Shape Sensing and Kinematic Control of a Cable-Driven Continuum Robot Based on Stretchable Capacitive Sensors

Area of Science:

• Robotics
• Mechanical Engineering
• Sensor Technology

Background:

• Continuum robots offer compliant motion but face challenges in accurate joint variable estimation due to external factors like gravity.
• Existing shape-sensing methods (FBG, vision-based) have limitations in sensitivity, implementation ease, or cost-effectiveness.
• Cable-Driven Continuum Joint Modules (CDCJMs) provide 2-DOF bending but require precise shape feedback for reliable control.

Purpose of the Study:

• To develop an accurate shape-sensing method for Cable-Driven Continuum Robots (CDCRs) using stretchable capacitive sensors.
• To formulate and calibrate a shape-sensing model relating sensor readings to the robot's bending variables.
• To implement and validate a closed-loop control strategy based on the calibrated model for enhanced trajectory tracking.

Main Methods:

• Integration of two stretchable capacitive sensors onto the flexible backbone of each CDCJM to measure bending shape.
• Formulation of a generic shape-sensing model and subsequent calibration to compensate for sensor placement errors.
• Implementation of a sliding-mode-based closed-loop control system utilizing the calibrated shape-sensing model.

Main Results:

• Stretchable capacitive sensors provide high sensitivity, ease of implementation, and cost-effectiveness for shape sensing.
• The calibrated shape-sensing model accurately determines the 2-DOF bending variables (direction and angle).
• Closed-loop control reduced average trajectory tracking errors by 82.94% (from 49.23mm to 8.40mm) for a 55mm radius circle.

Conclusions:

• Stretchable capacitive sensors offer a superior alternative for shape sensing in CDCRs.
• The proposed calibration method enhances the accuracy of the shape-sensing model.
• The closed-loop control strategy significantly improves the trajectory tracking performance of CDCRs.