Rancang Bangun Kendali Path Planning Pada As/Rs Dengan Rak Modular Menggunakan Metode Ant Colony Optimization

The global industry is currently developing rapidly, especially in the manufacturing and logistics sectors. One crucial implementation of automation technology in this sector is material handling systems, such as the Automated Storage and Retrieval System (AS/RS). AS/RS automatically stores and retrieves items from multi-tier racks, thereby easing human labor and reducing human error. However, current AS/RS implementations generally lack an efficient path optimization method. This study aims to design and develop an AS/RS prototype by integrating a path planning method based on the Ant Colony Optimization (ACO) algorithm to optimize the manipulator's trajectory and serve as an industrial automation learning medium. Based on the experimental results, the designed mechanical subsystem achieves a positioning accuracy of 99.25%. Meanwhile, artificial intelligence testing with the best combination of ACO parameters (*m* = 15, *k* = 20, *α* = 2, and *ρ* = 0.5) successfully produces a trajectory accuracy of up to 95%, approaching the global optimum. The implementation of this algorithm provides a significant efficiency increase, yielding an average distance efficiency of 41.10% and an average time efficiency of 52.01%. The difference in these efficiency values is influenced by the velocity profile characteristics and cycle times during the mechanical process. The analysis shows that as the rack matrix, capacity, and search space dimensions used in the system expand, the resulting efficiency scales up continuously.