An innovative methodology to realize a sensing system able to estimate the shape of a soft robot arm without hampering 'softness' is presented. The system is based on a low-cost plastic optical fiber (POF) used as curvature sensor and on a simplified steady-state model, both integrated in an Adaptive Extended Kalman Filter (AEKF). Sensory feedback was obtained through accelerometers and it was used as quantitative benchmark for the AEKF. The AEKF estimation turned out to be more accurate (RMS error < 5°) than the model prediction alone and the soft sensor alone, thus supporting the proposed fully soft proprioception strategy.

Shape estimation based on Kalman filtering: Towards fully soft proprioception

Lunni, Dario;GIORDANO, GOFFREDO;Sinibaldi, Edoardo;Cianchetti, Matteo;Mazzolai, Barbara
2018-01-01

Abstract

An innovative methodology to realize a sensing system able to estimate the shape of a soft robot arm without hampering 'softness' is presented. The system is based on a low-cost plastic optical fiber (POF) used as curvature sensor and on a simplified steady-state model, both integrated in an Adaptive Extended Kalman Filter (AEKF). Sensory feedback was obtained through accelerometers and it was used as quantitative benchmark for the AEKF. The AEKF estimation turned out to be more accurate (RMS error < 5°) than the model prediction alone and the soft sensor alone, thus supporting the proposed fully soft proprioception strategy.
2018
9781538645161
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11382/524115
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 28
social impact