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.

翻译：在群体运动中，感知能力有限的个体在没有集中控制的情况下有序移动。每个个体的感知能力高度局部化，其与其他个体互动的能力亦是如此。尽管自然群体运动具有鲁棒性，但大多数人工群体却十分脆弱。当使用视觉作为感知模态时，这种情况尤为突出，这是由于视觉感知固有的模糊性和信息损失所致。本文提出了受蝗虫研究启发的鲁棒群体运动机制。首先，我们开发了一种鲁棒的距离估计方法，该方法结合了视觉感知到的邻居水平与垂直尺寸。其次，我们引入间歇性运动作为一种机制，使机器人能够可靠地检测出未能跟上队伍的同伴，并中断群体的运动。我们展示了如何以对故障分类错误具有鲁棒性的方式避开此类故障机器人。通过大量基于物理的仿真实验，我们证明了在使用这些技术时，群体韧性得到了显著提升。我们表明这些机制与基于距离的避让-吸引模型以及依赖对齐的模型均相关，适用于广泛的实验场景。