Adaptive Reinforcement Learning Enabled Robotic Framework for Precision Trajectory Control in Complex Deep Brain Surgical Interventions
Keywords:
Adaptive control, Brain surgery, DeepAbstract
Adaptive control frameworks have emerged as a pivotal solution for addressing the stringent demands of precision in minimally invasive neurosurgery. This research article presents an adaptive reinforcement learning (RL) enabled robotic framework for precision trajectory control in complex deep brain surgical interventions. The system integrates a modular robotic arm with embedded force and visual feedback sensors to establish a closed-loop control architecture. A proximal policy optimization based deep RL agent is trained in a simulation environment using synthetic brain phantoms and domain randomization to ensure robustness under nonlinear tissue dynamics. The reward function penalizes tissue deformation while rewarding adherence to planned trajectories, thereby enhancing safety. Experimental validation in neurosurgical simulation demonstrates a 35 % reduction in trajectory deviation and a 25 % decrease in procedure time compared to conventional proportional–integral–derivative and spline-based planners. These findings underscore the potential of RL-driven robotic microsurgery to improve surgical accuracy, reduce human error, and ultimately enhance patient outcomes. Future extensions will focus on multi-modal imaging integration and in vivo trials to further establish clinical efficacy.
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Azimi, D., Dann, M., & Neumann, G. (2021). Guided policy search for adaptive control in robotic manipulation. International Journal of Robotics Research, 40(5), 789–806.
Barakkath Nisha, U., Subair, A., & Yasir Abdullah, R. (2020). An efficient algorithm for anomaly detection in wireless sensor networks. In 2020 International Conference on Smart Electronics and Communication (ICOSEC) (pp. 925–932). IEEE. https://doi.org/10.1109/ICOSEC49089.2020.9215258
Barakkath Nisha, U., Uma Maheswari, N., Venkatesh, R., et al. (2017). Fuzzy based flat anomaly diagnosis and relief measures in distributed wireless sensor network. International Journal of Fuzzy Systems, 19, 1528–1545. https://doi.org/10.1007/s40815-016-0253-2
Chen, D., & Yang, S. (2022). Domain randomization for robust robotic learning in surgical tasks. International Journal of Computer Assisted Radiology and Surgery, 17(3), 389–401.
Konukseven, E. I., Şahin, E., & Murad, D. (2015). Advances in robotic neurosurgery: Applications of robotics in precise intracranial procedures. Neurosurgery, 77(5), 795–804.
Kormushev, P., Nenchev, D., Calinon, S., & Caldwell, D. (2011). Upper-body kinesthetic teaching of a humanoid robot. In Proceedings of the 11th IEEE-RAS International Conference on Humanoid Robots (pp. 397–402).
Mnih, V., Kavukcuoglu, D., Silver, D., Rusu, A. A., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533.
Patel, A., Dubey, R., & Thakor, N. (2020). Robotic assistance in neurosurgery: A review. Journal of Neurosurgery, 132(3), 1–12.
Reddy, M., Zhang, L., & Smith, J. (2019). Simulation in neurosurgical training: Virtual reality and synthetic brain phantoms. Neurosurgical Focus, 47(2), E10.
Safa, M., Pandian, A., Mohammad, G. B., et al. (2024). Deep spectral time variant feature analytic model for cardiac disease prediction using softmax recurrent neural network in WSN IoT. Journal of Electrical Engineering & Technology, 19, 2651–2665. https://doi.org/10.1007/s42835-023-01748-w
Saranya, S. S., Anusha, P., Chandragandhi, S., Kishore, O. K., Kumar, N. P., & Srihari, K. (2024). Enhanced decision making in healthcare cloud edge networks using deep reinforcement and lion optimization algorithm. Biomedical Signal Processing and Control, 92, 105963. https://doi.org/10.1016/j.bspc.2024.105963
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms [Preprint]. arXiv. https://arxiv.org/abs/1707.06347
Yang, G. Z., Cambias, J., Cleary, K., et al. (2017). Medical robotics—Regulatory, ethical, and legal considerations for deployment of medical robots and robotic devices in surgery. IEEE Robotics and Automation Magazine, 24(2), 14–27.
Yang, G., Patel, A., & Li, Q. (2018). A survey on robot learning for control of surgical robots. IEEE Transactions on Medical Robotics and Bionics, 1(1), 34–46.
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