Name
Title | ||
|---|---|---|
Using Redundant And Disjoint Time-Variant Soft Robotic Sensors For Accurate Static State Estimation |
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LRA.2021.3061399 | IEEE ROBOTICS AND AUTOMATION LETTERS |
Keywords | DocType | Volume |
Soft sensors and actuators, sensor fusion, modeling, control, learning for soft robots | Journal | 6 |
Issue | ISSN | Citations |
2 | 2377-3766 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas George | 1 | 21 | 7.90 |
| Josie Hughes | 2 | 31 | 12.10 |
| Antonia Georgopoulou | 3 | 0 | 1.01 |
| Clemens, F. | 4 | 8 | 1.82 |
| Fumiya Iida | 5 | 513 | 68.42 |