Safety-Critical Defensive Guidance of Autonomous Surface Vehicles for Domain Protection

This paper addresses the safety-critical defensive guidance of autonomous surface vehicles (ASVs) concerning the interception of multiple attackers to protect a given domain within an obstacle environment. First, an optimal defensive matching method is proposed, which efficiently assigns multiple defensive ASVs to corresponding attackers. Then, an optimal collision-free defensive guidance law is developed to efficiently intercept an attacker in the presence of stochastic measurement noise. By using the proposed safety-critical defensive guidance method, the defensive ASVs are able to intercept the matched attackers with guaranteed safety. Through the stability analysis, the closed-loop system is practical stochastic input-to-state stability. Simulation results substantiate the effectiveness of the proposed safety-critical defensive guidance for the domain protection.