Conference proceeding
Performance quantification of strain sensors for flexible manipulators
2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Vol.2016, pp.584-589
2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) (Banff, Alberta, Canada, 12/07/2016–15/07/2016)
07/2016
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Abstract
As a natural result of the continual growth and progress in soft robotics to establish fully compliant bodies primarily made of soft materials using additive manufacturing techniques, there are also increasing efforts towards realizing flexible and stretchable sensors which can seamlessly be embedded in soft robotic devices. In this study, we report on the performance evaluation of two soft strain sensors based on different fabrication techniques and soft materials. We fabricated one of them in a laboratory environment using a silicone rubber substrate (EcoFlex 0010) engraved with microchannels, which were filled with liquid carbon black (CB). The other one is a commercially available sensor (flexible stretch sensor, Images SI) made of a polymer which changes its resistance when stretched. The electromechanical responses and strain sensing performances of these two sensors are experimentally quantified and their usefulness in practical soft robotics applications is discussed. Although both sensors display a similar strain sensing performance, the commercial sensor is durable over a longer period of time while the microfluidic silicone sensor has a much smaller time constant, making it more suitable for applications with rapidly changing inputs.
Details
- Title
- Performance quantification of strain sensors for flexible manipulators
- Creators
- S. Kumbay Yildiz - University of WollongongR. Mutlu - University of WollongongG. Alici - University of Wollongong
- Publication Details
- 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Vol.2016, pp.584-589
- Conference
- 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) (Banff, Alberta, Canada, 12/07/2016–15/07/2016)
- Publisher
- IEEE
- Number of pages
- 6
- Identifiers
- 991013225622702368
- Copyright
- © Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Conference proceeding