Laparoscope arm automatic positioning for robot-assisted surgery based on reinforcement learning

Abstract

Compared with the traditional laparoscopic surgery, the preoperative planning of robot-assisted laparoscopic surgery is more complex and essential. Through the analysis of the surgical procedures and surgical environment, the laparoscope arm preoperative planning algorithm based on the artificial pneumoperitoneum model, lesion parametrization model is proposed, which ensures that the laparoscope arm satisfies both the distance principle and the direction principle. The algorithm is divided into two parts, including the optimum incision and the optimum angle of laparoscope entry, which makes the laparoscope provide a reasonable initial visual field. A set of parameters based on the actual situation is given to illustrate the algorithm flow in detail. The preoperative planning algorithm offers significant improvements in planning time and quality for robot-assisted laparoscopic surgery. The improved method which combines the preoperative planning algorithm with deep deterministic policy gradient algorithm is applied to laparoscope arm automatic positioning for the robot-assisted laparoscopic surgery. It takes a fixed-point position and lesion parameters as input, and outputs the optimum incision, the optimum angle and motor movements without kinematics. The proposed algorithm is verified through simulations with a virtual environment built by pyglet. The results validate the correctness, feasibility, and robustness of this approach.