Thuruthel, TG;
Hughes, J;
Georgopoulou, A;
Clemens, F;
Iida, F;
(2021)
Using Redundant and Disjoint Time-Variant Soft Robotic Sensors for Accurate Static State Estimation.
IEEE Robotics and Automation Letters
, 6
(2)
pp. 2099-2105.
10.1109/LRA.2021.3061399.
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Abstract
Soft robotic sensors have been limited in their applications due to their highly nonlinear time variant behavior. Current studies are either looking into techniques to improve the mechano-electrical properties of these sensors or into modelling algorithms that account for the history of each sensor. Here, we present a method for combining multi-material soft strain sensors to obtain equivalent higher quality sensors; better than each of the individual strain sensors. The core idea behind this work is to use a combination of redundant and disjoint strain sensors to compensate for the time-variant hidden states of a soft-bodied system, to finally obtain the true strain state in a static manner using a learning-based approach. We provide methods to develop these variable sensors and metrics to estimate their dissimilarity and efficacy of each sensor combinations, which can double down as a benchmarking tool for soft robotic sensors. The proposed approach is experimentally validated on a pneumatic actuator with embedded soft strain sensors. Our results show that static data from a combination of nonlinear time variant strain sensors is sufficient to accurately estimate the strain state of a system.
| Type: | Article |
|---|---|
| Title: | Using Redundant and Disjoint Time-Variant Soft Robotic Sensors for Accurate Static State Estimation |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/LRA.2021.3061399 |
| Publisher version: | https://doi.org/10.1109/LRA.2021.3061399 |
| Language: | English |
| Additional information: | © The Authors 2022. Original content in this paper is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Soft sensors and actuators, sensor fusion, modeling, control, learning for soft robots |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159258 |
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