PDF(Pubmed)
Abstract：
Despite the profound implications of self-organization in animal groups for collective behaviors, understanding the fundamental principles and applying them to swarm robotics remains incomplete. Here we propose a heuristic measure of perception of motion salience (MS) to quantify relative motion changes of neighbors from first-person view. Leveraging three large bird-flocking datasets, we explore how this perception of MS relates to the structure of leader-follower (LF) relations, and further perform an individual-level correlation analysis between past perception of MS and future change rate of velocity consensus. We observe prevalence of the positive correlations in real flocks, which demonstrates that individuals will accelerate the convergence of velocity with neighbors who have higher MS. This empirical finding motivates us to introduce the concept of adaptive MS-based (AMS) interaction in swarm model. Finally, we implement AMS in a swarm of ~102 miniature robots. Swarm experiments show the significant advantage of AMS in enhancing self-organization of the swarm for smooth evacuations from confined environments.
摘要：
尽管动物群体的自我组织对集体行为有着深远的影响，理解基本原理并将其应用于群体机器人技术仍然不完整。在这里，我们提出了一种对运动显著性（MS）感知的启发式度量，以从第一人称视角量化邻居的相对运动变化。利用三个大型鸟群数据集，我们探讨了这种对MS的感知如何与领导者-追随者(LF)关系的结构相关，并进一步对过去的MS感知和未来的速度一致性变化率进行个体水平的相关分析。我们观察到真实羊群中正相关的患病率，这表明个体将加速与具有较高MS的邻居的速度收敛。这一经验发现激励我们在群体模型中引入基于自适应MS(AMS)交互的概念。最后,我们在102个微型机器人的群中实现了AMS。群体实验表明，AMS在增强群体的自组织能力方面具有显着优势，可以从密闭环境中顺利撤离。
