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In-Situ Sensing and Dynamics Predictions for Electrothermally-Actuated Soft Robot Limbs

Untethered soft robots that locomote using electrothermally-responsive materials like shape memory alloy (SMA) face challenging design constraints for sensing actuator states. At the same time, modeling of actuator behaviors faces steep challenges, even with available sensor data, due to complex ele...

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Autores principales: Sabelhaus, Andrew P., Mehta, Rohan K., Wertz, Anthony T., Majidi, Carmel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9152248/
https://www.ncbi.nlm.nih.gov/pubmed/35655533
http://dx.doi.org/10.3389/frobt.2022.888261
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author Sabelhaus, Andrew P.
Mehta, Rohan K.
Wertz, Anthony T.
Majidi, Carmel
author_facet Sabelhaus, Andrew P.
Mehta, Rohan K.
Wertz, Anthony T.
Majidi, Carmel
author_sort Sabelhaus, Andrew P.
collection PubMed
description Untethered soft robots that locomote using electrothermally-responsive materials like shape memory alloy (SMA) face challenging design constraints for sensing actuator states. At the same time, modeling of actuator behaviors faces steep challenges, even with available sensor data, due to complex electrical-thermal-mechanical interactions and hysteresis. This article proposes a framework for in-situ sensing and dynamics modeling of actuator states, particularly temperature of SMA wires, which is used to predict robot motions. A planar soft limb is developed, actuated by a pair of SMA coils, that includes compact and robust sensors for temperature and angular deflection. Data from these sensors are used to train a neural network-based on the long short-term memory (LSTM) architecture to model both unidirectional (single SMA) and bidirectional (both SMAs) motion. Predictions from the model demonstrate that data from the temperature sensor, combined with control inputs, allow for dynamics predictions over extraordinarily long open-loop timescales (10 min) with little drift. Prediction errors are on the order of the soft deflection sensor’s accuracy. This architecture allows for compact designs of electrothermally-actuated soft robots that include sensing sufficient for motion predictions, helping to bring these robots into practical application.
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spelling pubmed-91522482022-06-01 In-Situ Sensing and Dynamics Predictions for Electrothermally-Actuated Soft Robot Limbs Sabelhaus, Andrew P. Mehta, Rohan K. Wertz, Anthony T. Majidi, Carmel Front Robot AI Robotics and AI Untethered soft robots that locomote using electrothermally-responsive materials like shape memory alloy (SMA) face challenging design constraints for sensing actuator states. At the same time, modeling of actuator behaviors faces steep challenges, even with available sensor data, due to complex electrical-thermal-mechanical interactions and hysteresis. This article proposes a framework for in-situ sensing and dynamics modeling of actuator states, particularly temperature of SMA wires, which is used to predict robot motions. A planar soft limb is developed, actuated by a pair of SMA coils, that includes compact and robust sensors for temperature and angular deflection. Data from these sensors are used to train a neural network-based on the long short-term memory (LSTM) architecture to model both unidirectional (single SMA) and bidirectional (both SMAs) motion. Predictions from the model demonstrate that data from the temperature sensor, combined with control inputs, allow for dynamics predictions over extraordinarily long open-loop timescales (10 min) with little drift. Prediction errors are on the order of the soft deflection sensor’s accuracy. This architecture allows for compact designs of electrothermally-actuated soft robots that include sensing sufficient for motion predictions, helping to bring these robots into practical application. Frontiers Media S.A. 2022-05-17 /pmc/articles/PMC9152248/ /pubmed/35655533 http://dx.doi.org/10.3389/frobt.2022.888261 Text en Copyright © 2022 Sabelhaus, Mehta, Wertz and Majidi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Sabelhaus, Andrew P.
Mehta, Rohan K.
Wertz, Anthony T.
Majidi, Carmel
In-Situ Sensing and Dynamics Predictions for Electrothermally-Actuated Soft Robot Limbs
title In-Situ Sensing and Dynamics Predictions for Electrothermally-Actuated Soft Robot Limbs
title_full In-Situ Sensing and Dynamics Predictions for Electrothermally-Actuated Soft Robot Limbs
title_fullStr In-Situ Sensing and Dynamics Predictions for Electrothermally-Actuated Soft Robot Limbs
title_full_unstemmed In-Situ Sensing and Dynamics Predictions for Electrothermally-Actuated Soft Robot Limbs
title_short In-Situ Sensing and Dynamics Predictions for Electrothermally-Actuated Soft Robot Limbs
title_sort in-situ sensing and dynamics predictions for electrothermally-actuated soft robot limbs
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9152248/
https://www.ncbi.nlm.nih.gov/pubmed/35655533
http://dx.doi.org/10.3389/frobt.2022.888261
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