Summary
Soft robots made from squishy materials can move smoothly but are easy to damage by getting small holes or tears, which makes them stop working well. This paper introduces a way to notice these punctures quickly by using motion sensors attached to the robot, without needing lots of equipment. The system can figure out which part of the robot is damaged and how bad the problem is, then help the robot keep working by adjusting its movement. The researchers also made a special robot with several air-filled chambers that can bend in different ways and recover from damages automatically. This makes soft robots more reliable and safer when used around people or in tricky places.
soft robotspneumatic actuatorsinertial measurement unitanomaly detectiondamage severity estimationmulti-chamber actuatorreal-time monitoringfailure recovery
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.