Aiming at the problems of low search efficiency, easy to fall into local optimum, and too many redundant nodes in the path planning process of mobile robots in complex obstacle environments, this paper proposes a path planning method based on the fusion of genetic algorithm and particle swarm optimization algorithm. First, the improved genetic algorithm is used to generate a high-quality initial path population, which provides a priori search guidance for subsequent particle swarm optimization, increases the diversity of the population, and accelerates the convergence of the algorithm; second, a dual strategy based on the change of fitness and iteration progress is proposed to dynamically adjust the crossover probability, and a nonlinear dynamically diminishing inertia weight adjustment method is proposed, so as to efficiently balance the algorithm′s global and local search; next, a vector fork-based path planning method is proposed to solve the problem of low search efficiency in the path planning process. Then, the vector fork product-based geometric redundant node discrimination criterion and the obstacle safety distance threshold discrimination method are proposed to effectively remove the redundant nodes and transition nodes in the path, so as to shorten the path length and improve the optimization ability of the path; finally, simulation experiments are carried out in five benchmark test functions and two different raster maps environments to verify the optimization performance of the algorithm. The experimental results show that compared with the genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, gray wolf optimization algorithm, sparrow search algorithm, dung beetle optimization algorithm and crown porcupine optimization algorithm, the proposed algorithm in this paper reduces the path length by an average of 3.74% and the runtime by an average of 23.13% in a 20×20 raster map; and in a 30×30 raster map, the path length reduces by an average of by 4.83% and runtime by 19.95% in 30×30 raster maps. In addition, the number of path nodes planned by the algorithm in this paper is relatively small, indicating that the algorithm proposed in this paper can not only effectively shorten the path length and reduce the running time, but also effectively simplify the path, showing good optimization ability.