Aiming at the path planning of mobile robots in storage environment, a path planning method based on Ion Motion-Artificial Bee Colony (IM-ABC) algorithm was proposed. In order to improve the convergence speed and searching ability of the traditional Artificial Bee Colony (ABC) algorithm in path planning, a strategy of simulating ion motion was used to update the swarm in this method. Firstly, at the early stage of the algorithm, the anion-cation cross search in ion motion algorithm was used to update the leading bees and following bees, so as to guide the direction of population evolution and greatly improve the development ability of population. Secondly, at the late stage of the algorithm, in order to avoid the local optimum caused by premature convergence in the early stage, random search was adopted by the leading bees and reverse roulette was used by the following bees to select honey sources and expand population diversity. Finally, an adaptive floral fragrance concentration was proposed in the global update mechanism to improve the sampling method, and then the IM-ABC algorithm was obtained. Benchmark function test and simulation experiment results show that the IM-ABC algorithm can not only rapidly converge, but also reduce the number of iterations by 58.3% and improve the optimization performance by 12.6% compared to the traditional ABC algorithm, indicating the high planning efficiency of IM-ABC algorithm.