Sun, G. orcid.org/0000-0002-8729-2856, Zhou, R., Ma, Z. et al. (4 more authors) (2023) Mean-shift exploration in shape assembly of robot swarms. Nature Communications, 14 (1). 3476. ISSN 2041-1723
Abstract
The fascinating collective behaviors of biological systems have inspired extensive studies on shape assembly of robot swarms. Here, we propose a strategy for shape assembly of robot swarms based on the idea of mean-shift exploration: when a robot is surrounded by neighboring robots and unoccupied locations, it would actively give up its current location by exploring the highest density of nearby unoccupied locations in the desired shape. This idea is realized by adapting the mean-shift algorithm, which is an optimization technique widely used in machine learning for locating the maxima of a density function. The proposed strategy empowers robot swarms to assemble highly complex shapes with strong adaptability, as verified by experiments with swarms of 50 ground robots. The comparison between the proposed strategy and the state-of-the-art demonstrates its high efficiency especially for large-scale swarms. The proposed strategy can also be adapted to generate interesting behaviors including shape regeneration, cooperative cargo transportation, and complex environment exploration.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Complex networks; Electrical and electronic engineering |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Jun 2023 13:29 |
Last Modified: | 16 Jun 2023 13:29 |
Status: | Published |
Publisher: | Springer Science and Business Media LLC |
Refereed: | Yes |
Identification Number: | 10.1038/s41467-023-39251-5 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:200472 |