陈小静, 彭培成, 张高峰,等.基于BP神经网络的仿人两指末端执行器抓握模式预测[J].河南理工大学学报（自然科学版）,2020,39(2):97-102.

CHEN X J, PENG P C, ZHANG G F,et al.Prediction of humanoid two-finger end-effector grasp type based onBP neural network[J].Journal of Henan Polytechnic University(Natural Science) ,2020,39(2):97-102.

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, 彭培成, 张高峰, 王裕清

河南理工大学 机械与动力工程学院，河南焦作454000

摘要:为了得到手部特征及物体特征与两指抓握模式之间的非线性映射关系，以便对仿人两指末端执行器的抓握模式进行预测，采用5 554次人手拇指-食指成功抓握试验的数据作为训练样本，构建基于L-M算法的BP神经网络两指抓握模式预测模型，进行仿人两指末端执行器的抓握模式预测。结果表明：该神经网络模型的预测准确率达90% ，预测值与实测值的相关系数为0. 83，能够快速有效地预测仿人两指末端执行器的抓握模式；对于等效直径较小且质量较轻的目标物，多选择精密捏；对于等效直径较大且质量较重的目标物，多选择强力握。研究结果可为仿人两指末端执行器的稳定抓握控制提供重要的决策依据。

关键词:抓握模式;L-M算法;BP神经网络;两指末端执行器

doi:10.16186/j.cnki.1673-9787.2020.2.14

基金项目:国家自然科学基金资助项目（U1261115 ）

收稿日期:2019/04/28

修回日期:2019/06/06

出版日期: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