Real-Time Shape Estimation of Hyper-Redundant Flexible Manipulator Using Coiled Fiber Sensors

Area of Science:

• Robotics
• Biomedical Engineering
• Sensor Technology

Background:

• Accurate shape estimation of flexible endoscopic surgical robots is crucial for enhancing control precision and minimizing tissue damage.
• Integrating sensors into manipulators requires consideration of space efficiency, cost, system complexity, and calibration ease.

Purpose of the Study:

• To propose a real-time method for estimating the shape of hyper-redundant manipulators using embedded coiled fiber sensors.
• To ensure reliable shape recognition even when the manipulator is subjected to external loads.

Main Methods:

• Developed a real-time shape estimation method for hyper-redundant manipulators with embedded coiled fiber sensors.
• Optimized fiber sensor design for durability and sensitivity, enabling measurement of compressive and tensile strain.
• Determined optimal sensor count and placement through kinematic and moment distribution analysis.

Main Results:

• Achieved real-time shape estimation capability for flexible manipulators.
• Demonstrated shape recognition accuracy under both free-loading (mean max error < 3.54%) and loading conditions (mean max error < 5.47%).
• Validated the proposed method experimentally under various loading scenarios.

Conclusions:

• The proposed method offers a robust and accurate solution for real-time shape estimation of flexible surgical robots.
• Embedded coiled fiber sensors provide a flexible, compact, and cost-effective sensing solution for hyper-redundant manipulators.
• This technology has the potential to improve surgical robot performance and patient safety.