Research On Virtual Training Simulation And Autonomous Learning Of Minimally Invaise Surgery Robot Operation

Compared with the traditional abdominal minimally invasive surgery,the abdominal minimally invasive surgery robot has the advantages of less trauma,faster postoperative recovery,and easier implementation.The establishment of a virtual operation training simulation platform for minimally invasive surgery robot is of great significance to improve the operation skills of doctors,shorten the learning curve,and test new medical devices and control strategies.At present,the surgical operation training simulation system used provides a platform for surgical operation training,but it does not model and simulate the robot body.Based on the first generation of open-source da Vinci Surgical Robot(d VRK),this paper models and simulates the robot in the software V-REP,which is of great significance to improve doctors' operation skills and research on robot's control strategy.In addition,the breakthrough in the field of deep learning(DL)and reinforcement learning(RL)endows robots with more intelligence and makes it possible for robots to operate and learn autonomously.Based on the established simulation platform,this paper studies surgical task automation,which is vital to improving the efficiency of surgery.Firstly,based on the D-H parameters,this paper establishes the kinematics model of d VRK robot and the robot simulation platform.On the basis of analysis of the configuration of master hand and slave robot,a "heterogeneous" master-slave motion mapping strategy is developed through Jacobian matrix.In order to enhance the sense of immersion,a VR helmet is used as the control end and image output end of the robot endoscope arm.The operator can control the movement of the endoscope at the end of the arm through the VR helmet,and obtain the vision of the endoscope.Two independent threads are used to deal with motion mapping and image transmission respectively,which improves the smoothness of operation.Based on the simulation platform,a task environment for reinforcement learning training is established.In order to solve the problem of low learning efficiency of robot in the environment under sparse reward function,the paper designs HER algorithm based on Deep Deterministic Policy Gradient(DDPG)and the idea of Hindsight Experience Replay(HER),which effectively solves the problem of low learning efficiency of robot in the environment.Aiming at the problem of large searching space and slow search in multi-steps environment,the BC + DDPG algorithm is designed by combining Behavior Cloning(BC)in imitation learning with reinforcement learning,which improves the success rate of the task in the multi-steps environment.Finally,the master-slave motion control experiment is carried out for the simulation platform.The maximum error between the theoretical trajectory and the actual trajectory is 2.3mm.Two kinds of task testing environments are designed and tested for the operators without experience.It is proved that the master-slave control strategy is intuitive and easy to operate.Two kinds of task environments are designed for autonomous learning of robot.Experiments are carried out in the two task environments to verify the effectiveness of the algorithms.