Estimation of Wrist Force/torque for Robot Gripper using Neural Network

This paper proposes a kind of estimation method of wrist force/torque for robots. 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 a Back Propagation (BP) artificial neural network, and the construction and parameters of the network are obtained. The results of data fusion of the wrist force/torque are consistent with the practical calibration values, and the effectiveness of the wrist force/torque estimating technique is proved.