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Título:
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A recurrent neural network approach for 3d vision-based force estimation
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Autor/a:
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Avilés Rivero, Angélica Ivone; Marbán González, Arturo; Sobrevilla Frisón, Pilar; Casals Gelpi, Alicia; Fernández Ruzafa, José
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Otros autores:
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Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Universitat Politècnica de Catalunya. Departament de Matemàtica Aplicada II; Institut de Bioenginyeria de Catalunya; Universitat Politècnica de Catalunya. ICAIB - Grup de Recerca en Intel ligència Computacional per a l'Anàlisi d'Imatge Biomèdica; Universitat Politècnica de Catalunya. GRINS - Grup de Recerca en Robòtica Intel·ligent i Sistemes |
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Abstract:
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Robotic-assisted minimally invasive surgery has demonstrated its benefits in comparison with traditional procedures. However, one of the major drawbacks of current robotic system approaches is the lack of force feedback. Apart from space restrictions, the main problems of using force sensors are their high cost and the biocompatibility. In this work a proposal based on Vision Based Force Measurement is presented, in which the deformation mapping of the tissue is obtained using the L2-Regularized Optimization class, and the force is estimated via a recurrent neural network that has as inputs the kinematic variables and the deformation mapping. Moreover, the capability of RNN for predicting time series is used in order to deal with tool occlusions. The highlights of this proposal, according to the results, are: knowledge of material properties are not necessary, there is no need of adding extra sensors and a good trade-off between accuracy and efficiency has been achieved. |
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Abstract:
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Peer Reviewed |
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Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
-Àrees temàtiques de la UPC::Informàtica::Robòtica
-Image processing -- Medical applications
-Force estimation
-regularized optimization
-deformable tracking
-recurrent neural network
-Imatges -- Processament -- Tècniques digitals
-Enginyeria biomèdica |
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Derechos:
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Tipo de documento:
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Artículo - Versión publicada
Objeto de conferencia |
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Editor:
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Institute of Electrical and Electronics Engineers (IEEE)
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Compartir:
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