Unmanned aerial vehicle (UAV) path planning is a complex optimization problem, which aims to achieve an optimal or nearly optimal flight path despite various threats and constraints. In this paper, an improved version of Gray Wolf Optimization (GWO) is proposed to solve the UAV 3D path planning problem which considers the dynamics of the UAV. In improved GWO, a variable weighting called "align coefficient" is defined to deal with the problem of waypoint scattering. The parallel GWO is applied to reduce the computation time which makes the possibility of real-time implementation. Given the existence and unique features of CAN bus in UAVs, it is used as a platform to migrate individuals in the parallelization process. The simulation results demonstrate that applying improved GWO generates better performance for UAV 3D path planning problems compared to the conventional GWO, GA, PSO, SA, improved GA and improved PSO.

无人机（UAV）路径规划是一个复杂的优化问题，尽管存在各种威胁和约束，其目的还是要实现最佳或接近最佳的飞行路径。在本文中，提出了一种改进版的灰狼优化（GWO），以解决考虑了无人机动力学的无人机3D路径规划问题。在改进的GWO中，定义了称为“对齐系数”的可变权重来处理航点散射问题。应用并行GWO可以减少计算时间，这使得实时实现成为可能。鉴于无人机中CAN总线的存在和独特功能，它被用作在并行化过程中迁移个人的平台。