食品与机械2025，Vol.41Issue(3)：88-93,6.DOI:10.13652/j.spjx.1003.5788.2024.60146
基于改进DBO和多目标模型的食品分拣机器人分拣策略
Sorting strategy of food sorting robot based on improved DBO and multi-objective model
摘要
Abstract
[Objective]To enhance the operational efficiency and stability of Delta robots in food automation sorting systems.[Methods]Based on an analysis of food automation sorting systems,a Delta robot sorting strategy combining machine vision,a multi-objective model,and an improved dung beetle optimizer algorithm was proposed.Machine vision was used to obtain real-time food position data,and a sorting optimization model was established with the goal of achieving the optimal balance between running path efficiency and stability.The model was solved using an improved mantis optimization algorithm,and the performance of the proposed method was experimentally verified.[Results]Compared with conventional methods,the proposed method achieved a faster average sorting speed and lower end vibration impact,with an average sorting speed of<0.60 pieces/s and an average end acceleration of<16 m/s2.[Conclusion]The integration of machine vision,a multi-objective model,and intelligent algorithms can effectively improve the sorting efficiency and stability of Delta robots.关键词
自动化/分拣系统/Delta机器人/机器视觉/多目标模型/蜣螂优化算法Key words
automation/sorting system/Delta robot/machine vision/multi-objective model/dung beetle optimizer algorithm引用本文复制引用
傅明娣,李忠,王倩茹,赵飞..基于改进DBO和多目标模型的食品分拣机器人分拣策略[J].食品与机械,2025,41(3):88-93,6.基金项目
江苏省自然科学基金项目(编号:22JS135107) （编号:22JS135107）
江苏省教育教学改革研究课题(编号:ZYB530) （编号:ZYB530）
