Jie Ma, Jinzhou Li, Yan Yang, Wenjing Hu, Li Zhang, Zhijie Liu
{"title":"Multi-Sensor Fusion for State Estimation and Control of Cable-Driven Soft Robots","authors":"Jie Ma, Jinzhou Li, Yan Yang, Wenjing Hu, Li Zhang, Zhijie Liu","doi":"10.1007/s42235-024-00582-8","DOIUrl":null,"url":null,"abstract":"<div><p>Cable-driven soft robots exhibit complex deformations, making state estimation challenging. Hence, this paper develops a multi-sensor fusion approach using a gradient descent strategy to estimate the weighting coefficients. These coefficients combine measurements from proprioceptive sensors, such as resistive flex sensors, to determine the bending angle. Additionally, the fusion strategy adopted provides robust state estimates, overcoming mismatches between the flex sensors and soft robot dimensions. Furthermore, a nonlinear differentiator is introduced to filter the differentiated sensor signals to address noise and irrational values generated by the Analog-to-Digital Converter. A rational polynomial equation is also introduced to compensate for temperature drift exhibited by the resistive flex sensors, which affect the accuracy of state estimation and control. The processed multi-sensor data is then utilized in an improved PD controller for closed-loop control of the soft robot. The controller incorporates the nonlinear differentiator and drift compensation, enhancing tracking performance. Experimental results validate the effectiveness of the integrated approach, demonstrating improved tracking accuracy and robustness compared to traditional PD controllers.</p></div>","PeriodicalId":614,"journal":{"name":"Journal of Bionic Engineering","volume":"21 6","pages":"2792 - 2803"},"PeriodicalIF":4.9000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Bionic Engineering","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s42235-024-00582-8","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Cable-driven soft robots exhibit complex deformations, making state estimation challenging. Hence, this paper develops a multi-sensor fusion approach using a gradient descent strategy to estimate the weighting coefficients. These coefficients combine measurements from proprioceptive sensors, such as resistive flex sensors, to determine the bending angle. Additionally, the fusion strategy adopted provides robust state estimates, overcoming mismatches between the flex sensors and soft robot dimensions. Furthermore, a nonlinear differentiator is introduced to filter the differentiated sensor signals to address noise and irrational values generated by the Analog-to-Digital Converter. A rational polynomial equation is also introduced to compensate for temperature drift exhibited by the resistive flex sensors, which affect the accuracy of state estimation and control. The processed multi-sensor data is then utilized in an improved PD controller for closed-loop control of the soft robot. The controller incorporates the nonlinear differentiator and drift compensation, enhancing tracking performance. Experimental results validate the effectiveness of the integrated approach, demonstrating improved tracking accuracy and robustness compared to traditional PD controllers.
期刊介绍:
The Journal of Bionic Engineering (JBE) is a peer-reviewed journal that publishes original research papers and reviews that apply the knowledge learned from nature and biological systems to solve concrete engineering problems. The topics that JBE covers include but are not limited to:
Mechanisms, kinematical mechanics and control of animal locomotion, development of mobile robots with walking (running and crawling), swimming or flying abilities inspired by animal locomotion.
Structures, morphologies, composition and physical properties of natural and biomaterials; fabrication of new materials mimicking the properties and functions of natural and biomaterials.
Biomedical materials, artificial organs and tissue engineering for medical applications; rehabilitation equipment and devices.
Development of bioinspired computation methods and artificial intelligence for engineering applications.