S-Flocking: Trajectory Planning for Unmanned Surface Vehicle Formation Penetration in Constrained Harbor Environments

Unmanned surface vehicle (USV) formation navigation in constrained harbor environments presents challenges for maritime Internet of Things (IoT) applications, including surveillance, inspection, and rapid response missions. This paper addresses the trajectory planning problem for high-speed penetration, specifically focusing on minimizing oscillations in the reference trajectories. We propose S-Flocking, an integrated framework that reduces abrupt transitions in behavioral vector synthesis through the sigmoid-based transformation. For global path planning, we propose the Bisection-based Rewiring RRT* (B-RRT*) algorithm, which combines bidirectional search with dual backtracking optimization and bisection-based node insertion strategy to enhance computational efficiency and path quality. For reactive local planning, we introduce a dual-layer avoidance strategy (DAS) that integrates COLREGs-compliant maneuvers with 1-vs-1 emergency mechanism, resolving conflicts between formation-keeping and avoidance in dynamic encounters. Comprehensive simulations demonstrate that S-Flocking generates smoother reference trajectories with effective formation cohesion, thereby providing a robust solution for autonomous cooperative navigation in partially unknown dynamic maritime environments.