Real-time Puncture Detection and Recovery for Pneumatic Soft Actuators
Tejonidhi R Deshpande, Tingyu Cheng, Josiah Hester
AI summary
Problem
Soft pneumatic actuators are highly susceptible to punctures and tears that compromise safety and reliability, yet existing fault detection methods rely on complex, heavy, or costly sensors that undermine their compliance and untethered operation.
Approach
The system uses a single onboard IMU to capture motion data during a deliberate chamber perturbation scheme, feeding the features into an autoencoder trained only on normal operation to detect anomalies and estimate puncture severity.
Key results
- Over 96% accuracy in localizing punctured chambers using only IMU data
- Novel chamber perturbation scheme enables real-time fault localization and severity scoring
- Mechanical redundancy strategy maintains actuation force post-puncture
- Open-source dataset of IMU readings for soft actuator puncture detection released
Why it matters
Enables safer, untethered deployment of soft robots in medical and field applications by providing real-time fault awareness without compromising compliance or adding heavy sensors.
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.