Social competence improves the performance of biomimetic robots leading live fish

Maxeiner, Moritz; Hocke, M.; Moenck, Hauke Juergen; Gebhardt, Gregor H. W.; Weimar, Nils; Musiolek, Lea; Krause, Jens; Bierbach, David; Landgraf, Tim

doi:10.1088/1748-3190/acca59

Cited by 4 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Live fish did not follow a virtual fish as much if it only swam into the center (Stowers et al, 2017). Experiments using guppies and a biomimetic robot suggest a similar mechanism: social responsiveness of the robot led to less avoidance of the robot (Maxeiner et al, 2023). Together, these studies suggest that fish do not blithely follow other fish, but that their willingness to follow depends on the willingness of the other fish to follow them.…”

Section: Social Behaviorsmentioning

confidence: 91%

Can we model autism using zebrafish?

Washbourne

2023

Dev Growth Differ

View full text Add to dashboard Cite

Autism Spectrum Disorder (ASD) is one of the most common, heritable neuropsychiatric disorders in the world, affecting almost 1% of the population. The core symptoms used to diagnose ASD are decreased social interaction and increased repetitive behaviors. Despite the large number of affected individuals, the precise mechanisms that cause the disorder remain unclear. The identification of genes and environmental factors associated with ASD allows the study of the underlying mechanisms in animal models. Although ASD presents as a human disorder, based on recent advances in understanding their brain anatomy, physiology, behavior, and evolutionary conservation of neuronal cell types, I propose that zebrafish may provide novel insights into the etiology.This article is protected by copyright. All rights reserved.

show abstract

Section: Social Behaviorsmentioning

confidence: 91%

Can we model autism using zebrafish?

Washbourne

2023

Dev Growth Differ

View full text Add to dashboard Cite

show abstract

“…In the experiments described in the article by Maxeiner et al [11], authors used a robotic fish replica (programmed as a socially competent leader) to interact with individual female guppies (Poecilia reticulata Peters) and to investigate how they adapt their rules based on social contexts. The guppies followed the socially competent robot longer when it adapted to their avoidance movements.…”

Section: Microscopic Viewmentioning

confidence: 99%

Animal–robot interaction—an emerging field at the intersection of biology and robotics

Romano,

Porfiri,

Zahadat

et al. 2024

Bioinspir. Biomim.

View full text Add to dashboard Cite

The field of animal–robot and organism–robot interaction systems (ARIS, ORIS) is a currently rapidly emerging field in biorobotics. In this special issue we aim for providing a comprehensive overview of the cutting-edge advancements and pioneering breakthroughs within this scientific and engineering discipline. Therefore, we collected scientific articles that delineate and expound upon the complexity of these remarkable biohybrid systems. These configurations stand as engineered conduits, facilitating the accurate investigation and profound exploration of the multifaceted interactions between robotic devices and biological entities, including various fish species, honeybees and plants. Also the human factor plays a role in this collection, as we also include a philosophical perspective on such systems as well as an augmented reality setup that brings humans into the loop with living fish. Within our editorial purview, we categorize the scientific contributions based on their focal points, differentiating between examinations of singular agent-to-agent interactions, extensions to the social stratum, and further expansions to the intricate levels of swarm dynamics, colonies, populations, and ecosystems. Considering potential applications, we delve into the multifaceted domains wherein these biohybrid systems might be applied. This discourse culminates in a tentative glimpse into the future trajectories these technologies might traverse, elucidating their promising prospects for both scientific advancement and societal enrichment. In sum, this special issue aims at facilitating the convergence of diverse insights, at encapsulating the richness of the ARIS and ORIS domain, and at charting a course toward the untapped prospects lying at the nexus of biology and robotics.

show abstract

“…Aside from using particle models and observation studies, robotic fish embedded within a school of fish are being used to observe and influence the behaviors of fish [44,45]. Researchers have demonstrated that the appearance and locomotion of a robot play crucial roles in its successful assimilation within a school of biological fish [46,47].…”

Section: Fish Behaviorsmentioning

confidence: 99%

Fish-inspired robotic algorithm: mimicking behaviour and communication of schooling fish

Connor,

Joordens,

Champion

2023

Bioinspir. Biomim.

View full text Add to dashboard Cite

This study proposes a novel flocking algorithm for underwater robotics based on the collective behaviors of schooling fish. The algorithm, Fish-Inspired Robotic Algorithm (FIRA), integrates standard flocking behaviors such as Attraction, Alignment, and Repulsion, as well as predator avoidance, foraging, general obstacle avoidance, and wandering. Compared with traditional flocking algorithms, FIRA includes predictive elements to counteract processing delays from sensors and prioritizes the highest cluster of agents on a single side, leading to superior performance in collision avoidance, exploration, foraging, and the emergence of realistic behaviors. To address the challenges of underwater communication, FIRA is designed to work with high-latency, non-guaranteed communication methodology based on stigmergy methods found in nature, enabling practical, multi-agent, inexpensive, and tetherless communication. FIRA aims to provide a computational light control algorithm designed to work with low-cost, low-computing agents to further research using robotics to assimilate into a school of biological fish. To demonstrate the effectiveness of FIRA, it was simulated within Unity using a digital twin of a bio-inspired robotic fish, which incorporates the robot's motion and sensors in a realistic, real-time environment, with the algorithm used to direct the movements of individual agents. The performance of FIRA was compared against other collective motion algorithms to determine its effectiveness as a flocking algorithm. From the experiments, FIRA outperformed the other algorithms in both collision avoidance and exploration. These experiments establish FIRA as a viable flocking algorithm to mimic fish behavior in robotics.

show abstract

Social competence improves the performance of biomimetic robots leading live fish

Cited by 4 publications

References 47 publications

Can we model autism using zebrafish?

Can we model autism using zebrafish?

Animal–robot interaction—an emerging field at the intersection of biology and robotics

Fish-inspired robotic algorithm: mimicking behaviour and communication of schooling fish

Contact Info

Product

Resources

About