Aiming at the problems that the traditional osprey algorithm has low convergence efficiency and is prone to fall into local optimality when solving the path planning problem of intelligent agents, an improved osprey algorithm is proposed. This algorithm integrates the Tent chaotic mapping to enhance the diversity of the population. Secondly, a weight factor and a Gaussian mutation strategy are introduced to prevent the algorithm from falling into local optimality, effectively improving the global search ability. To verify the effectiveness of this algorithm, 10 standard test functions and 2 sets of grid environments with different complexities are selected for experiments. The results show that the improved osprey optimization algorithm has good convergence and convergence rate on the standard test functions. Moreover, compared with the traditional osprey algorithm, the average value of the path optimization length of the improved osprey algorithm decreases by 9.08% and the standard deviation decreases by 49.18% in Environment 1, and the average value of the path optimization length decreases by 6.51% and the standard deviation decreases by 39.62% in Environment 2, which reflects a better path optimization effect and stability.