Control and Optimization in Multi-agent Systems: A Trilogy
Talk, Pennovation Center, Philadelphia, PA
Multi-agent systems are collections of interacting autonomous agents that are becoming increasingly prevalent with the rising popularity of learning-enabled autonomy. Achieving coordinated behavior in collaborative multi-agent settings raises fundamental challenges, including limited or structured communication, complex heterogeneous system architectures, and stringent safety requirements. In this talk, we develop theoretical tools and algorithms that address these challenges across communication, optimization, and safety. First, we leverage applied sheaf theory to model heterogeneous agents and tasks over complex communication topologies, and we propose an asynchronous nonlinear sheaf diffusion method for coordination. Second, we introduce a fully asynchronous momentum-based algorithm that accelerates convergence in parallel optimization without requiring known bounds on communication or computation delays. Finally, we develop a control barrier function (CBF) selection framework with time-varying CBFs called Delta-CBF that preserves safety guarantees while accelerating task completion.