基于前馈径向基网络的动态抓取参数估计方法

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

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

Dynamic grasping parameter estimation based on feedforward radial basis function network
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

robot technology; motion estimation; kernel correlation filter; Kalman filter; feed-forward radial basis function; dynamic crawling

DOI: 10.3724/SP.J.1249.2022.03334

备注

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.
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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