基于几何特征约束的机器人辅助骨科手术护理路径优化研究OA

Objective:An optimization model based on geometric feature constraints is proposed to address the problem of nursing path optimization in robot-assisted orthopedic surgery.Method:A three-layer neural network model is developed,where simulated annealing is used to initialize the network weights to avoid local minima,and backpropagation is employed to adjust the weights for path optimization.Results:The model can achieve optimal performance when the hidden layer nodes is 20 and the learning rate is 0.0008,with a mean absolute error of 0.214,a root mean square error(RMSE)of 0.286,and a mean absolute percentage error(MAPE)of 0.352.After three rounds of path correction,the final path accuracy reaches 0.005 mm for the left lamina and 0.006 mm for the right lamina,with a real-time correction response time of 1.12 seconds.Conclusion:The model proposed in this study is an effective approach to optimizing the nursing path in robot-assisted orthopedic surgery.