让多智能体在未知环境下，按社会属性协作避障，准时抵达目标。
Incorporating Social Awareness into Control of Unknown Multi-Agent Systems: A Real-Time Spatiotemporal Tubes Approach
- 基于时空管框架在线生成动态控制管，实时融合社会行为
- 无需模型、计算轻量，确保避障与碰撞规避，且在规定时间完成任务
- 适合复杂动态环境下的机器人协同控制，尤其看重实时性与安全性
本文提出一种去中心化的控制框架，将社会意识融入具有未知动力学的多智能体系统中，实现在动态环境中预定时间内的到达-避障-停留任务。每个智能体被赋予一个社会意识指数，量化其合作或利己程度，实现系统内异质的社会行为。基于时空管（STT）框架，我们提出了一个实时STT框架，可为每个智能体在线合成其动态时空管，同时捕捉与其他智能体的社会交互。推导出闭式、无需近似的控制律，确保每个智能体始终处于其演化的时空管内，从而在避免动态障碍的同时，以社会感知方式防止智能体间碰撞，并在规定时间内到达目标。所提方法提供安全性和时序的严格保证，计算轻量，无需模型，且对未知扰动鲁棒。通过二维全向移动平台的仿真与硬件实验验证了该框架的有效性与可扩展性。
原文摘要 · Abstract (English)
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
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