Curvature and force estimation for a soft finger using an EKF with unknown input optimization

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3 Citations (Scopus)

Abstract

Sensory data such as bending curvature and contact force are essential for controlling soft robots. However, it is inconvenient to measure these variables because sensorizing soft robots is difficult due to their inherent softness. An attractive alternative is to use an observer/filter to estimate the variables that would have been measured by those sensors. Nevertheless, an observer/filter requires a model which can be analytically demanding for soft robots due to their high nonlinearity. In this paper, we propose an Unknown Input Extended Kalman Filter (UI-EKF) consisting of an EKF interconnected with a UI-optimizer to respectively estimate the state (curvature) and unknown input (contact force) for a pneumatic-based soft finger based on an identified nonlinear model. We also prove analytically that the estimation errors are bounded. Experimental results show that the UI-EKF can perform the estimation with high accuracy, even when the identified system model is not accurate and the sensor measurement is noisy. In other words, the proposed framework is able to estimate proprioceptive (internal) and exteroceptive (external) variables (curvature and contact force respectively) of the robot using a single sensor (flex), which is still an open problem in soft robotics.

Original languageEnglish
Title of host publication21st IFAC World Congress 2020
PublisherElsevier - International Federation of Automatic Control (IFAC)
Pages8506-8512
Number of pages7
Volume53
Edition2
DOIs
Publication statusPublished - 2020
EventInternational Federation of Automatic Control World Congress 2020 - Berlin, Germany
Duration: 12 Jul 202017 Jul 2020
Conference number: 21st
https://www.sciencedirect.com/journal/ifac-papersonline/vol/53/issue/2 (IFAC PapersOnline — ISSN 2405-8963 Volume 53, Issue 2 )

Publication series

NameIFAC-PapersOnLine

Conference

ConferenceInternational Federation of Automatic Control World Congress 2020
Abbreviated titleIFAC 2020
Country/TerritoryGermany
CityBerlin
Period12/07/2017/07/20
Internet address

Keywords

  • Extended kalman filters
  • Lyapunov stability
  • Neural-network models
  • Robotics
  • Stochastic systems
  • Unknown input estimation

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