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Many soft robotic components require highly stretchable, electrically conductive materials for proper operation. Often these conductive materials are used as sensors or as heaters for thermally responsive materials. However, there is a scarcity of stretchable materials that can withstand the high strains typically experienced by soft robots, while maintaining the electrical properties necessary for Joule heating ( e.g. , uniform conductivity). In this work, we present a silicone composite containing both liquid and solid inclusions that can maintain a uniform conductivity while experiencing 200% linear strains. This composite can be cast in thin sheets enabling it to be wrapped around thermally responsive soft materials that increase their volume or stretchability when heated. We show how this material opens up possibilities for electrically controllable shape changing soft robotic actuators, as well as all-silicone actuation systems powered only by electrical stimulus. Additionally, we show that this stretchable composite can be used as an electrode material in other applications, including a strain sensor with a linear response up to 200% strain and near-zero signal noise.
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Nonlinear compensation of stretchable strain sensors with application to proprioceptive sensing of soft robotic arm
Abstract With advances in materials and manufacturing techniques, recent years have seen a number of conductive composite materials that exhibit pronounced strain-dependent electrical resistivity, allowing them to be used for embedded, cost-effective strain sensing in various applications. The strain-resistivity relationship of these materials, however, is often highly nonlinear and dynamic, posing challenges for effective use of such strain sensors. In this paper, a computationally efficient scheme is proposed for compensating the nonlinear, dynamic strain-resistance behavior of a soft conductive rubber using a time delay neural network. The accuracy and feasibility of the technique is evaluated with a soft robotic arm incorporating three strain sensors for proprioception. Experimental results show that the sensing scheme is able to predict both the tip position and the shape of the robotic manipulator, achieving an average tip positional error of less than 4% relative to the total length of the manipulator.
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- PAR ID:
- 10572404
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Smart Materials and Structures
- Volume:
- 34
- Issue:
- 3
- ISSN:
- 0964-1726
- Format(s):
- Medium: X Size: Article No. 035018
- Size(s):
- Article No. 035018
- Sponsoring Org:
- National Science Foundation
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