To address the issues of insufficient adaptive adjustment capability during the search phase and susceptibility to local optima due to excessive pheromone accumulation in the path planning process of warehouse robots

a warehouse-oriented ant colony optimization (WOACO) algorithm is proposed. First

the heuristic function is improved by introducing an adaptive iterative weight factor and an obstacle density correction term. Second

a positive-negative update mechanism is introduced into the pheromone strategy to appropriately strengthen and reduce the pheromone on the path. Finally

triangular pruning is incorporated to optimize large deflection angles of the motion trajectory. Experiments on two different grid maps show that optimal path length

number of inflection points

and running time are reduced by 75.27%

84.87%

and 16.98%

respectively

demonstrating the superiority and robustness of the improved ant colony algorithm.