4.6 Article

Using Redundant and Disjoint Time-Variant Soft Robotic Sensors for Accurate Static State Estimation

Journal

IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 6, Issue 2, Pages 2099-2105

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2021.3061399

Keywords

Soft sensors and actuators; sensor fusion; modeling; control; learning for soft robots

Categories

Funding

  1. SHERO project, a Future and Emerging Technologies (FET) programme of the European Commission [828818]

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Traditional soft robotic sensors are limited by their highly nonlinear time variant behavior. Current research focuses on improving mechano-electrical properties or modeling algorithms. This study presents a method for combining multi-material soft strain sensors to obtain higher quality sensors, allowing for accurate estimation of the strain state of a system.
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.

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