水下灵巧手指端力传感器及静态标定方法

指端力传感器是水下灵巧手实现复杂作业的关键,开展了基于圆筒结构的指端力传感器研究。由于指端力传感器的结构特点,采用通常六维力静态标定方法较困难,且精度无法保证。因此,提出基于径向基函数(RBF)神经网络的静态标定方法,对标定装置、标定过程设计进行了研究。以研制的指端力传感器为对象进行了静态标定试验。结果表明:使用基于RBF神经网络的静态标定方法有效地保证了传感器测试精度,可满足目前水下灵巧手研究要求。结果证明了该方法的可行性和有效性。

Fingertip force sensor for underwater dexterous hand and static calibration method

The fingertip force sensor is the key for the complex task of the dexterous underwater hand,so a fingertip force sensor based on cylinder elastic body is developed.It is difficult to employ the accustomed calibration method for the characteristic of the fingertip force sensor,and the accustomed method is not able to assure the precision.A calibration method based on radial-basis function(RBF) neural network is introduced.Furthermore,the calibration system and program are also designed.The calibration experiment of the sensor is carried out.The results show the nonlinear calibration method based on RBF neural network can assure the precision of the sensor,which meets the demand of research on the underwater dexterous hand.The feasibility and validity of the method are proved.