Study on Contact Force Prediction for the Vascular Interventional Surgical Robot based on Parameter Identification

With the development of science and technology, surgical robots have made it possible for doctors to perform remote operations, which has also brought good news to patients in remote areas. However, there are some factors that cause the problem of force feedback lag, such as network delay and network instability, so we can use the model-based force feedback prediction method. In this paper, the contact force is modeled in the master manipulator side, which is used to predict the contact force when the slave manipulator side contacts the real tissue. It can obtain better system transparency. In order to ensure the accuracy of the contact force model, autoregressive least squares method is used for parameter identification, so that the environmental model parameters can be identified in real time and the master side prediction model can be corrected. Experimental results indicated that this method can perform force prediction well.