This paper presents a decentralized control framework that incorporates social awareness into multi-agent systems with unknown dynamics to achieve prescribed-time reach-avoid-stay tasks in dynamic environments. Each agent is assigned a social awareness index that quantifies its level of cooperation or self-interest, allowing heterogeneous social behaviors within the system. Building on the spatiotemporal tube (STT) framework, we propose a real-time STT framework that synthesizes tubes online for each agent while capturing its social interactions with others. A closed-form, approximation-free control law is derived to ensure that each agent remains within its evolving STT, thereby avoiding dynamic obstacles while also preventing inter-agent collisions in a socially aware manner, and reaching the target within a prescribed time. The proposed approach provides formal guarantees on safety and timing, and is computationally lightweight, model-free, and robust to unknown disturbances. The effectiveness and scalability of the framework are validated through simulation and hardware experiments on a 2D omnidirectional

翻译：本文提出了一种去中心化控制框架，将社会意识融入具有未知动态特性的多智能体系统中，以在动态环境中实现规定时间的到达-规避-停留任务。每个智能体被分配一个社会意识指数，用于量化其合作或自利的程度，从而允许系统内存在异构的社会行为。基于时空管框架，我们提出了一种实时时空管框架，该框架在线为每个智能体合成时空管，同时捕捉其与其他智能体的社会交互。我们推导出一种闭式、无近似的控制律，以确保每个智能体保持在自身演化的时空管内，从而以社会意识的方式规避动态障碍物并防止智能体间碰撞，并在规定时间内到达目标。所提出的方法为安全性和时序提供了形式化保证，并且计算轻量、无需模型、对未知干扰具有鲁棒性。该框架的有效性和可扩展性通过二维全向移动平台的仿真和硬件实验得到了验证。