{"title":"基于模型的分布式应变传感软机器人三维形状重构。","authors":"Liangshu Liu,Xinghao Huang,Xiaoci Zhang,Baiyu Zhang,Hao Xu,Vedanshee Mihir Trivedi,Kenneth Liu,Zoheb Shaikh,Hangbo Zhao","doi":"10.1089/soro.2024.0158","DOIUrl":null,"url":null,"abstract":"Proprioception in soft robots is essential for enabling autonomous behaviors, allowing them to navigate and interact safely in unstructured environments. Previous sensorization-based shape reconstruction methods, which often rely on machine learning techniques, have limitations in their broad applicability for different robotic systems and environments. In this work, we present a shape reconstruction scheme enabled by sparsely distributed soft strain sensors on the surfaces of soft robots, combined with a model-based reconstruction framework. Our approach utilizes miniaturized stretchable capacitive strain sensors with large stretchability and low hysteresis, which can be easily attached to soft robot surfaces for accurate local strain measurements. These measurements are fed into an optimization algorithm with embedded mechanical constraints. Our approach can predict all deformation modes in a soft bar with a maximum displacement error of less than 4% of the bar length and accurately reconstruct the shapes of soft pneumatic grippers during grasping actions. Additional reconstructions of a bioinspired arm in complex contact scenarios further demonstrate the versatility of our approach. This shape reconstruction scheme using distributed strain sensors offers a convenient and broadly applicable solution for enhancing proprioception in soft robots.","PeriodicalId":48685,"journal":{"name":"Soft Robotics","volume":"11 1","pages":""},"PeriodicalIF":6.4000,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Model-Based 3D Shape Reconstruction of Soft Robots via Distributed Strain Sensing.\",\"authors\":\"Liangshu Liu,Xinghao Huang,Xiaoci Zhang,Baiyu Zhang,Hao Xu,Vedanshee Mihir Trivedi,Kenneth Liu,Zoheb Shaikh,Hangbo Zhao\",\"doi\":\"10.1089/soro.2024.0158\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Proprioception in soft robots is essential for enabling autonomous behaviors, allowing them to navigate and interact safely in unstructured environments. Previous sensorization-based shape reconstruction methods, which often rely on machine learning techniques, have limitations in their broad applicability for different robotic systems and environments. In this work, we present a shape reconstruction scheme enabled by sparsely distributed soft strain sensors on the surfaces of soft robots, combined with a model-based reconstruction framework. Our approach utilizes miniaturized stretchable capacitive strain sensors with large stretchability and low hysteresis, which can be easily attached to soft robot surfaces for accurate local strain measurements. These measurements are fed into an optimization algorithm with embedded mechanical constraints. Our approach can predict all deformation modes in a soft bar with a maximum displacement error of less than 4% of the bar length and accurately reconstruct the shapes of soft pneumatic grippers during grasping actions. Additional reconstructions of a bioinspired arm in complex contact scenarios further demonstrate the versatility of our approach. This shape reconstruction scheme using distributed strain sensors offers a convenient and broadly applicable solution for enhancing proprioception in soft robots.\",\"PeriodicalId\":48685,\"journal\":{\"name\":\"Soft Robotics\",\"volume\":\"11 1\",\"pages\":\"\"},\"PeriodicalIF\":6.4000,\"publicationDate\":\"2025-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Soft Robotics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1089/soro.2024.0158\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Soft Robotics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1089/soro.2024.0158","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ROBOTICS","Score":null,"Total":0}
Model-Based 3D Shape Reconstruction of Soft Robots via Distributed Strain Sensing.
Proprioception in soft robots is essential for enabling autonomous behaviors, allowing them to navigate and interact safely in unstructured environments. Previous sensorization-based shape reconstruction methods, which often rely on machine learning techniques, have limitations in their broad applicability for different robotic systems and environments. In this work, we present a shape reconstruction scheme enabled by sparsely distributed soft strain sensors on the surfaces of soft robots, combined with a model-based reconstruction framework. Our approach utilizes miniaturized stretchable capacitive strain sensors with large stretchability and low hysteresis, which can be easily attached to soft robot surfaces for accurate local strain measurements. These measurements are fed into an optimization algorithm with embedded mechanical constraints. Our approach can predict all deformation modes in a soft bar with a maximum displacement error of less than 4% of the bar length and accurately reconstruct the shapes of soft pneumatic grippers during grasping actions. Additional reconstructions of a bioinspired arm in complex contact scenarios further demonstrate the versatility of our approach. This shape reconstruction scheme using distributed strain sensors offers a convenient and broadly applicable solution for enhancing proprioception in soft robots.
期刊介绍:
Soft Robotics (SoRo) stands as a premier robotics journal, showcasing top-tier, peer-reviewed research on the forefront of soft and deformable robotics. Encompassing flexible electronics, materials science, computer science, and biomechanics, it pioneers breakthroughs in robotic technology capable of safe interaction with living systems and navigating complex environments, natural or human-made.
With a multidisciplinary approach, SoRo integrates advancements in biomedical engineering, biomechanics, mathematical modeling, biopolymer chemistry, computer science, and tissue engineering, offering comprehensive insights into constructing adaptable devices that can undergo significant changes in shape and size. This transformative technology finds critical applications in surgery, assistive healthcare devices, emergency search and rescue, space instrument repair, mine detection, and beyond.