A Model-Based Sensor Fusion Approach for Force and Shape Estimation in Soft Robotics
Stefan Escaida Navarro, Steven Nagels, Hosam Alagi, Lisa-Marie Faller, Olivier Goury, Thor Morales Bieze, Hubert Zangl, Björn Hein 等 12 位
Institut national de recherche en sciences et technologies du numérique Centre de Recherche en Informatique, Signal et Automatique de Lille DEFROST: Logiciels pour robots déformables IMEC
阅读操作
确认中在文库中上传 PDF 后可生成中文音频讲解。
摘要与影响
In this letter, we address the challenge of sensor fusion in Soft Robotics for estimating forces and deformations. In the context of intrinsic sensing, we propose the use of soft capacitive sensing to find a contact's location, and the use of pneumatic sensing to estimate the force intensity and the deformation. Using a FEM-based numerical approach, we integrate both sensing streams and model two Soft Robotics devices we have conceived. These devices are a Soft Pad and a Soft Finger. We show in an evaluation that external forces on the Soft Pad can be estimated and that the shape of the Soft Finger can be reconstructed.
逐年被引趋势
关键指标
同类平均 = 1
同领域 · 同年份 · 同类型
Google Scholar 与 OpenAlex 的被引统计范围不同,数值存在差异属正常。
AI 辅助阅读
依据:摘要
可就本文提问;依据不足时会说明。
学术脉络
学科主题
工程Soft Robotics and Applications
Robot Manipulation and Learning · Advanced Materials and Mechanics
参考文献 16
此处列出前 3 条
引用本文 86
按被引量排序,此处列出前 3 条