[1]王建强,黄开启,苏建华.基于前馈径向基网络的动态抓取参数估计方法[J].深圳大学学报理工版,2022,39(3):334-342.[doi:10.3724/SP.J.1249.2022.03334]
 WANG Jianqiang,HUANG Kaiqi,and SU Jianhua.Dynamic grasping parameter estimation based on feedforward radial basis function network[J].Journal of Shenzhen University Science and Engineering,2022,39(3):334-342.[doi:10.3724/SP.J.1249.2022.03334]
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基于前馈径向基网络的动态抓取参数估计方法()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第39卷
期数:
2022年第3期
页码:
334-342
栏目:
电子与信息科学
出版日期:
2022-05-16

文章信息/Info

Title:
Dynamic grasping parameter estimation based on feedforward radial basis function network
文章编号:
202203012
作者:
王建强1黄开启1苏建华2
1)江西理工大学电气工程与自动化学院,江西赣州 341000
2)中国科学院自动化研究所复杂系统管理与控制国家重点实验室,北京 100190
Author(s):
WANG Jianqiang1 HUANG Kaiqi1 and SU Jianhua2
1) School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, Jiangxi Province, P. R. China
2) The State Key Laboratory for Management and Control of Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P. R. China
关键词:
机器人技术运动估计核相关滤波卡尔曼滤波前馈径向基函数动态抓取
Keywords:
robot technology motion estimation kernel correlation filter Kalman filter feed-forward radial basis function dynamic crawling
分类号:
TP242;TP181
DOI:
10.3724/SP.J.1249.2022.03334
文献标志码:
A
摘要:
针对机器人抓取非匀速运动物体过程中需要估计目标运动参数的问题,提出一种基于前馈径向基网络的物体运动参数估计方法.该方法首先采用核相关滤波算法实时跟踪目标位置,建立目标的运动模型;然后,通过径向基网络预测目标的运动参数,动态调整卡尔曼滤波运动估计方程的采样时间,大幅降低了计算耗时,同时提高了机器人对运动参数估计的准确性;最后,利用UR5机器人开展了抓取平面自由运动物体的实验.结果表明,与前馈感知机网络相比,所提出方法将抓取所需的时间缩短了20%,能较好地解决机器人抓取物体过程中,由于物体运动或方向变化造成的抓取失败问题.
Abstract:
In this paper, we propose a motion estimation method based on a feed-forward radial basis network for grasping arbitrary moving objects. We first employ a kernel correlation filtering (KCF) algorithm to track the target position in real-time and establish the motion model of the target. Using the feed-forward radial base network, we then adjust the sampling time of the Kalman filter (KF) to predict the motion parameters of the target. Since that, we can reduce the computing time and improve the accuracy of the estimation of the motion parameters. Compared with the feed-forward perceptron network, the proposed method shortens the required time for grasping by 20%, which can avoid a failure grasp due to the arbitrary movement of the object in grasping.

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备注/Memo

备注/Memo:
Received: 2021-06-16; Accepted: 2021-10-13; Online (CNKI): 2022-04-21
Foundation: National Natural Science Foundation of China (51905240); Beijing Natural Science Foundation Fengtai Rail Transit Frontier Research Joint Fund (L201019)
Corresponding author: Professor HUANG Kaiqi. E-mail: kaiqi.huang@163.com
Citation: WANG Jianqiang,HUANG Kaiqi,SU Jianhua.Dynamic grasping parameter estimation based on feedforward radial basis function network [J]. Journal of Shenzhen University Science and Engineering, 2022, 39(3): 334-342.(in Chinese)
基金项目:国家自然科学基金资助项目(51905240);北京市自然科学基金-丰台轨道交通前沿研究联合基金资助项目(L201019)
作者简介:王建强(1997—),江西理工大学硕士研究生.研究方向:机器人.E-mail:1647743302@qq.com
引文:王建强,黄开启,苏建华.基于前馈径向基网络的动态抓取参数估计方法[J].深圳大学学报理工版,2022,39(3):334-342.
更新日期/Last Update: 2022-05-30