectively achieve a variety of tasks, such as environmental monitoring and exploration. Previous work on communication map building typically considered only scenarios with a fixed base station and designed offline methods, which did not exploit data collected online by the robots. This paper proposes Gaussian Process-based online methods to efficiently build a communication map with multiple robots. Such robots form a mesh network, where there is no fixed base station. Specifically, we provide two leader-follower online sensing strategies to coordinate and guide the robots while collecting data. Furthermore, we improve the performance and computational efficiency by exploiting prior communication models that can be built from the physical map of the environment. Extensive experimental results in simulation and with a team of TurtleBot 2 platforms validate the approach.
ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-019-09862-3