Adaptive Remote Center of Motion Control for Robotic Laparoscopic Surgery

Core Concepts

A novel kinematic framework that integrates admittance control at the remote center of motion (RCM) location with an augmented Jacobian approach to accommodate changes in the RCM location driven by external forces during robotic laparoscopic surgery.

Abstract

The paper presents a novel framework for robot-assisted laparoscopic surgery that addresses the remote center of motion (RCM) constraint. The key highlights are:

1. Modeling of the RCM constraint and the instrument motion constraint using an augmented Jacobian approach.
2. Integration of an admittance controller to drive the RCM velocity based on the estimated forces at the RCM location.
3. A robust estimation method to decouple the forces at the RCM and the instrument distal end using force/torque sensory feedback at the base of the instrument driving mechanism.
4. Validation of the proposed control algorithm through simulations in both MATLAB and ROS2 environments, demonstrating the ability to track the instrument trajectory while respecting the RCM constraint in the presence of external forces.
5. Development of a hardware platform including a 7-DoF robot, a customized instrument driving mechanism, and an off-the-shelf surgical instrument for future experimental validation.

Stats

During surgery, movements of the chest area and the abdominal cavity can result in displacements of up to a few centimeters at the trocar location.
The proposed framework can accommodate changes in the RCM location driven by external forces of up to a few centimeters.

Quotes

"Factors such as the patient's breathing and heartbeats may introduce continuous variations in the trocar location during an operation."
"We propose using the interaction force at the RCM location to drive RCM control."

Key Insights Distilled From

Admittance Control for Adaptive Remote Center of Motion in Robotic Laparoscopic Surgery

by Ehsan Nasiri... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04416.pdf

Deeper Inquiries

How can the proposed framework be extended to handle more complex force loading scenarios, such as multiple external forces acting on the instrument at different locations

To handle more complex force loading scenarios, such as multiple external forces acting on the instrument at different locations, the proposed framework can be extended by incorporating advanced force estimation techniques and algorithms. One approach could involve implementing a distributed force sensing system along the instrument shaft to capture forces at various points. By integrating multiple force/torque sensors strategically placed along the instrument, the system can gather data on the forces exerted at different locations. This data can then be processed using advanced signal processing and estimation algorithms to accurately determine the individual forces acting on the instrument. Additionally, machine learning algorithms can be employed to analyze the force data and predict the most likely distribution of forces along the instrument shaft. By combining advanced sensing technologies with sophisticated data processing techniques, the framework can effectively handle complex force loading scenarios in robotic surgical applications.

What are the potential limitations of the force estimation approach based on the force/torque sensor at the base of the instrument driving mechanism, and how can they be addressed

The force estimation approach based on the force/torque sensor at the base of the instrument driving mechanism may have some potential limitations that need to be addressed. One limitation could be the presence of noise and disturbances in the force measurements, which can affect the accuracy of the estimated forces at the RCM location. To address this limitation, advanced filtering and signal processing techniques can be implemented to reduce noise and improve the quality of force estimation. Additionally, calibration procedures should be regularly performed to ensure the accuracy and reliability of the force sensor data. Another limitation could be the inability to decouple forces acting at different locations along the instrument shaft. This challenge can be mitigated by developing sophisticated algorithms that can separate and estimate individual forces based on the sensor data collected. By addressing these limitations through advanced signal processing, calibration, and algorithm development, the force estimation approach can be enhanced to provide more accurate and reliable force feedback for the robotic surgical system.

What are the implications of the proposed adaptive RCM control on the overall stability and safety of the robotic surgical system, especially during unexpected patient movements or interactions

The proposed adaptive RCM control has significant implications for the overall stability and safety of the robotic surgical system, especially during unexpected patient movements or interactions. By integrating an admittance control framework with redundancy resolution methods, the system can dynamically adjust to external forces acting on the instrument, ensuring that the RCM constraint is maintained even in the presence of disturbances. This adaptive control mechanism enhances the system's ability to respond to changes in the surgical environment, such as patient movements or physiological variations, while maintaining precise and safe operation. Additionally, the force estimation approach allows the system to accurately sense and respond to forces at the RCM location, further enhancing the safety and stability of the robotic surgical system. Overall, the proposed adaptive RCM control contributes to the system's robustness and reliability, making it well-equipped to handle unexpected scenarios and ensure patient safety during robotic surgical procedures.