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：本文提出了一种人群建模与运动控制方法，该方法在自适应网络中采用扩散自适应机制。网络中，节点协同解决特定的估计问题，同时作为受特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表现。在这一框架下，智能体追求多个目标，如目标追踪、一致性运动及避障。在导航过程中，它们表现出自组织与自调整的特性，促使彼此之间保持一定的社会距离，并根据环境变化自适应地调整自身行为。研究结果表明，该方法为缓解病毒大流行的传播和预防踩踏事件提供了一种有前景的途径。