[1]张少白,施梦甜.基于协同控制的手臂移动与抓取神经网络研究[J].计算机技术与发展,2019,29(10):146-152.[doi:10. 3969 / j. issn. 1673-629X. 2019. 10. 029]
　ZHANG Shao-bai,SHI Meng-tian.Research on Neural Network Model with Postural Synergies during Reach to Grasp[J].,2019,29(10):146-152.[doi:10. 3969 / j. issn. 1673-629X. 2019. 10. 029]
点击复制

基于协同控制的手臂移动与抓取神经网络研究()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年10期
页码:
146-152
栏目:
智能、算法、系统工程
出版日期:
2019-10-10

文章信息/Info

Title:
Research on Neural Network Model with Postural Synergies during Reach to Grasp
文章编号:
1673-629X(2019)10-0146-07
作者:
张少白; 施梦甜
南京邮电大学 计算机学院,江苏 南京 210023
Author(s):
ZHANG Shao-bai; SHI Meng-tian
School of Computer,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
关键词:
抓取运动; 神经网络; 手势协同; 逆运动学; 拟人机械手
Keywords:
reaching and grasping; neural network; gesture synergy; inverse kinematics; anthropomorphic manipulator
分类号:
TP18
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 10. 029
摘要:
针对手势抓取的多自由度和抓取模式的灵活复杂特性,提出了一种面向任务的手臂移动与手势抓取神经网络模型。 基于人手抓取动作数据,运用一种描述手部姿势的协同值提取策略,仅用三个协同系数定义手势抓取演变过程,进而构建面向任务和物体几何特征的协同系数神经网络认知模块,实现拟人机械手的抓取模式规划。 模型还将抓取运动细分为手臂移动、手指预成型和手掌朝向三大通道,利用 VITE(vector integration to endpoint)点对点运动轨迹计算模型动态更新运动命令,并应用小脑逆向内模处理手臂运动过程中的经典动态逆问题,完成手势空间位置到关节角度的转换。 实验对神经网络的有效性与抓取系统的协调性进行了验证,结果表明,以上方法构建的手势抓取模型具有良好的适用性与抓取特性。
Abstract:
In view of the multiple degrees of freedom in gesture grasping and flexibility or complex characteristics of prehension pattern,a task-oriented neural network model for arm movement and hand prehension is proposed. Based on the data of hand grasping movements,a collaborative control strategy for gesture description is applied,using three synergies coefficients to define the evolution process of grasping,and thus building a feedforward neural network which combines task demands and geometric features of objects to realize humanoid manipulator grasp mode planning. The model also divides the prehension motion into three main channels:arm movement,hand preshaping and palm orientation. VITE (vector integration to endpoint) point-to-point trajectory generation model is used for movement command updating,and the inverse dynamic arm model is processed by the cerebellar inverse internal model to complete theconversion from gesture space to joint angle. The validity of neural network and the coordination of grasping system are verified by experiments. Simulation shows that the gesture grasping model constructed by the above method has great applicability and grasping characteristics.

相似文献/References:

[1]路川 胡欣杰.区域航空市场航线客流量预测研究[J].计算机技术与发展,2010,(04):84.
　LU Chuan,HU Xin-jie.Analysis of Regional Airline Passenger Forecast Title[J].,2010,(10):84.
[2]高峥 陈蜀宇 李国勇.混合入侵检测系统的研究[J].计算机技术与发展,2010,(06):148.
　GAO Zheng,CHEN Shu-yu,LI Guo-yong.Research of a Hybrid Intrusion Detection System[J].,2010,(10):148.
[3]包力伟 周俊.铸锻企业生产质量控制系统的开发[J].计算机技术与发展,2008,(04):174.
　BAO Li-wei,ZHOU Jun.Development of a Manufacture Quality Control System in Casting Company[J].,2008,(10):174.
[4]李志俊 程家兴 金奎 饶玉佳.基于样本期望训练数的BP神经网络改进研究[J].计算机技术与发展,2009,(05):103.
　LI Zhi-jun,CHENG Jia-xing,JIN Kui,et al.BP Algorithm Improvement Based on Sample Expected Training Number[J].,2009,(10):103.
[5]李龙澍 葛瑞峰 王慧萍.基于神经网络的批强化学习在Robocup中的应用[J].计算机技术与发展,2009,(07):98.
　LI Long-shu,GE Rui-feng,WANG Hui-ping.Application of Batch Reinforcement Learning Based on NN to Robocup[J].,2009,(10):98.
[6]贾志先.神经网络在空白试卷识别中的应用[J].计算机技术与发展,2009,(08):208.
　JIA Zhi-xian.Application of Neural Network in Recognization Blank Examination Paper[J].,2009,(10):208.
[7]肖宜龙 路游 亓永刚.基于神经网络的NURBS曲面重建[J].计算机技术与发展,2009,(09):65.
　XIAO Yi-long,LU You,QI Yong-gang.NURBS Surface Reconstruction Based on Neural Network[J].,2009,(10):65.
[8]蔡秋茹 罗烨 柳益君 叶飞跃.企业资信的BP神经网络评估模型研究[J].计算机技术与发展,2009,(10):117.
　CAI Qiu-ru,LUO Ye,LIU Yi-jun,et al.Research on BP Neural Network Model for Corporation Credit Rating[J].,2009,(10):117.
[9]王晓敏 刘希玉 戴芬.BP神经网络预测算法的改进及应用[J].计算机技术与发展,2009,(11):64.
　WANG Xiao-min,LIU Xi-yu,DAI Fen.Improvement and Application of BP Neural Network Forecasting Algorithm[J].,2009,(10):64.
[10]崔海青 刘希玉.基于粒子群算法的RBF网络参数优化算法[J].计算机技术与发展,2009,(12):117.
　CUI Hai-qing,LIU Xi-yu.Parameter Optimization Algorithm of RBF Neural Network Based on PSO Algorithm[J].,2009,(10):117.

更新日期/Last Update: 2019-10-10