Incorporating Social Awareness into Control of Unknown Multi-Agent Systems: A Real-Time Spatiotemporal Tubes Approach
This work addresses the challenge of safe and timely coordination in dynamic environments for applications like robotics and autonomous systems, representing an incremental advance by integrating social awareness into existing spatiotemporal tube methods.
The paper tackles the problem of controlling multi-agent systems with unknown dynamics by incorporating social awareness to achieve prescribed-time reach-avoid-stay tasks, resulting in a decentralized framework that provides formal safety and timing guarantees and is validated through simulations and hardware experiments.
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