Triplet-Fusion Self-Attention-Enhanced Pyramidal Convolutional Neural Network for Surgical Robot Kinematic Solution

Surgical robots are increasingly utilized in medicine for their reliability and convenience. An accurate kinematic model is essential for precise robot control and enhanced surgical safety. However, the high nonlinearity and computational complexity of kinematics pose significant challenges to traditional numerical methods. This study designs a surgical robotic arm and establishes the motion mapping relationship between the joint space and the end-effector workspace. Subsequently, a hybrid kinematic estimation model based on deep pyramid convolutional neural network (DPCNN) is proposed, which integrates data sampling and an attention mechanism to improve computational accuracy. The Latin hypercube sampling technique is used to improve the uniformity of dataset sampling, and the triplet-fusion self-attention mechanism (TFSAM) is employed for multi-scale feature information. Experimental results show that the TFSAM-DPCNN model achieves coefficient of determination (R2) values exceeding 0.99 across all testing scenarios. Compared with other models, the proposed model reduced the root mean square error (RMSE) by up to 81.34%, exhibiting superior performance. Furthermore, the developed 3D simulation platform validates the effectiveness of the proposed model. This study offers a robust solution for multi-degree-of-freedom robot modeling, with potential applications across a range of robotic motion control systems.