模仿蝗虫行为，让机器人集群在视觉受限下仍能稳定协同运动。
Bugs with Features: Vision-Based Fault-Tolerant Collective Motion Inspired by Nature
- 结合视觉水平与垂直尺寸估算距离，提升感知鲁棒性。
- 引入间歇性移动机制，自动识别并排除掉队个体。
- 在多种模型和场景下显著增强集群抗故障能力。
在群体运动中，感知能力有限的个体无需中心控制即可有序移动，其感知和交互均高度局部化。尽管自然界群体运动具有强鲁棒性，但大多数人工集群却十分脆弱，尤其当使用视觉作为感知方式时，易受视觉模糊和信息丢失影响。本文受蝗虫研究启发，提出两种增强集群鲁棒性的机制：首先，通过融合邻近个体在水平和垂直方向的视觉尺寸，实现更稳定的距离估计；其次，引入间歇性运动机制，使机器人能够可靠检测落后个体，并中断集群运动以避免其干扰。实验表明，该方法对误判为故障个体具有强容错性，在多种基于距离的避撞-吸引模型及对齐模型中，均显著提升了集群在物理仿真环境下的韧性，适用于广泛实验设置。
原文摘要 · Abstract (English)
In collective motion, perceptually-limited individuals move in an ordered manner, without centralized control. The perception of each individual is highly localized, as is its ability to interact with others. While natural collective motion is robust, most artificial swarms are brittle. This particularly occurs when vision is used as the sensing modality, due to ambiguities and information-loss inherent in visual perception. This paper presents mechanisms for robust collective motion inspired by studies of locusts. First, we develop a robust distance estimation method that combines visually perceived horizontal and vertical sizes of neighbors. Second, we introduce intermittent locomotion as a mechanism that allows robots to reliably detect peers that fail to keep up, and disrupt the motion of the swarm. We show how such faulty robots can be avoided in a manner that is robust to errors in classifying them as faulty. Through extensive physics-based simulation experiments, we show dramatic improvements to swarm resilience when using these techniques. We show these are relevant to both distance-based Avoid-Attract models, as well as to models relying on Alignment, in a wide range of experiment settings.
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