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https://scholarbank.nus.edu.sg/handle/10635/156068
DC Field | Value | |
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dc.title | SOFT CRAWLING ROBOTS BASED ON VARIOUS SMART ACTUATORS | |
dc.contributor.author | QIN LEI | |
dc.date.accessioned | 2019-07-01T18:04:13Z | |
dc.date.available | 2019-07-01T18:04:13Z | |
dc.date.issued | 2019-01-17 | |
dc.identifier.citation | QIN LEI (2019-01-17). SOFT CRAWLING ROBOTS BASED ON VARIOUS SMART ACTUATORS. ScholarBank@NUS Repository. | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/156068 | |
dc.description.abstract | In this thesis, we have developed multiple soft actuators, including freestanding dielectric elastomer actuators (DEAs), vacuum-actuated-spring actuators (VASAs) and electroadhesion actuators (EAs). These actuators were characterized systematically in terms of their deformation, output force and operating voltage/pressure. In addition, analytical models were developed to predict the actuation performance and optimize the design parameters of these actuators. With these soft actuators, we first developed a fast-moving soft crawling robot based on the freestanding DEA. Later, we proposed the first-in-class approach to generate switchable asymmetrical friction force using EAs for soft crawling robots. The new soft robot based on circular DEA and EAs could move in multiple directions without slippage. Furthermore, the soft robot could also achieve obstacle detection without any external sensors through its inherent self-sensing capability. Finally, a soft crawling robot based on two VASAs was developed to demonstrate its versatile functionalities. | |
dc.language.iso | en | |
dc.subject | soft robot, dielectric elastomer actuator, electroadhesion, vacuum actuation, obstacle detection and avoidance, fast and versatile locomotion | |
dc.type | Thesis | |
dc.contributor.department | MECHANICAL ENGINEERING | |
dc.contributor.supervisor | ZHU JIAN | |
dc.contributor.supervisor | HUANG HUI | |
dc.description.degree | Ph.D | |
dc.description.degreeconferred | DOCTOR OF PHILOSOPHY | |
Appears in Collections: | Ph.D Theses (Open) |
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QinL.pdf | 4.03 MB | Adobe PDF | OPEN | None | View/Download |
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