基于改进粒子群的食品分拣机器人动态目标抓取控制方法  

Dynamic target grasping control method of food sorting robot based on improved particle swarm optimization

作　　者：王敏[1] 蒋金伟 曹彦陶 WANG Min;JIANG Jin-wei;CAO Yan-tao(Anhui Science and Technology Trade School,Bengbu,Anhui 233080,China;Changzhou Vocational and Technical College of Industry,Changzhou,Jiangsu 213164,China;Jiangsu University,Zhenjiang,Jiangsu 212013,China)

机构地区：[1]安徽科技贸易学校,安徽蚌埠233080 [2]常州工业职业技术学院,江苏常州213164 [3]江苏大学,江苏镇江212013

出　　处：《食品与机械》2022年第3期86-91,共6页Food and Machinery

基　　金：江苏省高校“青蓝工程”项目(编号:苏教师函[2019]31号)。

摘　　要：目的:解决目前食品生产线分拣机器人抓取操作不稳定、分拣效率低的问题。方法:基于高速并联食品分拣机器人的体系结构,提出了一种基于改进粒子群算法的多目标运动优化策略用于食品分拣机器人的动态目标抓取控制方法。协调抓取顺序和分拣轨迹建立最短路径模型,以末端加速度建立机构稳定性优化模型,并通过改进粒子群优化算法对目标进行优化。结果:通过试验进行了验证,在输送速度100 mm/s时,抓取成功率由96.8%提高到100%,分选速率由1.62个/s提高到1.98个/s。结论:该控制方法能有效提高食品分拣机器人的操作稳定性和分拣效率。Objective:To solve the problems of unstable grasping operation and low sorting efficiency of sorting robot in food production line.Methods:Based on the architecture of high-speed parallel food sorting robot,a multi-objective motion optimization strategy based on improved particle swarm optimization algorithm was proposed for the dynamic target grasping control method of food sorting robot.By coordinating the grasping sequence and sorting trajectory,the shortest path model was established.Establish the mechanism stability optimization model with the end acceleration,and optimized the target by improving the particle swarm optimization algorithm.Results:Through experimental verification,when the conveying speed was 100 mm/s,the grasping success rate was increased from 96.8% to 100%,and the sorting rate was increased from 1.62 to 1.98 s;.Conclusion:This control method can effectively improve the operation stability and sorting efficiency of the food sorting robot.

关 键 词：食品生产线 分拣机器人 抓取控制 粒子群 动态目标 

分 类 号：TS203[轻工技术与工程—食品科学] TP242[轻工技术与工程—食品科学与工程] TP18[自动化与计算机技术—检测技术与自动化装置]