Safety Embedded Stochastic Optimal Control of Networked Multi-Agent Systems via Barrier States

TL;DR

This paper introduces a new method for ensuring safety in stochastic optimal control of networked multi-agent systems by embedding barrier states into the system dynamics, enabling safe and optimal control actions.

Contribution

It develops a barrier state-based control framework that guarantees safety constraints are satisfied while optimizing control performance in networked multi-agent systems.

Findings

Successfully applied to cooperative UAV team simulations

Guarantees safety through bounded barrier states

Maintains optimality with path integral approximation

Abstract

This paper presents a novel approach for achieving safe stochastic optimal control in networked multi-agent systems (MASs). The proposed method incorporates barrier states (BaSs) into the system dynamics to embed safety constraints. To accomplish this, the networked MAS is factorized into multiple subsystems, and each one is augmented with BaSs for the central agent. The optimal control law is obtained by solving the joint Hamilton-Jacobi-Bellman (HJB) equation on the augmented subsystem, which guarantees safety via the boundedness of the BaSs. The BaS-based optimal control technique yields safe control actions while maintaining optimality. The safe optimal control solution is approximated using path integrals. To validate the effectiveness of the proposed approach, numerical simulations are conducted on a cooperative UAV team in two different scenarios.