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arXiv · 1910.12415

Robotic Hierarchical Graph Neurons. A novel implementation of HGN for swarm robotic behaviour control

Phillip Smith·Aldeida Aleti·Vincent C. S. Lee·Robert Hunjet·Asad Khan
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Abstract

This paper explores the use of a novel form of Hierarchical Graph Neurons (HGN) for in-operation behaviour selection in a swarm of robotic agents. This new HGN is called Robotic-HGN (R-HGN), as it matches robot environment observations to environment labels via fusion of match probabilities from both temporal and intra-swarm collections. This approach is novel for HGN as it addresses robotic observations being pseudo-continuous numbers, rather than categorical values. Additionally, the proposed approach is memory and computation-power conservative and thus is acceptable for use in mobile devices such as single-board computers, which are often used in mobile robotic agents. This R-HGN approach is validated against individual behaviour implementation and random behaviour selection. This contrast is made in two sets of simulated environments: environments designed to challenge the held behaviours of the R-HGN, and randomly generated environments which are more challenging for the robotic swarm than R-HGN training conditions. R-HGN has been found to enable appropriate behaviour selection in both these sets, allowing significant swarm performance in pre-trained and unexpected environment conditions.

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Phillip Smith, Aldeida Aleti, Vincent C. S. Lee, Robert Hunjet, Asad Khan. 2019-10-28. Robotic Hierarchical Graph Neurons. A novel implementation of HGN for swarm robotic behaviour control. https://doi.org/10.1016/j.eswa.2021.115675

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