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Joint Entropy-Based Morphology Optimization of Soft Strain Sensor Networks for Functional Robustness

Accepted version
Peer-reviewed

Type

Article

Change log

Abstract

Dense and distributed tactile sensors are critical for robots to achieve human-like manipulation skills. Soft robotic sensors are a potential technological solution to obtain the required high dimensional sensory information unobtrusively. However, the design of this new class of sensors is still based on human intuition or derived from traditional flex sensors. This work is a first step towards automated design of soft sensor morphologies based on optimization of information theory metrics and machine learning. Elementary simulation models are used to develop the optimized sensor morphologies that are more accurate and robust with the same number of sensors. Same configurations are replicated experimentally to validate the feasibility of such an approach for practical applications. Furthermore, we present a novel technique for drift compensation in soft strain sensors that allows us to obtain accurate contact localization. This work is an effort towards transferring the paradigm of \textit {morphological computation} from soft actuator designing to soft sensor designing for high performance, resilient tactile sensory networks.

Description

Keywords

Soft robotics, tactile sensors, information entropy, optimization methods, machine learning

Journal Title

IEEE Sensors Journal

Conference Name

Journal ISSN

1530-437X
1558-1748

Volume Title

20

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Rights

All rights reserved
Sponsorship
European Commission Horizon 2020 (H2020) Future and Emerging Technologies (FET) (828818)
Engineering and Physical Sciences Research Council (EP/N029003/1)
EPSRC (EP/T00519X/1)
uture and Emerging Technologies (FET) programme of the European Commission (grant agreement ID 828818).