Dynamic Modeling of Cable Driven Elongated Surgical Instruments for Sensorless Grip Force Estimation

Area of Science:

• Robotics
• Surgical Technology
• Control Systems

Background:

• Haptic feedback is crucial in surgery but is currently lacking in robotic minimally invasive surgery (MIS).
• Elongated cable-driven surgical instruments are commonly used in MIS, but their grip force is difficult to measure directly.

Purpose of the Study:

• To propose and validate a dynamic model-based sensorless method for estimating grip force in cable-driven surgical instruments for MIS.
• To enhance haptic perception for surgeons operating robotic surgical systems.

Main Methods:

• Dynamic modeling of cable and cable-pulley properties.
• Joint estimation of grip forces, motor positions/velocities, and gripper jaw positions/velocities using Unscented Kalman Filter.
• Utilizing only motor encoder readings and output torques as known observations.
• Employing a bounding filter to address model inaccuracies and improve robustness.

Main Results:

• The proposed sensorless method achieved sufficiently accurate grip force estimation.
• Validation was performed on a 10mm gripper integrated with the Raven-II surgical robot.
• Experimental results confirmed the method's effectiveness using only motor encoder and torque data.

Conclusions:

• The developed dynamic model-based sensorless approach effectively estimates grip force in robotic MIS.
• This method can significantly improve haptic feedback in robotic surgery without requiring additional force sensors.
• The technique offers a robust solution for enhancing surgeon's sense of touch during minimally invasive procedures.