A Clamping Force Estimation Method Based on a Joint Torque Disturbance Observer Using PSO-BPNN for Cable-Driven...

Area of Science:

• Robotics
• Surgical Technology
• Sensor Technology

Background:

• Minimally-invasive surgical robots (MISRs) require precise force sensing for safe and effective operation.
• Current MISRs often lack integrated force sensors in their end-effectors, limiting tactile feedback.
• Accurate clamping force sensing is crucial for delicate surgical manipulations.

Purpose of the Study:

• To develop a one-dimensional clamping force sensing method for cable-driven MISR end-effectors.
• To enable force sensing without requiring internal force sensors within the forceps.
• To enhance the safety and precision of surgical procedures through improved force feedback.

Main Methods:

• Proposed a joint torque disturbance observer (JTDO) for cable-driven surgical robot end-effectors.
• Utilized a Particle Swarm Optimization Back Propagation Neural Network (PSO-BPNN) to estimate cable tension variations.
• Developed a clamping force estimator based on the forceps' JTDO and mechanical relationships.

Main Results:

• Achieved a detection resolution of 0.11 N for both collision and clamping forces.
• Experimental studies verified the feasibility and effectiveness of the proposed method.
• Demonstrated accurate clamping force estimation without internal end-effector sensors.

Conclusions:

• The proposed JTDO-based method effectively estimates clamping force in cable-driven MISRs.
• This approach enhances force sensing capabilities without complex internal sensor integration.
• The findings contribute to safer and more precise minimally-invasive surgeries.