Soft actuators detect and recover from punctures in real time
Real-time Puncture Detection and Recovery for Pneumatic Soft Actuators
Robotics
Summary
Soft robots are made from squishy materials and move by inflating chambers with air. These robots can get holes that make it hard for them to work properly. The authors design a system that uses a small motion sensor to spot when a hole happens and how bad it is. Their approach can figure out which part got damaged and help the robot keep working even after a hole appears.
What this means in practice
- •For robotic system designers: Add real-time puncture detection and recovery to soft robots using motion-based anomaly detection and chamber-specific damage scoring.
- •For medical device manufacturers: Improve safety of soft robotic devices that contact humans by detecting actuator leaks early and maintaining force after failures.
Authors
Tejonidhi R. Deshpande, Tingyu Cheng, Josiah Hester
Abstract
Soft robots offer safe and adaptive interaction with humans and unstructured environments through their inherent ability to deform and comply. Pneumatic actuators are one way to build soft robots. They are typically made from soft silicone materials and are especially effective for driving such systems, enabling smooth and adaptable motion. However, their compliant nature also makes them vulnerable to mechanical failures like punctures and tears, limiting practical deployment. To address this, we propose a puncture detection system for soft actuators using motion data from a single inertial measurement unit. Extracted features are used to train anomaly detectors for puncture detection and non-linear models to estimate severity. We also introduce a multi-chamber pneumatic soft bending actuator capable of diverse configurations via selective chamber inflation. Our algorithm identifies the punctured chamber and provides a severity score using a chamber perturbation scheme. Anomaly detectors are trained on normal operation data and detect damage through reconstruction errors, while severity is estimated by a separate model trained under slightly modified conditions. Finally, we demonstrate a failure recovery strategy to maintain actuation force post-failure. This approach enhances the reliability and safety of soft robotic systems through real-time, data-driven damage detection.