Abstract: In order to reduce time and improve efficiency of route search for inspection robots in a tobacco aging warehouse, a simulation model was constructed using the grid map. Based on Aquila Optimizer (AO), an Improved Aquila Optimizer (IAO) was developed by introducing dynamically adjustable weights and combining the random factor, minimum weight and maximum weight to realize the fast route planning for the inspection robot. The performance of IAO, AO, Harris Hawks Optimization (HHO), Genetic Algorithm (GA) and Ant Colony Algorithm (ACA) was comparatively analyzed with the baseline function, and their performance in the simulation model of tobacco aging warehouse was also compared. The results showed that compared with the other four algorithms, IAO had higher convergence accuracy and robustness, the inspection distance of the robot was reduced by 1.8%, 5.3%, 3.2% and 6.2%, and the inspection time was reduced by 17.3%, 22.5%, 19.6% and 27.1%, respectively. This method supports the improvement of the management efficiency of the tobacco aging warehouse.