In order to address the issues of traditional algorithms in path planning under complex grid environments, such as easy entrapment in local optima, slow convergence speed, and insufficient path safety, a path planning method fusing grey wolf optimizer (GWO) and ant colony optimization (ACO) is proposed. The grid map is taken as the environment model. Key parameters of ACO are optimized by GWO. Simultaneously, excellent path information from GWO leaders is introduced to enhance the pheromone update mechanism of ACO. The core highlight of this algorithm lies in the realization of a deep closed-loop between macroscopic parameter optimization and microscopic pheromone guidance. To enhance algorithm performance, the GWO was improved. Logistic chaotic map was adopted for population initialization to improve diversity. An adaptive convergence factor and a dynamic weight adjustment strategy were designed to optimize position updates. Furthermore, unsafe diagonal movements near obstacles were constrained by a function to ensure path legitimacy. The experimental results indicate that compared with traditional ACO or GWO, the proposed algorithm converges to shorter paths more quickly in large-scale grid maps. Obstacles and unsafe areas are effectively avoided. The effectiveness and superiority of the method in complex environment path planning are verified. Technical support is provided for autonomous navigation of mobile robots and Automated Guided Vehicles .