Hybrid grey prediction model-based autotracking algorithm for the laparoscopic visual window of surgical robot

Abstract An autotracking algorithm based on a hybrid grey prediction model is presented for autonomously navigating the laparoscopic visual window of a robot-assisted surgical system. This method can be applied to any view angle of the three-dimensional (3D) laparoscope with a 200 ms predictive motion. Firstly, a preset parameter-based tracking algorithm is proposed based on the kinematic relationships between instrument arms and laparoscope arm. Subsequently, a hybrid grey prediction model is constructed through the combination of the optimized GM(1,1) and grey Verhulst models with the use of an adaptive weight-tuning method and a filtered amendment method. Furthermore, the algorithm results in a constant distribution area ratio, whereby the instrument marks can be guaranteed to lie within the field of the visual window, such that the concurrent motion of the visual window and the instrument marks can be realized. The visual window can sustain automatic tracking of the movement of the marks. The user does not have to switch to the target controlling mode by adjusting the master-slave mapping. Finally, the proposed algorithm is verified through simulations with real motion trajectories from a Phantom Omni master manipulator. The results validate the correctness, feasibility, and robustness of this approach.