Abstract
The actuation of silicone/ethanol soft composite material-actuators is based on the phase change of ethanol upon heating, followed by the expansion of the whole composite, exhibiting high actuation stress and strain. However, the low thermal conductivity of silicone rubber hinders uniform heating throughout the material, creating overheated damaged areas in the silicone matrix and accelerating ethanol evaporation. This limits the actuation speed and the total number of operation cycles of these thermally-driven soft actuators. In this paper, we showed that adding 8 wt.% of diamond nanoparticle-based thermally conductive filler increases the thermal conductivity (from 0.190 W/mK to 0.212 W/mK), actuation speed and amount of operation cycles of silicone/ethanol actuators, while not affecting the mechanical properties. We performed multi-cyclic actuation tests and showed that the faster and longer operation of 8 wt.% filler material-actuators allows collecting enough reliable data for computational methods to model further actuation behavior. We successfully implemented a long short-term memory (LSTM) neural network model to predict the actuation force exerted in a uniform multi-cyclic actuation experiment. This work paves the way for a broader implementation of soft thermally-driven actuators in various robotic applications.
Highlights
Creating nature-like compliant and autonomous robots has been one of the main aims of robotics.A combination of compliance and self-awareness in robots may allow for proper human–robot interaction, required for co-working with people
Machine learning methods have been used in soft actuation, mainly for the automation and optimization of control (reinforcement been used in soft actuation, mainly for the automation and optimization of control and for modeling the kinematics of unknown soft actuators learning [31], deep neural network [32]) and for modeling the kinematics of unknown soft actuators and estimating the interaction forces with external items (long short-term memory (LSTM) recurrent and estimating the interaction forces with external items (long short-term memory (LSTM) recurrent neural network) [33])
It was shown that adding small amounts (
Summary
A combination of compliance and self-awareness in robots may allow for proper human–robot interaction, required for co-working with people. The ability of such robots to optimally interact with their environment and humans depends to a large extent on their embodiment, namely the morphology and the materials comprising them [1]. The materials used in soft actuation are mostly based on various types of polymers and their composites, responsive to one or several stimuli. Among others, these materials comprise dielectric elastomer actuators [5], ionic polymer–metal composite actuators [6], hydrogel actuators [7], Actuators 2020, 9, 62; doi:10.3390/act9030062 www.mdpi.com/journal/actuators
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