Cai, Y;
Hardman, D;
Iida, F;
Thuruthel, TG;
(2023)
Design and Development of a Hydrogel-based Soft Sensor for Multi-Axis Force Control.
In:
Proceedings - IEEE International Conference on Robotics and Automation.
(pp. pp. 594-600).
IEEE
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Abstract
As soft robotic systems become increasingly complex, there is a need to develop sensory systems which can provide rich state information to the robot for feedback control. Multi-axis force sensing and control is one of the less explored problems in this domain. There are numerous challenges in the development of a multi-axis soft sensor: from the design and fabrication to the data processing and modelling. This work presents the design and development of a novel multi-axis soft sensor using a gelatin-based ionic hydrogel and 3D printing technology. A learning-based modelling approach coupled with sensor redundancy is developed to model the environmentally dependent soft sensors. Numerous real-time experiments are conducted to test the performance of the sensor and its applicability in closed-loop control tasks at 20 Hz. Our results indicate that the soft sensor can predict force values and orientation angle within 4% and 7% of their total range, respectively.
| Type: | Proceedings paper |
|---|---|
| Title: | Design and Development of a Hydrogel-based Soft Sensor for Multi-Axis Force Control |
| Event: | 2023 IEEE International Conference on Robotics and Automation (ICRA), 29 May 2023 - 02 June 2023, London, UK |
| Dates: | 29 May 2023 - 2 Jun 2023 |
| ISBN-13: | 9798350323658 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICRA48891.2023.10160807 |
| Publisher version: | https://doi.org/10.1109/ICRA48891.2023.10160807 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. - For the purpose of open access, the author(s) has applied a Creative Commons Attribution (CC BY) license to any Accepted Manuscript version arising. |
| Keywords: | Fabrication, Recurrent neural networks, Soft sensors, Hydrogels, Force, Dynamics, Robot sensing systems |
| 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-pp.ucl.ac.uk/id/eprint/10177104 |
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