Safety–Critical Pursuit–Evasion Game of Multiple Autonomous Surface Vehicles Based on Min–Max Optimization and Neural Network Dynamic Control

This article investigates the pursuit–evasion problem of multiple underactuated autonomous surface vehicles (ASVs) under velocity and collision avoidance constraints. A safety–critical pursuit–evasion game (PEG) method based on min–max optimization and neural network dynamic control is proposed. Specifically, an allocation strategy is designed at first based on the position information of the pursuing and evading ASVs to achieve a rational and efficient allocation of pursuit targets by minimizing pursuit distances. Next, a nominal PEG guidance law is proposed by combining model predictive control (MPC) with min–max optimization methods. Then, the nominal guidance law is optimized based on a heading-constrained control barrier function (CBF) such that a safety–critical guidance law for collision avoidance can be achieved. Finally, a predicator-based neural network is developed to estimate the uncertainty and external disturbance, and a dynamic control law is proposed to track the guidance signals without using any model parameters. It is proven that the closed-loop system is input–to–state stable (ISS), and the ASV system is safe. A robot-operating-system (ROS)-based simulation results demonstrate the effectiveness of the proposed safety–critical PEG method based on min–max optimization and neural network dynamic control.