UAV Stocktaking Task-Planning for Industrial Warehouses Based on the Improved Hybrid Differential Evolution Algorithm

Tobacco industry companies need to conduct a regular inventory of finished products and raw and auxiliary materials, and drones with radio frequency identification (RFID) readers are becoming a major application trend of inventory. Under the condition of ensuring the accuracy of inventory, this article considers the physical performance constraints of the drone, the constraints of the RFID reader, etc., and introduces the force of the drone into the model establishment, and a task planning model for UAV inventory library equipped with RFID reader is proposed. Then, in view of the problem that the greedy strategy in the traditional differential evolution (DE) algorithm will cause the location information retained by other individuals to be lost, a hybrid DE algorithm based on the lion swarm optimization is proposed. Finally, the proposed algorithm was verified by environmental modeling based on the data of the tobacco enterprise warehouse.

Automated summary

This article discusses the importance of using drones equipped with RFID readers for inventory management in the tobacco industry. It proposes a task planning model for UAV inventory library and introduces a hybrid algorithm based on lion swarm optimization to address the limitations of traditional algorithms. The proposed algorithm is validated through environmental modeling using real data from a tobacco warehouse.