Abstract: Stocktaking work is an important link in the invoicing system of industrial enterprises.Aiming at the current shortcomings of manual stocktaking work such as low efficiency and error-proneness,the technology of allowing drones to be equipped with high-precision portable radio frequency identification readers for stocktaking is emerged in recent years,and the trajectory planning is a key issue that needs to be resolved among them.To reduce energy consumption and improve inventory efficiency,the lowest energy efficiency ratio and time efficiency ratio were taken as the goal to construct the objective function,and the corresponding mathematical model of UAV route planning was established.In this model,a hybrid Differential Evolution (DE) Algorithm based on Life-cycle Swarm Optimization (LSO) algorithm was proposed.The actual environmental data of a raw and the auxiliary material warehouse of a tobacco industry company were used for physical modeling,and the simulation experiment was carried out by using the proposed intelligent optimization algorithm.In the same environment,compared with the other three cutting-edge trajectory planning algorithm,the experimental results showed that the route planned by the proposed algorithm could effectively improve the efficiency of UAV stocktaking.

Key words: unmanned aerial vehicle, stocktaking work, radio frequency identification, track planning in 3D environment, differential evolution algorithm, life-cycle swarm optimization algorithm

摘要： 盘库作业是各工业企业进销存系统中的重要环节,针对目前人工盘库工作效率低、容易出错等缺点,近年来出现了无人机搭载高精度便携式射频识别阅读器进行库存盘点的技术。其中,航迹规划是需要解决的关键问题。为减少能耗并提高盘库效率,以能效比最低以及时效比最小为目标来构造目标函数,建立了相应的无人机航迹规划数学模型,提出一种基于生命周期群搜索的混合差分进化算法。利用某烟草工业企业原辅物料仓库的实际环境数据进行物理建模,进一步利用所提出的智能优化算法进行仿真实验。最后,在相同的环境下,与其他3种前沿航迹规划算法进行对比,结果表明利用所提算法规划出的路径更能有效地提高无人机盘点效率。

关键词: 无人机, 盘库作业, 射频识别, 三维环境航迹规划, 差分进化算法, 生命周期群搜索算法