The path planned by the traditional Grey Wolf Optimization(GWO) algorithm better in the world, but it had the defects of low solution efficiency and easy to fall into the local optimum. Although the path planned by the Artificial Potential Field(APF) algorithm is smooth, there are turbulences and fluctuations in the planned path. The defect of unreachable target. Aiming at the different shortcomings of the two algorithms, the two algorithms are improved respectively, and the two algorithms are merged, and an algorithm that takes into account the global and local characteristics is proposed—Grey Wolf Potential Field Algorithm (GWPFA). First, a new method for establishing feature grid maps is proposed to speed up the determination of feature grids and the establishment of feature grid maps; secondly, by setting the relative distance d of the grey wolf individual and the adjustment factor λ, the parameter a is improved to non-Linear attenuation; again, the concept of node priority is proposed, and the path planning problem is modeled based on this concept; finally, the node that improves the global path planning of the GWO algorithm is used as the temporary target point of the APF algorithm, and the temporary target point is improved as temporary Boundaries, and then local path planning. The simulation results show that in the global static environment, the running time, optimal path length, and turning angle of the GWPAF algorithm are optimized by 224.5s､16.3m and 38.9°C respectively compared with the GWO algorithm; in a local dynamic environment, the GWPFA algorithm is guaranteed The path is optimal while avoiding obstacles successfully. The simulation results verify the effectiveness､feasibility and superiority of the GWPFA algorithm.