摘要
为了解决人机交互作业时操作人员不能准确感知末端接触力信息的问题,提出了一种基于GRNN信息融合的方法.基于主动端的力反馈手控器和从动端的遥操作机器人,搭建实验操作平台,以人为中心构建整个系统,同步采集手控器的姿态信号、手臂肌电信号以及末端机器人的速度、加速度和接触力信息训练GRNN,并将GRNN得到的预测力与真实力进行比较.结果显示,采用纸盒和泡沫板2种不同材料进行穿刺实验的均方误差值分别为0. 24和0. 16,泡沫板进行穿刺和切割2种不同作业得到的均方误差值分别为0. 16和0. 13,从而证明了所提方法的有效性.
In order to solve the problem that the operators can not accurately sense the contact force information in the teleoperation,an information fusion method based on GRNN(generalized regression neural network)was proposed.The teleoperation platform was built based on the force feedback hand controller in the active side and the teleoperation robot in the slave side.The operator in the whole system was considered as the center.The gesture signal of the hand controller,the electromyographic signal in the arm,the velocity,the acceleration and contact force information of the end robot were synchronously collected to train the GRNN.The prediction force by the GRNN was compared with the real contact force.The results show that the mean square errors in the puncture experiments with two different materials,the paper box and the foam board,are 0.24 and 0.16,respectively.The mean square errors in the puncture and cutting experiments with the foam board are 0.16 and 0.13,respectively.These prove the effectiveness of the proposed method.
作者
熊鹏文
雷耀
李鸣
Xiong Pengwen;Lei Yao;Li Ming(School of Instrument Science and Engineering,Southeast University,Nanjing 210096,China;School of Information Engineering,Nanchang University,Nanchang 330031,China)
出处
《东南大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2018年第6期1130-1136,共7页
Journal of Southeast University:Natural Science Edition
基金
国家自然科学基金资助项目(61663027)
中国博士后科学基金资助项目(2018M642136)
江西省自然科学基金资助项目(20181BAB211019)
江西省教育厅科学技术研究资助项目(60212)
江苏省博士后科研资助计划资助项目(2018K024A)
东南大学江苏省远程测控技术重点实验室开放基金资助项目(YCCK201602)
关键词
遥操作系统
人机交互
信息融合
GRNN
teleoperation system
human-computer interaction
information fusion
GRNN(generalized regression neural network)