ATI: Assemble topological interaction overcomes consistency–cohesion trade-off in bird flocking

IF 1.5 Q3 AUTOMATION & CONTROL SYSTEMS IET Cybersystems and Robotics Pub Date : 2024-04-21 DOI:10.1049/csy2.12114
Jialei Huang, Bo Zhu, Tianjiang Hu
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Abstract

In nature, various animal groups like bird flocks display proficient collective navigation achieved by maintaining high consistency and cohesion simultaneously. Both metric and topological interactions have been explored to ensure high consistency among groups. The topological interactions found in bird flocks are more cohesive than metric interactions against external perturbations, especially the spatially balanced topological interaction (SBTI). However, it is revealed that in complex environments, pursuing cohesion via existing interactions compromises consistency. The authors introduce an innovative solution, assemble topological interaction, to address this challenge. Contrasting with static interaction rules, the new interaction empowers individuals with self-awareness to adapt to the complex environment by switching between interactions through visual cues. Most individuals employ high-consistency k-nearest topological interaction when not facing splitting threats. In the presence of such threats, some switch to the high-cohesion SBTI to avert splitting. The assemble topological interaction thus transcends the limit of the trade-off between consistency and cohesion. In addition, by comparing groups with varying degrees of these two features, the authors demonstrate that group effects are vital for efficient navigation led by a minority of informed agents. Finally, the real-world drone-swarm experiments validate the applicability of the proposed interaction to artificial robotic collectives.

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ATI:集合拓扑相互作用克服了鸟群中一致性与内聚性的权衡
在自然界中,各种动物群体(如鸟群)通过同时保持高度的一致性和凝聚力,显示出熟练的集体导航能力。为了确保群体间的高度一致性,人们对度量和拓扑相互作用都进行了探索。在鸟群中发现的拓扑交互作用比度量交互作用(尤其是空间平衡拓扑交互作用(SBTI))更能抵御外部扰动。然而,研究发现,在复杂的环境中,通过现有的相互作用来追求凝聚力会损害一致性。作者引入了一种创新的解决方案--组合拓扑交互来应对这一挑战。与静态的交互规则不同,新的交互赋予了具有自我意识的个体权力,通过视觉提示在交互之间进行切换,从而适应复杂的环境。在不面临分裂威胁时,大多数个体会采用高一致性的 k-nearest 拓扑交互。在面临这种威胁时,一些个体会切换到高内聚力的 SBTI 来避免分裂。因此,集合拓扑互动超越了一致性和内聚力之间权衡的极限。此外,通过比较具有不同程度这两种特征的群体,作者证明了群体效应对于由少数知情者领导的高效导航至关重要。最后,现实世界的无人机群实验验证了所提出的互动方法适用于人工机器人集体。
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来源期刊
IET Cybersystems and Robotics
IET Cybersystems and Robotics Computer Science-Information Systems
CiteScore
3.70
自引率
0.00%
发文量
31
审稿时长
34 weeks
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