融合机器视觉与粒子群优化算法的智能制造生产线机械臂抓取定位  

Intelligent manufacturing production line robotic arm grasping and positioning integrating machine vision and particle swarm optimization algorithm

作　　者：袁学卫 YUAN Xuewei(Zhejiang Zaili Technology Co.,Ltd.,Hangzhou 310000,China)

机构地区：[1]浙江载力科技有限公司,杭州310000

出　　处：《高科技与产业化》2024年第12期67-69,共3页High-Technology & Commercialization

摘　　要：由于复杂环境中的多变性及不确定性,导致机械臂在定位与抓取任务中常出现较大偏差。为了解决上述问题,提出融合机器视觉与粒子群优化算法的智能制造生产线机械臂抓取定位研究。利用高精度传感器捕获生产线图像,经多层预处理与边缘检测,结合Hu矩与形态学特征的模板匹配,识别图像中的目标。采用增强的离散粒子群优化算法,通过双态粒子与局部强化学习,避免局部最优,规划最优抓取路径。标定相机后,机械臂依据路径精准定位,动态优化末端执行器姿态,完成目标物体的精确抓取与安全放置。实验结果表明,研究方法在复杂环境中展现出更高的定位精准度与稳定性,显著提升定位精度,为实际应用提供了有力支持。Due to the variability and uncertainty in complex environments,robotic arms often experience significant deviations in positioning and grasping tasks.In order to solve the above problems,a research on the grasping and positioning of robotic arms in intelligent manufacturing production lines by integrating machine vision and particle swarm optimization algorithms is proposed.Capture production line images using high-precision sensors,perform multi-layer preprocessing and edge detection,and combine template matching with Hu moments and morphological features to identify targets in the images.Adopting an enhanced discrete particle swarm optimization algorithm,using binary particles and local reinforcement learning to avoid local optima and plan the optimal grasping path.After calibrating the camera,the robotic arm accurately locates based on the path,dynamically optimizes the posture of the end effector,and achieves precise grasping and safe placement of the target object.The experimental results show that the research method exhibits higher positioning accuracy and stability in complex environments,significantly improving positioning accuracy and providing strong support for practical applications.

关 键 词：机器视觉 粒子群优化算法 智能制造生产线 机械臂控制 抓取定位 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]