STP-Net-Based Respiratory Prediction Method for Autonomous Laminectomy Surgical Robots

To address force signal distortion and neural injury risks caused by respiratory-induced spinal displacement coupled with system control delays (>200 ms) in robot-assisted laminectomy surgery, this study proposes a Swin Transformer-based Prediction Network (STP-Net). STP-Net replaces the standard Transformer with a shifted window multi-head self-attention mechanism (alternating W-MSA and SW-MSA), reducing computational complexity from to . The encoder-decoder architecture incorporates hierarchical downsampling and future-masking to extract global features from long sequences. Experimental results demonstrate that for 150-frame inputs predicting 75 frames (≈300 ms), STP-Net achieves a prediction error (MSE = 3.35 × 10 -3 , MAE = 2.97 × 10 -2 ) - 76% and 66% lower than LSTM and Informer, respectively. With a per-frame inference latency of 8.4 ms, the total closed-loop delay is <25 ms. In the closed-loop respiratory compensation experiment, the motion trajectories of the active and passive arms showed an exceptional correlation (r = 0.9996), while end-effector contact force fluctuations were reduced to merely 1.6 N. This approach achieves high-accuracy and low-delay respiratory compensation in spinal surgery, providing a critical technical foundation for enhancing the safety of autonomous surgical robots.