A task-based hierarchical control strategy for autonomous motion of an unmanned surface vehicle swarm

In this paper, a hierarchical control framework with relevant algorithms is proposed to achieve autonomous navigation for an underactuated unmanned surface vehicle (USV) swarm. In order to implement automatic target tracking, obstacle avoidance and avoid collisions between group members, the control framework is divided into three layers based on task assignments: flocking strategy design, motion planning and control input design. The flocking strategy design transmits some basic orders to swarm members. Motion planning applies the potential function method and then improves it; thus, the issue of autonomous control is transformed into one of designing the velocity vector. In the last layer, the control inputs (surge force and yaw moment) are designed using the sliding mode method, and the problem of underactuation is handled synchronously. The proposed closed-loop controller is shown to be semi-asymptotically stable by applying Lyapunov stability theory, and the effectiveness of the proposed methodology is demonstrated via numeric simulations of a homogeneous USV swarm.