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An inchworm-inspired motion strategy for robotic swarms

Published online by Cambridge University Press:  23 April 2021

Kasra EshaghiOpen the ORCID record for Kasra Eshaghi[Opens in a new window],
Zendai KashinoOpen the ORCID record for Zendai Kashino[Opens in a new window],
Hyun Joong Yoon,
Goldie Nejat and
Beno Benhabib
Kasra Eshaghi*
Affiliation:
Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ON M5S 3G8, Canada
Zendai Kashino
Affiliation:
Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ON M5S 3G8, Canada
Hyun Joong Yoon
Affiliation:
School of Mechanical and Automotive Engineering, Daegu Catholic University, Hayang, Gyeongsan, Gyeongbuk 712-702, Republic of Korea
Goldie Nejat
Affiliation:
Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ON M5S 3G8, Canada
Beno Benhabib
Affiliation:
Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ON M5S 3G8, Canada
*
*Corresponding author. Email: eshaghi@mie.utoronto.ca
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Abstract

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Effective motion planning and localization are necessary tasks for swarm robotic systems to maintain a desired formation while maneuvering. Herein, we present an inchworm-inspired strategy that addresses both these tasks concurrently using anchor robots. The proposed strategy is novel as, by dynamically and optimally selecting the anchor robots, it allows the swarm to maximize its localization performance while also considering secondary objectives, such as the swarm’s speed. A complementary novel method for swarm localization, that fuses inter-robot proximity measurements and motion commands, is also presented. Numerous simulated and physical experiments are included to illustrate our contributions.

Keywords

Swarm roboticsMotion planningSwarm localizationMillirobotsMulti-robot systems

Information

Type
Article
Information
Robotica , Volume 39 , Issue 12 , December 2021, pp. 2283 - 2305
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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