Video Data (4K): Purely vision-based collective movement of robots

This dataset holds the four main experimental videos in 4K resolution of the paper titled "Purely vision-based collective movement of robots" by Mezey et al. (2024).

Abstract:

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.

The video files are as follows:

1. Video 1: Video of a ca. 90 minute experiment with robots flocking aligned with the arena wall
2. Video 2: Demonstrating fission and later fusion of a single robot
3. Video 3: Demonstrating fisiion/fusion behavior of 2 robots
4. Video 4: Showcasing decentralized design by adding/removing robots on the fly.

The videos are also avialable on the TIB-AV portal in 1080p resolution where they can be cited individually under the DOIs:

1. Video 1: https://doi.org/10.5446/68205
2. Video 2: https://doi.org/10.5446/68204
3. Video 3: https://doi.org/10.5446/68203
4. Video 4: https://doi.org/10.5446/68201

Funding:

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2002/1 “Science of Intelligence” – project number 390523135.