  Existing Global Path Planning (GPP) algorithms predominantly presume planning
in static environments. This assumption immensely limits their applications to
Unmanned Surface Vehicles (USVs) that typically navigate in dynamic
environments. To address this limitation, we present OkayPlan, a GPP algorithm
capable of generating safe and short paths in dynamic scenarios at a real-time
executing speed (125 Hz on a desktop-class computer). Specifically, we approach
the challenge of dynamic obstacle avoidance by formulating the path planning
problem as an Obstacle Kinematics Augmented Optimization Problem (OKAOP), which
can be efficiently resolved through a PSO-based optimizer at a real-time speed.
Meanwhile, a Dynamic Prioritized Initialization (DPI) mechanism that adaptively
initializes potential solutions for the optimization problem is established to
further ameliorate the solution quality. Additionally, a relaxation strategy
that facilitates the autonomous tuning of OkayPlan's hyperparameters in dynamic
environments is devised. Comprehensive experiments, including comparative
evaluations, ablation studies, and \textcolor{black}{applications to 3D
physical simulation platforms}, have been conducted to substantiate the
efficacy of our approach. Results indicate that OkayPlan outstrips existing
methods in terms of path safety, length optimality, and computational
efficiency, establishing it as a potent GPP technique for dynamic environments.
The video and code associated with this paper are accessible at
https://github.com/XinJingHao/OkayPlan.
