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- Publisher Website: 10.1089/soro.2021.0056
- Scopus: eid_2-s2.0-85163780096
- WOS: WOS:000961052800001
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Article: Soft Robot Proprioception Using Unified Soft Body Encoding and Recurrent Neural Network
Title | Soft Robot Proprioception Using Unified Soft Body Encoding and Recurrent Neural Network |
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Authors | |
Keywords | kinematic model pressure information proprioception receptor receptor failure soft pneumatic chamber |
Issue Date | 1-Aug-2023 |
Publisher | Mary Ann Liebert |
Citation | Soft Robotics, 2023, v. 10, n. 4, p. 825-837 How to Cite? |
Abstract | Compared with rigid robots, soft robots are inherently compliant and have advantages in the tasks requiring flexibility and safety. But sensing the high dimensional body deformation of soft robots is a challenge. Encasing soft strain sensors into the internal body of soft robots is the most popular solution to address this challenge. But most of them usually suffer from problems like nonlinearity, hysteresis, and fabrication complexity. To endow the soft robots with body movement awareness, this work presents a bioinspired architecture by taking cues from human proprioception system. Differing from the popular usage of smart material-based sensors embedded in soft actuators, we created a synthetic analog to the human muscle system, using paralleled soft pneumatic chambers to serve as receptors for sensing body deformation. We proposed to build the system with redundant receptors and explored deep learning tools for generating the kinematic model. Based on the proposed methodology, we demonstrated the design of three degrees of freedom continuum joint and how its kinematic model was learned from the unified pressure information of the actuators and receptors. In addition, we investigated the response of the soft system to receptor failures and presented both hardware and software level solutions for achieving graceful degradation. This approach offers an alternative to enable soft robots with proprioception capability, which will be useful for closed-loop control and interaction with environment. |
Persistent Identifier | http://hdl.handle.net/10722/331966 |
ISSN | 2023 Impact Factor: 6.4 2023 SCImago Journal Rankings: 2.430 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wang, L | - |
dc.contributor.author | Lam, J | - |
dc.contributor.author | Chen, X | - |
dc.contributor.author | Li, J | - |
dc.contributor.author | Zhang, R | - |
dc.contributor.author | Su, Y | - |
dc.contributor.author | Wang, Z | - |
dc.date.accessioned | 2023-09-28T04:59:55Z | - |
dc.date.available | 2023-09-28T04:59:55Z | - |
dc.date.issued | 2023-08-01 | - |
dc.identifier.citation | Soft Robotics, 2023, v. 10, n. 4, p. 825-837 | - |
dc.identifier.issn | 2169-5172 | - |
dc.identifier.uri | http://hdl.handle.net/10722/331966 | - |
dc.description.abstract | Compared with rigid robots, soft robots are inherently compliant and have advantages in the tasks requiring flexibility and safety. But sensing the high dimensional body deformation of soft robots is a challenge. Encasing soft strain sensors into the internal body of soft robots is the most popular solution to address this challenge. But most of them usually suffer from problems like nonlinearity, hysteresis, and fabrication complexity. To endow the soft robots with body movement awareness, this work presents a bioinspired architecture by taking cues from human proprioception system. Differing from the popular usage of smart material-based sensors embedded in soft actuators, we created a synthetic analog to the human muscle system, using paralleled soft pneumatic chambers to serve as receptors for sensing body deformation. We proposed to build the system with redundant receptors and explored deep learning tools for generating the kinematic model. Based on the proposed methodology, we demonstrated the design of three degrees of freedom continuum joint and how its kinematic model was learned from the unified pressure information of the actuators and receptors. In addition, we investigated the response of the soft system to receptor failures and presented both hardware and software level solutions for achieving graceful degradation. This approach offers an alternative to enable soft robots with proprioception capability, which will be useful for closed-loop control and interaction with environment. | - |
dc.language | eng | - |
dc.publisher | Mary Ann Liebert | - |
dc.relation.ispartof | Soft Robotics | - |
dc.subject | kinematic model | - |
dc.subject | pressure information | - |
dc.subject | proprioception | - |
dc.subject | receptor | - |
dc.subject | receptor failure | - |
dc.subject | soft pneumatic chamber | - |
dc.title | Soft Robot Proprioception Using Unified Soft Body Encoding and Recurrent Neural Network | - |
dc.type | Article | - |
dc.identifier.doi | 10.1089/soro.2021.0056 | - |
dc.identifier.scopus | eid_2-s2.0-85163780096 | - |
dc.identifier.volume | 10 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 825 | - |
dc.identifier.epage | 837 | - |
dc.identifier.eissn | 2169-5180 | - |
dc.identifier.isi | WOS:000961052800001 | - |
dc.identifier.issnl | 2169-5172 | - |