Controlling the fold: proprioceptive feedback in a soft origami robot

Area of Science:

• Robotics
• Soft Robotics
• Origami Engineering

Background:

• Soft robots offer advantages in dexterity and safety but often lack precise proprioceptive feedback for control.
• Origami-inspired robots, like the Kresling structure, provide unique actuation capabilities but require advanced sensing for closed-loop control.
• Capacitive sensing presents a promising, non-visual method for proprioception in compliant robotic systems.

Purpose of the Study:

• To demonstrate proprioceptive feedback control for a pneumatically actuated, Kresling-inspired origami robot.
• To develop and optimize capacitive sensors for accurate position estimation in soft robotic structures.
• To enable closed-loop control of both single-unit (1-DOF) and two-unit (2-DOF) soft origami robot systems.

Main Methods:

• Designing and fabricating a 3-D printed Kresling-inspired soft robot with integrated capacitive sensors.
• Utilizing a finite element approach to optimize capacitive electrode shapes for enhanced sensitivity across actuation ranges.
• Implementing discrete-time proportional-integral-derivative (PID) control algorithms for feedback-driven actuation.
• Testing control performance with static set points, dynamic stepping, and sinusoidal signal following.

Main Results:

• Achieved stable position control for a single Kresling robot with errors under 3 mm up to 10 mm contraction.
• Demonstrated two-degree-of-freedom control (extension and rotation) for a two-unit robot system with low error (1.7 mm and 6.1°).
• Validated the effectiveness of optimized capacitive sensors for proprioceptive state estimation in soft origami robots.

Conclusions:

• Closed-loop feedback position control is achievable in soft origami robots using capacitive sensing without external visual tracking.
• Optimized capacitive sensor design significantly improves control accuracy across the robot's actuation range.
• This work represents a significant advancement in proprioceptive feedback control for soft robotics, paving the way for more autonomous and capable soft machines.