doi:10.16186/j.cnki.1673-9787.2020.2.14

Received:2019/04/28

Revised:2019/06/06

Published:2020/03/15

Prediction of humanoid two-finger end-effector grasp type based onBP neural network

CHEN Xiaojing, PENG Peicheng, ZHANG Gaofeng, WANG Yuqing

School of Mechanical and Power Engineering， Henan Polytechnic University Jiaozuo 454000 ， Henan，China

Abstract:The nonlinear mapping relationship between hand and object characteristics and thumb-index finger grasp type was studied to predict the grasp type for humanoid two-finger end-effector. The data from 5 554 thumb-index finger success grasping trails were used as the training samples， the predicting model of two-finger grasp type was established by BP neural network with L-M algorithm， and the grasp type for humanoid two-finger end-effector was predicted. The results showed that the accuracy of BP neural network model with L-M algorithm was 90% ， and the correlation coefficient between predicted value and observed value was 0. 83. The grasp type for humanoid two-finger end-effector could be predicted fast and effectively ： the precision-pinch was more likely to be chosen for the small equivalent diameter and light objects， otherwise the power-grasp was more likely to be chosen for the large equivalent diameter and heavy objects. The study provided a helpful reference of decision-making for the stable grasp control of humanoid two-finger end-effector.

Key words:grasp type;L-M algorithm;BP neural network;two-finger end-effector

基于BP神经网络的仿人两指末端执行器抓握模式预测_陈小静.pdf