Research on Force Visual Perception of Bevel-Tip Needle Based on BP Neural Network

Needle insertion is a minimally invasive treatment technique. During the surgery, the insertion path needs to be planned in advance to manipulate the flexible needle to avoid nerves and organs. In order to predict the insertion trajectory, a force-visual perception prediction model based on the BP neural network is established. Through the force analysis of the flexible needle, the displacement L of the needle holder and the reaction force Fr, insertion force F and torque M on the needle holder are used as the force visual perception model. Input to predict the trajectory of the needle tip during insertion. Through experiments on three different types of flexible needles, data are collected to train the model. The lowest mean absolute error (MAE) of the model is 0.7490, the correlation coefficient R is between 0.99962 and 0.99996, and the accuracy is high. The force visual perception model provides a feasible prediction of the needle tip trajectory. The results show that the displacement of the needle tip in the X and Y directions predicted by the model is basically consistent with the experimental results, and the insertion trajectory can be predicted more accurately.