The development of high-precision intelligent dental surgical robots addresses the inherent limitations

The development of high-precision intelligent dental surgical robots addresses the inherent limitations of manual techniques, such as physiological tremor, fatigue, and restricted vision. This research presents the design, implementation, and validation of a complete robotic system for restorative and implant dental surgery based on advanced artificial intelligence. The proposed robot features a hybrid parallel-serial kinematic architecture (a ۳-DOF Delta arm combined with a ۳-DOF serial wrist), integrated with high-precision actuators and a six-axis force/torque sensor. Its vision system fuses stereo intraoral camera images with cone-beam computed tomography (CBCT) data through a deep learning fusion network (FusionNet). The AI algorithms include tooth tissue segmentation with an improved Mask R-CNN (Dice accuracy of ۰.۹۶), optimal path planning using Deep Reinforcement Learning (PPO with attention mechanisms), and nonlinear adaptive impedance control for real-time adjustment of cutting force while ensuring safety. Extensive experiments on ۶۰ dental phantoms (anterior and posterior teeth) and in a simulated clinical environment demonstrated that the robot can perform cavity preparation, crown preparation, and implant osteotomy with an average positioning error of ۰.۱۱ ± ۰.۰۳ mm, contact force error of less than ۰.۰۴ N, and a success rate of ۹۷.۵%. Compared to the control group (expert surgeon), a ۶۵% improvement in accuracy, a ۵۰% reduction in unwanted healthy tissue removal, and a ۴۷% decrease in operative time were achieved. These results demonstrate the system's capability to revolutionize automated dental surgery with near-zero error and reduced reliance on operator experience.

کلیدواژه ها:

Dental surgical robot ، artificial intelligence ، precision control ، deep reinforcement learning ، adaptive impedance control