Environment induced emergence of collective behavior in evolving swarms with limited sensing

Diggelen, Fuda van; Luo, Jianxun; Karagüzel, Tugay Alperen; Cambier, Nicolas; Ferrante, Eliseo; Eiben, A. E.

doi:10.1145/3512290.3528735

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…In their study, the authors tested their control software on robots with minimal capabilities: each robot had a single binary sensor that controlled the speed of its two wheels. van Diggelen et al. (2022) evolved a gradient following behavior.…”

Section: Robot Evolutionmentioning

confidence: 99%

Recent trends in robot learning and evolution for swarm robotics

Kuckling

2023

Front. Robot. AI

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Swarm robotics is a promising approach to control large groups of robots. However, designing the individual behavior of the robots so that a desired collective behavior emerges is still a major challenge. In recent years, many advances in the automatic design of control software for robot swarms have been made, thus making automatic design a promising tool to address this challenge. In this article, I highlight and discuss recent advances and trends in offline robot evolution, embodied evolution, and offline robot learning for swarm robotics. For each approach, I describe recent design methods of interest, and commonly encountered challenges. In addition to the review, I provide a perspective on recent trends and discuss how they might influence future research to help address the remaining challenges of designing robot swarms.

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Section: Robot Evolutionmentioning

confidence: 99%

Recent trends in robot learning and evolution for swarm robotics

Kuckling

2023

Front. Robot. AI

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Emergence of Specialised Collective Behaviors in Evolving Heterogeneous Swarms

van Diggelen,

de Carlo,

Cambier

et al. 2024

Lecture Notes in Computer Science

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Natural groups of animals, such as swarms of social insects, exhibit astonishing degrees of task specialization, useful for solving complex tasks and for survival. This is supported by phenotypic plasticity: individuals sharing the same genotype that is expressed differently for different classes of individuals, each specializing in one task. In this work, we evolve a swarm of simulated robots with phenotypic plasticity to study the emergence of specialized collective behavior during an emergent perception task. Phenotypic plasticity is realized in the form of heterogeneity of behavior by dividing the genotype into two components, with a different neural network controller associated to each component. The whole genotype, which expresses the behavior of the whole group through the two components, is subject to evolution with a single fitness function. We analyze the obtained behaviors and use the insights provided by these results to design an online regulatory mechanism. Our experiments show four main findings: 1) Heterogeneity improves both robustness and scalability; 2) The sub-groups evolve distinct emergent behaviors. 3) The effectiveness of the whole swarm depends on the interaction between the two sub-groups, leading to a more robust performance than with singular sub-group behavior. 4) The online regulatory mechanism improves overall performance and scalability.

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Rat-Robot Autonomous Navigation System Based on Wearable Sensors

Yang

Xie

et al. 2023

IEEE Sensors J.

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Environment induced emergence of collective behavior in evolving swarms with limited sensing

Cited by 5 publications

References 28 publications

Recent trends in robot learning and evolution for swarm robotics

Recent trends in robot learning and evolution for swarm robotics

Emergence of Specialised Collective Behaviors in Evolving Heterogeneous Swarms

Rat-Robot Autonomous Navigation System Based on Wearable Sensors

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