This paper introduces a crowd modeling and motion control approach that employs diffusion adaptation within an adaptive network. In the network, nodes collaboratively address specific estimation problems while simultaneously moving as agents governed by certain motion control mechanisms. Our research delves into the behaviors of agents when they encounter spatial constraints. Within this framework, agents pursue several objectives, such as target tracking, coherent motion, and obstacle evasion. Throughout their navigation, they demonstrate a nature of self-organization and self-adjustment that drives them to maintain certain social distances with each other, and adaptively adjust their behaviors in response to the environmental changes. Our findings suggest a promising approach to mitigate the spread of viral pandemics and averting stampedes.

翻译：本文提出了一种采用自适应网络中扩散自适应机制的人群建模与运动控制方法。在网络中，各节点协作解决特定估计问题，同时作为受一定运动控制机制支配的智能体进行移动。本研究深入探讨了智能体在面临空间约束时的行为特征。在此框架下，智能体追求目标跟踪、一致性运动及避障等多重目标。在导航过程中，它们展现出自我组织与自我调节的特性，能够彼此保持一定的社交距离，并根据环境变化自适应地调整自身行为。我们的研究结果表明，该方法为减缓病毒大流行传播及防止踩踏事件提供了有前景的解决途径。