Incorporating Social Awareness into Control of Unknown Multi-Agent Systems: A Real-Time Spatiotemporal Tubes Approach

2025-10-29

AI Summary

This paper addresses decentralized real-time control of heterogeneous multi-agent systems operating in dynamic environments with unknown dynamics and diverse behavioral characteristics, aiming to achieve coordinated arrival–obstacle avoidance–stationkeeping within a prescribed time. Methodologically, we propose an online-synthesizable spatiotemporal tube (STT) framework integrated with a social-awareness metric that quantifies individual collaboration levels; further, we design a model-free, approximation-free closed-form control law ensuring unified guarantees on safety, timeliness, and social compliance. Our key contributions include: (i) the first integration of explicit social-awareness modeling into the STT paradigm, enabling robust, lightweight, and disturbance-resilient closed-loop coordination among heterogeneous agents; and (ii) rigorous theoretical guarantees of prescribed-time convergence and collision avoidance. Extensive simulations and experiments on 2D omnidirectional robots validate the framework’s effectiveness, real-time performance, and scalability.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multi-Robot Systems

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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

Problem

• Decentralized control for multi-agent systems with unknown dynamics
• Incorporating social awareness to enable heterogeneous cooperative behaviors
• Real-time spatiotemporal tubes ensuring safety and prescribed-time objectives

Innovation

• Decentralized control framework with social awareness index
• Real-time spatiotemporal tubes for online social interactions
• Model-free control law ensuring safety and timing guarantees