Broadening applicability of swarm-robotic foraging through constraint relaxation

John Harwell, Maria L Gini

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

6 Scopus citations

Abstract

Swarm robotics (SR) offers promising solutions to real-world problems that can be modeled as foraging tasks, e.g. disaster/trash cleanup or object gathering for construction. Yet current SR foraging approaches make limiting assumptions that restrict their applicability to selected real-world environments. We propose an improved self-organized task allocation method based on task partitioning that removes restrictions such as: (1) a priori knowledge of foraging environment, and (2) strict limitations on intermediate drop/pickup site behavior. With experiments in simulation, we show that under the proposed constraint relaxation, our approach still provides performance increases when compared to an unpartitioned strategy within some combinations of swarm sizes, robot capabilities, and environmental conditions. This work broadens the applicability of SR foraging approaches, showing that they can be effective under ideal conditions while continuing to perform robustly in more volatile/challenging environments.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2018
EditorsNan Ye, Hanna Kurniawati, Bruce MacDonald, Evan Drumwright, Thierry Fraichard
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages116-122
Number of pages7
ISBN (Electronic)9781538659748
DOIs
StatePublished - Jun 8 2018
Event2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2018 - Brisbane, Australia
Duration: May 16 2018May 19 2018

Publication series

Name2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2018

Other

Other2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2018
Country/TerritoryAustralia
CityBrisbane
Period5/16/185/19/18

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