Purely vision-based collective movement of robots

Collective movement inspired by animal groups promises inherited benefits for robot swarms, such as enhanced sensing and efficiency. However, while animals move in groups using only their local senses, robots often obey central control or use direct communication, introducing systemic weaknesses to...

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Bibliographic Details
Published inarXiv.org
Main Authors Mezey, David, Bastien, Renaud, Zheng, Yating, McKee, Neal, Stoll, David, Hamann, Heiko, Romanczuk, Pawel
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 24.06.2024
Subjects
Changing environments
Communication
Confined spaces
Field of view
Robot control
Robot dynamics
Robots
Vision
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Summary:Collective movement inspired by animal groups promises inherited benefits for robot swarms, such as enhanced sensing and efficiency. However, while animals move in groups using only their local senses, robots often obey central control or use direct communication, introducing systemic weaknesses to the swarm. In the hope of addressing such vulnerabilities, developing bio-inspired decentralized swarms has been a major focus in recent decades. Yet, creating robots that move efficiently together using only local sensory information remains an extraordinary challenge. In this work, we present a decentralized, purely vision-based swarm of terrestrial robots. Within this novel framework robots achieve collisionless, polarized motion exclusively through minimal visual interactions, computing everything on board based on their individual camera streams, making central processing or direct communication obsolete. With agent-based simulations, we further show that using this model, even with a strictly limited field of view and within confined spaces, ordered group motion can emerge, while also highlighting key limitations. Our results offer a multitude of practical applications from hybrid societies coordinating collective movement without any common communication protocol, to advanced, decentralized vision-based robot swarms capable of diverse tasks in ever-changing environments.
ISSN:2331-8422