Seongin Na
Bio-inspired artificial pheromone system for swarm robotics applications
Na, Seongin; Qiu, Yiping; Turgut, Ali E; Ulrich, Jiří; Krajník, Tomáš; Yue, Shigang; Lennox, Barry; Arvin, Farshad
Authors
Yiping Qiu
Ali E Turgut
Jiří Ulrich
Tomáš Krajník
Shigang Yue
Barry Lennox
Professor Farshad Arvin farshad.arvin@durham.ac.uk
Professor
Abstract
Pheromones are chemical substances released into the environment by an individual animal, which elicit stereotyped behaviours widely found across the animal kingdom. Inspired by the effective use of pheromones in social insects, pheromonal communication has been adopted to swarm robotics domain using diverse approaches such as alcohol, RFID tags and light. COSΦ is one of the light-based artificial pheromone systems which can emulate realistic pheromones and environment properties through the system. This article provides a significant improvement to the state-of-the-art by proposing a novel artificial pheromone system that simulates pheromones with environmental effects by adopting a model of spatio-temporal development of pheromone derived from a flow of fluid in nature. Using the proposed system, we investigated the collective behaviour of a robot swarm in a bio-inspired aggregation scenario, where robots aggregated on a circular pheromone cue with different environmental factors, that is, diffusion and pheromone shift. The results demonstrated the feasibility of the proposed pheromone system for use in swarm robotic applications.
Citation
Na, S., Qiu, Y., Turgut, A. E., Ulrich, J., Krajník, T., Yue, S., Lennox, B., & Arvin, F. (2021). Bio-inspired artificial pheromone system for swarm robotics applications. Adaptive Behavior, 29(4), 395-415. https://doi.org/10.1177/1059712320918936
Journal Article Type | Article |
---|---|
Online Publication Date | Jun 3, 2020 |
Publication Date | 2021-08 |
Deposit Date | May 27, 2022 |
Journal | Adaptive Behavior |
Print ISSN | 1059-7123 |
Electronic ISSN | 1741-2633 |
Publisher | SAGE Publications |
Volume | 29 |
Issue | 4 |
Pages | 395-415 |
DOI | https://doi.org/10.1177/1059712320918936 |
Public URL | https://durham-repository.worktribe.com/output/1202686 |
You might also like
Editorial: Swarm neuro-robots with the bio-inspired environmental perception.
(2024)
Journal Article
Organisms as sensors in biohybrid entities as a novel tool for in-field aquatic monitoring
(2023)
Journal Article
Reinforcement learning-based aggregation for robot swarms
(2023)
Journal Article