A recurrent neural network approach for 3d vision-based force estimation
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
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.
CitationAviles, A. [et al.]. A recurrent neural network approach for 3d vision-based force estimation. A: IEEE International Conference on Image Processing Theory, Tools and Applications. "4th IEEE International Conference on Image Processing Theory, Tools and Applications, IPTA : Paris, France, October 2014". Paris: Institute of Electrical and Electronics Engineers (IEEE), 2014, p. 1-6.
- IBEC - Institute for Bioengineering of Catalonia - Ponències/Comunicacions de congressos 
- ICAIB - Grup de Recerca en Intel ligència Computacional per a l'Anàlisi d'Imatge Biomèdica - Ponències/Comunicacions de congressos 
- GRINS - Robòtica Intel·ligent i Sistemes - Ponències/Comunicacions de congressos 
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Ponències/Comunicacions de congressos 
- Departament de Matemàtiques - Ponències/Comunicacions de congressos 
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