DefinePK

Abstract

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.