10个球形机器人仅靠视觉实现群体运动，首次真实复现集群行为。
Visual collective behaviors on spherical robots
- 仅用全景视觉信息（位置、大小、光流）驱动群体运动
- 成功复现蜂拥与环形运动，且物理实验与仿真几乎一致
- 无需全局感知，适合可扩展的机器人集群研究
传统群体运动实现通常假设个体具有全知感知能力，忽视实际感知限制。本研究采用「机器人闭环」方法，在由10个独立球形机器人组成的群体中实现了视觉蜂群模型。该模型仅依赖每个机器人的全景视觉信息，包括邻近机器人在视网膜上的位置、光学大小及光流。引入虚拟锚点以避免与墙碰撞，从而约束集体运动。首次通过简单视觉闭环方法成功再现了多种群体运动阶段，尤其是蜂拥与环形运动。另一重要突破是，该模型在仿真与物理实验中表现出几乎相同的动态行为，成功弥合了仿真与实测之间的差距。结论表明，此最小化视觉群体运动模型足以在机器人闭环系统中重现多数群体行为，具备可扩展性、符合数值模拟预测，且易于与传统模型对比。
原文摘要 · Abstract (English)
The implementation of collective motion, traditionally, disregard the limited sensing capabilities of an individual, to instead assuming an omniscient perception of the environment. This study implements a visual flocking model in a ``robot-in-the-loop'' approach to reproduce these behaviors with a flock composed of 10 independent spherical robots. The model achieves robotic collective motion by only using panoramic visual information of each robot, such as retinal position, optical size and optic flow of the neighboring robots. We introduce a virtual anchor to confine the collective robotic movements so to avoid wall interactions. For the first time, a simple visual robot-in-the-loop approach succeed in reproducing several collective motion phases, in particular, swarming, and milling. Another milestone achieved with by this model is bridging the gap between simulation and physical experiments by demonstrating nearly identical behaviors in both environments with the same visual model. To conclude, we show that our minimal visual collective motion model is sufficient to recreate most collective behaviors on a robot-in-the-loop system that is scalable, behaves as numerical simulations predict and is easily comparable to traditional models.
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