Intelligent grasping and surgical assistant model of medical robot based on neural network and reinforcement learning

In this paper, an intelligent grasping and surgical assistant model of medical robot based on neural network and reinforcement learning (RL) is proposed, which breaks through the limitation of traditional medical robot's lack of adaptability in complex surgical environment and improves the accuracy and safety of surgery. The model adopts three-level architecture: the multi-modal perception layer fuses the data of RGB-D vision, force sensor and joint encoder to generate a 3D semantic segmentation map of the surgical scene and the contact force field of the instrument; RL decision layer constructs a dual-network architecture based on PPO algorithm to optimize the trajectory of 7-DOF manipulator online. The hybrid control layer fuses the doctor's operation instructions and the independent decision-making output through confidence weighting, and dynamically adjusts the control weight. Multi-modal neural network model combines visual, force feedback and joint angle information, and fuses multi-source data through cross-modal attention mechanism to improve decision-making quality. RL model guides agent learning through safety constraint reward function and optimizes surgical operation strategy. The experimental platform adopts da Vinci Xi surgical robot, ATI Mini45 six-axis force sensor and Intel RealSense D435i depth camera, and builds a simulation environment based on PyBullet. A variety of surgical scenes were designed by contrast experiments, including organ suturing, sudden bleeding treatment and narrow space operation. It was verified that this method was superior to traditional PID control, RL model based on LSTM and 3D CNN visual servo control method in terms of completion time, injury rate, force error and path optimization. Digital twin verification and live pig experiments further prove the stabilit y and effectiveness of this method under complex conditions, significantly reducing the amount of surgical bleeding and improving the postoperative survival rate.