基于深度学习的摘钩机器人手爪位置控制研究  

Research on Claw Position Control of Unhooked Robot Hand Based on Deep Learning

作　　者：贾军祥 蔡茁 杜宗达 朱立学 陈禹桥 JIA Junxiang;CAI Zhuo;DU Zongda;ZHU Lixue;CHEN Yuqiao(Liaoning Datang International Huludao Thermoelectric Co.,Ltd.,Huludao,Liaoning 125000,China)

机构地区：[1]辽宁大唐国际葫芦岛热电有限责任公司,辽宁葫芦岛125000

出　　处：《移动信息》2025年第2期347-349,共3页Mobile Information

摘　　要：摘钩机器人在自动化生产线中的应用日益广泛,尤其在物流和制造业中具有广阔的应用前景.摘钩任务要求机器人手爪具备高精度的空间位置控制能力,以应对复杂的工作环境,而传统的手爪控制方法往往难以应对多变的环境和多样化的目标物体.文中提出了一种基于深度学习的摘钩机器人手爪位置控制模型,利用深度卷积神经网络(Deep Convolutional Neural Network,DCNN)与强化学习算法,构建了一种适应性强、精度高的手爪位置控制系统.此外,该系统还使用了改进的梯度下降算法来优化模型参数,以提高控制精度和响应速度.实验结果表明,该控制系统在复杂多变的场景中具有优异的定位精度和较快的响应速度,显著提高了摘钩机器人的操作性能和鲁棒性.Hook-picking robots are increasingly widely used in automated production lines,especially in logistics and manufacturing.Hook-picking tasks require robot grippers to have high-precision spatial position control capabilities to cope with complex working environments,while traditional gripper control methods are often difficult to cope with changeable environments and diverse target objects.This paper proposes a deep learning-based claw position control model for hook-picking robots.Using Deep Convolutional Neural Network(DCNN)and reinforcement learning algorithms,a hand claw position control system with strong adaptability and high accuracy is constructed.In addition,the system also uses an improved layer descent algorithm to optimize the model parameters to improve the control accuracy and response speed.Experimental results show that the control system has excellent positioning accuracy and response speed in complex and changeable scenes,which significantly improves the operation performance and robustness of the hook-picking robot.

关 键 词：摘钩机器人 手爪位置控制 深度卷积神经网络 强化学习 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]