HAC-based adaptive combined pick-up path optimization strategy for intelligent warehouse

Smart warehousing has been widely used due to its efficient storage and applications. However, the efficiency of transporting high-demand goods is still limited, because the existing methods lack path optimization strategies applicable to multiple scenarios and are unable to adapt conflict strategies to different warehouses. For solving these problems, this paper considers a multi-robot path planning method from three aspects: conflict-free scheduling, order picking and collision avoidance, which is adaptive to the picking needs of different warehouses by hierarchical agglomerative clustering algorithm, improved Reservation Table, and Dynamic Weighted Table. Firstly, the traditional A* algorithm is improved to better fit the actual warehouse operation mode. Secondly, the reservation table method is applied to solve the head-on collision problem of robots, and this paper improves the efficiency of the reservation table by changing the form of the reservation table. And the dynamic weighted table is added to solve the multi-robot problem about intersection conflict. Then, the HAC algorithm is applied to analyse the goods demand degree in current orders based on historical order data and rearrange the goods order in descending order, so that goods with a high-demand degree can be discharged from the warehouse in the first batch. Moreover, a complete outbound process is presented, which integrates HAC algorithm, improved reservation table and dynamic weighting table. Finally, the simulation is done to verify the validity of the proposed algorithm, which shows that the overall transit time of high-demand goods is reduced by 21.84% on average compared to the “A* + reservation table” algorithm, and the effectiveness of the solution is fully verified.