Summary of ‘Full Study, Model Verification, and Control of a Five Degrees of Freedom Hybrid Robotic-Assisted System for Neurosurgery’
The research article titled “Full Study, Model Verification, and Control of a Five Degrees of Freedom Hybrid Robotic‐Assisted System for Neurosurgery,” authored by Ahmed Sedky and colleagues, explores an innovative hybrid robotic system designed to enhance precision in neurosurgical procedures. This study emphasises the importance of robotic assistance in surgeries that demand high accuracy, aiming to improve surgical outcomes and reduce recovery times.
Background and Motivation
Neurosurgery has increasingly integrated robotic technology to transform traditional open-surgery approaches into less invasive methods, leading to reduced bleeding and quicker recovery. Over the past three decades, various robotic systems have been developed for procedures such as craniotomy and stereotactic treatments. While serial manipulators offer flexibility and a larger operational area, they suffer from rigidity issues. In contrast, parallel manipulators provide greater rigidity but limited flexibility. The proposed Hybrid Robotic‐Assisted System for Neurosurgery combines the advantages of both types, employing a remote centre of motion (RCM) mechanism to address the challenges faced in cranial surgeries.
System Description and Methodology
This study introduces a dual-mechanism system comprising a 3 Degrees of Freedom (DoF) RCM double-hybrid parallelogram mechanism and a 2-DoF compensation mechanism. The main mechanism facilitates access to points on the skull, while the compensation mechanism ensures that the cutting tool remains perpendicular to the skull surface. The system was modelled using MATLAB/Simscape Multibody, allowing for accurate kinematic and dynamic representations.
The research presents both forward and inverse kinematic analyses, crucial for controlling the robotic mechanism during surgery. The inverse kinematics framework was developed to validate a circular trajectory at the end-effector tip, while two control strategies were compared: traditional active joint Proportional-Integral-Derivative (PID) control and a combined trajectory feedback plus feedforward control.
Results and Discussion
The results indicate that the combined control strategy significantly enhances performance, achieving an average reduction of 46.5% in maximum absolute error and 50.31% in mean square error compared to the PID control alone. The findings underscore the potential of the trajectory feedback and feedforward control to improve the reliability and precision of robotic-assisted neurosurgical procedures.
In the discussion section, the authors highlight the importance of achieving sub-millimetre tolerances in high-precision neurosurgical applications, emphasizing that their results meet the precision requirements set by existing literature [[6][7]]. The study also identifies potential sources of error, such as actuator misalignment and inaccuracies in the parallelogram mechanism, which can impact the overall performance of the robotic system.
Conclusion and Future Directions
The research concludes by affirming the effectiveness of the hybrid robotic system in neurosurgery, emphasizing the successful modelling and validation of its kinematic operations. The authors advocate for the adoption of advanced control strategies to further enhance the system’s precision and reliability in clinical settings. Future work will involve exploring various advanced control techniques to optimise the performance of the robotic system.
READ MORE… https://onlinelibrary.wiley.com/doi/epdf/10.1002/rcs.70047
