2017
DOI: 10.1177/1045389x17704921
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Experimental characterization of a dielectric elastomer fluid pump and optimizing performance via composite materials

Abstract: Dielectric elastomer is a class of soft actuators with exceptionally high strain capabilities and energy density. It is being studied for wide range of various applications and has been hypothesized to be a good material for biomedical blood pumps. We performed experimental characterization of a simple dielectric elastomer fluid pump to test this feasibility. We achieved substantial flow rates (10 mL/s) and actuation pressure (45 mm Hg) and found that dielectric elastomer fluid pump performance can exhibit sig… Show more

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Cited by 40 publications
(17 citation statements)
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References 33 publications
(45 reference statements)
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“…Some of them are depicted in Figure 1. Hence, the amalgamation of these budding fields presents the potential of building smarter control systems [11] that can handle objects of varying shapes [23], adapt to continuously diverging environments and perform substantial tasks combining soft robots. Hence, in this paper, we focus on applying DRL and imitation learning techniques to soft robots to perform the task of control of robotic systems.…”
Section: Deep Learning In Soromentioning
confidence: 99%
“…Some of them are depicted in Figure 1. Hence, the amalgamation of these budding fields presents the potential of building smarter control systems [11] that can handle objects of varying shapes [23], adapt to continuously diverging environments and perform substantial tasks combining soft robots. Hence, in this paper, we focus on applying DRL and imitation learning techniques to soft robots to perform the task of control of robotic systems.…”
Section: Deep Learning In Soromentioning
confidence: 99%
“…The last decade has seen a huge dependence on soft robotics (and/or bio-robotics) for solving the control related tasks, and applying such DRL techniques on these soft robotics systems has become a focus point of heavy ongoing research. Hence, the amalgamation of these budding fields of interests presents before us a challenge as well as potential of building much smarter control systems that can handle objects of varying shapes [15], adapt to continuously diverging environments and perform substantial tasks of manipulation combining the hardware capabilities of a soft robotic system alongside the learning procedures of a deep learning agent. Hence, in this paper we focus applying DRL and imitation learning techniques to soft robots to perform the task of control of robotic systems.…”
Section: Deep Learning In Soromentioning
confidence: 99%
“…The last decade has seen a huge dependence on soft robotics (and/or bio-robotics) for solving the control related tasks, and applying such DRL techniques on these soft robotics systems has become a focus point of heavy ongoing research. Hence, the amalgamation of these budding fields of interests presents before us a challenge as well as potential of building much smarter control systems [10] that can handle objects of varying shapes [19], adapt to continuously diverging environments and perform substantial tasks of manipulation combining the hardware capabilities of a soft robotic system alongside the learning procedures of a deep learning agent. Hence, in this paper we focus applying DRL and imitation learning techniques to soft robots to perform the task of control of robotic systems.…”
Section: Deep Learning In Soromentioning
confidence: 99%