@Article{cmes.2026.085095,
AUTHOR = {Wei Wei, Shujuan Li, Legend Zhang, Junmin Lyu, Qi Hu, Wenfeng Zheng, Bo Yang},
TITLE = {LSTM-Enhanced Deep Reinforcement Learning for Active Motion Compensation of Surgical Robots with Known Target Position},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {148},
YEAR = {2026},
NUMBER = {2},
PAGES = {--},
URL = {http://www.techscience.com/CMES/v148n2/68575},
ISSN = {1526-1506},
ABSTRACT = {Active motion compensation is essential for improving the precision and safety of robot-assisted surgery in the presence of physiological motion such as heartbeat and respiration. Conventional direct error feedback controllers often show limited performance when sensing delay and measurement noise are present. To address this issue, this study proposes an active motion compensation framework based on deep reinforcement learning enhanced with a Long Short-Term Memory (LSTM) network, where the target position is assumed to be known. The motion compensation task is formulated as a Markov decision process, and the controller is trained to generate continuous control forces for the surgical instrument in three Cartesian directions. To overcome the performance degradation caused by sensing delay, LSTM is incorporated into both the actor and critic networks. Four controllers, including DDPG, TD3, DDPG-LSTM, and TD3-LSTM, are trained and evaluated in a Unity-based simulation environment with a 40 ms sensing delay and zero-mean Gaussian measurement noise. Experiments are conducted on simulated motion signals as well as Phantom and <i>in vivo</i> cardiac motion datasets. The results show that conventional fully connected controllers trained by DDPG and TD3 fail to achieve satisfactory compensation under delayed conditions, whereas the proposed LSTM-enhanced controllers significantly improve tracking performance. Among them, TD3-LSTM achieves the best overall results, with RMSE values of 0.1739, 0.3823, and 0.6136 mm on simulated, Phantom, and <i>in vivo</i> data, respectively. In addition, the proposed LSTM-enhanced controller outperforms a conventional PD controller, demonstrating the effectiveness of combining temporal sequence modeling with reinforcement learning for delay-aware surgical motion compensation.},
DOI = {10.32604/cmes.2026.085095}
}