发表机构
Georgia Institute of Technology; University of Notre Dame（佐治亚理工学院; 圣母大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于惯性测量单元数据的气动软体执行器实时穿刺检测与恢复方法，利用异常检测器和腔室扰动方案识别损伤并估计严重程度，并通过多腔室执行器实现故障后驱动力维持。
AI 中文摘要
软体机器人通过其固有的变形和顺应能力，能够与人类及非结构化环境进行安全且自适应的交互。气动执行器是构建软体机器人的一种方式，通常由柔软的硅胶材料制成，特别适用于驱动此类系统，实现平滑且可适应的运动。然而，其顺应性也使其容易受到穿刺和撕裂等机械故障的影响，限制了实际部署。为解决这一问题，我们提出了一种利用单个惯性测量单元的运动数据来检测软体执行器穿刺的系统。提取的特征用于训练异常检测器以进行穿刺检测，并训练非线性模型以估计严重程度。我们还引入了一种多腔室气动软体弯曲执行器，该执行器可通过选择性腔室充气实现多种构型。我们的算法利用腔室扰动方案识别被穿刺的腔室并提供严重程度评分。异常检测器在正常操作数据上训练，并通过重建误差检测损伤，而严重程度则由在略微修改条件下训练的独立模型进行估计。最后，我们展示了一种故障恢复策略，以在故障后维持驱动力。该方法通过实时、数据驱动的损伤检测，增强了软体机器人系统的可靠性和安全性。
英文摘要
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.
CommentsAccepted at IEEE ICRA 2026
