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Particle swarm optimization algorithms for autonomous robots with deterministic leaders using space filling movements

Abstract

In this work the swarm behavior principles of Craig W. Reynolds are combined with deterministic traits. This is done by using leaders with motions based on space filling curves like Peano and Hilbert. Our goal is to evaluate how the swarm of agents works with this approach, supposing the entire swarm will better explore the entire space. Therefore, we examine different combinations of Peano and Hilbert with the already known swarm algorithms and test them in a practical challenge for the harvesting of manganese nodules on the sea ground with the use of autonomous agents. We run experiments with various settings, then evaluate and describe the results. In the last section some further development ideas and thoughts for the expansion of this study are considered.

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Copyright (2018, Springer Nature)

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Category:
Articles
Type:
artykuły w czasopismach
Published in:
Evolving Systems no. 11, pages 383 - 396,
ISSN: 1868-6478
Language:
English
Publication year:
2020
Bibliographic description:
Logofătu D., Sobol G., Andersson C., Stamate D., Balabanov K., Cejrowski T.: Particle swarm optimization algorithms for autonomous robots with deterministic leaders using space filling movements// Evolving Systems -Vol. 11, (2020), s.383-396
DOI:
Digital Object Identifier (open in new tab) 10.1007/s12530-018-9245-9
Bibliography: test
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Gdańsk University of Technology

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