基于改进BP神经网络的食品分拣机器人视觉伺服控制方法  

Visual servo control method of food sorting robot based on improved BP neural network

作　　者：余晓兰[1] 万云[1] 陈靖照 YU Xiao-lan;WAN Yun;CHEN Jing-zhao(Chongqing City Vocational College,Chongqing 402160,China;Zhengzhou University,Zhengzhou,Henan 450000,China)

机构地区：[1]重庆城市职业学院,重庆402160 [2]郑州大学,河南郑州450000

出　　处：《食品与机械》2021年第8期126-131,135,共7页Food and Machinery

基　　金：重庆市教委科学技术研究项目(编号:KJQN201803901)。

摘　　要：目的:解决目前食品分拣机器人的视觉伺服控制系统结构复杂、计算量大,无法满足分拣机器人对视觉伺服控制系统的灵活性和适应性的问题。方法:在机器人视觉伺服控制系统结构的基础上,提出一种将改进粒子群算法与BP神经网络相结合的食品分拣机器人视觉伺服控制方法。粒子群算法在迭代过程中使用交叉和变异来保持种群多样性,对BP神经网络的初始权值和阈值进行优化。结果:与常规控制方法相比,该控制方法可以在较短的时间内将食品生产线机器人带到预定位置,位置逼近的相对误差为0.38%。结论:该控制方法在处理较复杂的任务时,具有较强的适应性,有一定的实用价值。Objective:To solve the problems of complex structure and large calculation amount of the visual servo control system of the current food sorting robot,which unable to meet the flexibility and adaptability of the sorting robot to the visual servo control system.Methods:Based on the structure of robot visual servo control system,a visual servo control method of food sorting robot based on improved particle swarm optimization algorithm and BP neural network was proposed.The particle swarm algorithm used crossover and mutation in the iterative process to maintain population diversity,the initial weight and threshold of BP neural network were optimized.Results:Compared with conventional control methods,the control method could bring the food production line robot to the predetermined position in a short time,the relative error of position approximation was 0.38%.Conclusion:When dealing with more complicated tasks,with strong adaptability,it has certain practical value.

关 键 词：食品 分拣机器人 视觉伺服 BP神经网络 粒子群优化算法 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP183[自动化与计算机技术—控制科学与工程]