Multirobot Exploration for Building Communication Maps with Prior from Communication Models

Phani Krishna Penumarthi, Alberto {Quattrini Li}, Jacopo Banfi, Nicola Basilico, Francesco Amigoni, Ioannis Rekleitis, Jason M. O'Kane, Srihari Nelakuditi
In International Symposium on Multi-Robot and Multi-Agent Systems 2017. [26 citations]

Abstract

This paper addresses the problem of building a communication map of a known environment using multiple robots. A communication map encodes whether two robots are likely to be able to communicate between two arbitrary locations. Such a communication map is fundamental for reliably deploying a multirobot system to accomplish a variety of tasks, including exploration and environmental monitoring. Previous work proposed offline approaches, which did not utilize data measured by robots. This paper, utilizing Gaussian Processes, proposes methods to efficiently build a communication map with multiple robots. Specifically, the number of measurements used to update the communication map, and the number of possible candidate locations where robots should go are reduced, by exploiting communication models that can be built from the physical map of the environment. This allows robots to take fewer measurements, travel less distance, be more efficient in processing the data online, and get similar accuracy to methods that consider all the locations in the environment. Experiments with a team of TurtleBot 2 platforms validate the approach.

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BibTeX

@inproceedings{PenumarthiMRS2017,
  author       = {Phani Krishna Penumarthi and Alberto {Quattrini Li} and
		 Jacopo Banfi and Nicola Basilico and Francesco Amigoni and
		 Ioannis Rekleitis and Jason M. O'Kane and Srihari
		 Nelakuditi},
  title        = {Multirobot Exploration for Building Communication Maps
		 with Prior from Communication Models},
  booktitle    = {International Symposium on Multi-Robot and Multi-Agent
		 Systems},
  month        = {Dec.},
  pages        = {90--96},
  address      = {Los Angeles, CA, USA},
  year	       = {2017},
  doi	       = {https://doi.org/10.1109/mrs.2017.8250936}
}

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