Aiming at the problems of slow convergence speed, insufficient precision, and easy trapping in local optima of the traditional Whale Optimization Algorithm (WOA) in mobile robot path planning, an Improved Whale Optimization Algorithm (IWOA) is proposed. The specific improvements include: using Logistic chaotic mapping to initialize the population, which enhances the randomness and diversity of initial solutions; designing a nonlinear convergence factor to avoid the algorithm entering the contraction encirclement phase prematurely and extend the global search time; introducing an adaptive spiral shape constant to dynamically adjust the search range for balancing exploration and exploitation capabilities; and integrating the bird navigation mechanism to optimize the individual update strategy through the companion position information, thereby improving the algorithm’s stability and convergence speed. Path planning simulations were conducted in 30×30 and 50×50 grid maps. The results show that compared with WOA, GA, and PSO, the path length of IWOA is shortened by 3.21%, 2.00%, and 7.76% respectively in the 30×30 map, and by 4.88%, 10.50%, and 23.55% respectively in the 50×50 map. The research indicates that IWOA has significant advantages in both path planning precision and efficiency, verifying its feasibility and superiority.