Implementation of Path Planning Algorithm for AGV in Smart Warehouse

The inefficiency of traditional warehouse systems can lead to a 20% decrease in revenue and a 30% reduction in storage capacity within five years. Therefore, this study aims to design an AGV (Automated Guided Vehicle) capable of picking up and storing assets in the warehouse. The design focuses on enabling the AGV to choose the most optimal route. To achieve this, the method used in this research involves creating a warehouse map model using graph theory and employing the A* and D* algorithms for route selection. The heuristic value in both algorithms is calculated using Euclidean distance in addition to AGV turning costs. The findings demonstrate that, within the confines of the developed warehouse model, the D* algorithm perform slightly better than A* algorithm by approximately 1.02%. These results offer valuable insights into the efficacy of the D* algorithm in conjunction with graph theory mapping for warehouse navigation. By revealing the slight performance advantage of D* over A* in a static warehouse environment, this research contributes valuable insights for Industry 4.0 implementation. These findings can inform the selection of optimal pathfinding algorithms in warehouse automation, ultimately leading to improved efficiency and competitiveness within Indonesia's logistics sector.