基于数学建模的PSO-DE算法在机器人智能拣货过程中的应用研究  

The Application of PSO-DE Algorithm Based on Mathematical Modeling in Robot Intelligent Picking Process

作　　者：李云娟[1] 樊雪双[1] LI YUNjuan;FAN XUEshuang(Xi’an Siyuan University,Xi’an 710038,China)

机构地区：[1]西安思源学院,西安710038

出　　处：《自动化与仪器仪表》2024年第3期197-200,205,共5页Automation & Instrumentation

基　　金：陕西省“十四五”教育科学规划2022年度课题《应用型本科高校大学数学微课的优化设计与应用-以西安思源学院为例》(SGH22Y1860)。

摘　　要：随着互联网技术的发展,网络电商发展迅速。为更好地实现智能仓储系统中商品订单的分拣,提高拣货效率,研究基于粒子群算法和双重编码方式构建拣货机器人的智能数学模型。针对粒子群算法在实现过程中存在的问题,研究引入交叉算法对其进行改进。实验结果显示,研究提出的基于改进粒子群算法的双重编码智能机器人拣货算法在训练集上的F1值为0.925,显著高于另外几种方法。由此说明,研究构建的智能机器人拣货数学模型可以更好地实现智能仓储系统中商品的订单的拣货效率,全面提高商品订单的处理能力,实现资源的优化利用。With the development of internet technology,online e-commerce has developed rapidly.To better implement the picking of commodity orders in intelligent warehousing systems and improve picking efficiency,an intelligent mathematical model for building picking robots based on particle swarm optimization algorithm and dual coding method is studied.In response to the problems in the implementation process of particle swarm optimization algorithm,a cross algorithm is introduced to improve it.The experimental results show that the F1 value of the dual encoding intelligent robot picking algorithm based on improved particle swarm optimization algorithm proposed in the study is 0.925 on the training set,which is significantly higher than other methods.This indicates that the intelligent robot picking mathematical model can better achieve the picking efficiency of goods orders in intelligent warehousing systems,comprehensively improve the processing ability of goods orders,and achieve optimal utilization of resources.

关 键 词：数学建模 粒子群算法 双重编码方式 机器人拣货 智能仓储系统 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]