This research addresses prevalent issues in global path planning algorithms for Automated Guided Vehicles (AGVs) by proposing an improved ant colony algorithm(IACA). A nonuniform distribution method for initial pheromones is proposed to enhance path planning accuracy and minimize randomness in ant colony searches. A new heuristic function calculation model is developed to expedite the algorithm’s convergence rate. To bolster the ant colony’s search capability and avert convergence to local optima, this study integrates a reward and punishment mechanism in the pheromone update process. This mechanism is coupled with adaptive adjustments of the pheromone evaporation factor based on iteration count. Empirical tests and analysis validate the algorithm’s efficacy and superior performance in AGV path planning.
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