Agent-based simulations for multi-robot systems exploration of tree-like environments

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, an agent-based simulation model is proposed to explore an unknown tree with arbitrary edge distances by a set of robots which are initially located at the root of the tree and expected to return back to the root after all nodes are explored. The proposed algorithm depends only on local information stored at nodes using a bookkeeping token left at nodes by robots. It is shown that the proposed algorithm with the Earliest Selection Policy (ESP) is superior to using Random Selection Policy (RSP). The proposed agents-based simulation can be used to study general cases of network and tree exploration problems by multiple robots.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages172-176
Number of pages5
ISBN (Electronic)9781538668689
DOIs
Publication statusPublished - Jan 22 2019
Event2018 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2018 - Kandima, Maldives
Duration: Aug 1 2018Aug 5 2018

Publication series

Name2018 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2018

Conference

Conference2018 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2018
CountryMaldives
CityKandima
Period8/1/188/5/18

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Optimization

Cite this

Konak, A., Cabrera-Mora, F., & Kulturel-Konak, S. (2019). Agent-based simulations for multi-robot systems exploration of tree-like environments. In 2018 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2018 (pp. 172-176). [8621759] (2018 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RCAR.2018.8621759