Estimation of the wrist torque of robot gripper using data fusion and ANN techniques

This paper proposes a kind of measurement method of wrist force/torque for robotic multi-sensor gripper. The method adopts the data fusion technique according to the output variations of the finger force sensors installed in the gripper. The finger force sensors are used to measure the clamping force of the gripper in the design. When the accuracy of measurement is not required exactly and there is the limitation of weight and volume in space robots, we utilize the existing devices to estimate the wrist force/torque without the wrist force/torque sensor, which not only meets the practical requirement but also decreases the weight and cost of robots. An experimental bench is developed and the calibration experiments are conducted to detect the relationship between the wrist force/torque and finger forces. The experimental data are used to train three layers of radial basis function (RBF) artificial neural network and the structure and weight values of the network are obtained. Comparing with BP network and single RBF network based on the same samples, this method can reduce the training time greatly, and increase the estimation accuracy significantly. The results of data fusion of the wrist force/torque are cons technique is proved. Istent with the practical calibration values, and the effectiveness of the wrist force/torque estimating.