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Abstract
In surgical robot simulation and motion planning a forward (or predictive) model is critical. This paper considers an initial approach to estimate the effect of the variation of parameters on the overall statistical distribution for the total strain and stress as predicted by a nonlinear biomechanical model. This considers the statistical distribution, as compared to a single mean value, of the predicted variables. For example, if the material were an ideal a Hookean spring (i.e., F = kx), then Gaussian variations in force (F ∼ N (μ_{F} , σ_{F}^{2} )) and material stiffness (k ∼ N (μ_{k}, σ_{k}^{2} )) would give a Ratio distribution in displacement x (i.e., the distribution formed from the ratio of two Gaussians). In order to evaluate the case when the material properties are nonlinear, such as those for brain tissue, this paper explores an empirical approach based around a sequence of multiple cases where the strain was determined via FEA. When assuming an Ogden hyperelastic constitutive model, results show a Normal variation of displacement for Normal varying moduli, but a Bimodal distribution of displacement for Normal varying forces.
Original language  English 

Title of host publication  ACRA 2019 Proceedings 
Publisher  Australian Robotics & Automation Association 
Number of pages  7 
Publication status  Published  2019 
Event  Australasian Conference on Robotics and Automation 2019  Adelaide, Australia Duration: 9 Dec 2019 → 11 Dec 2019 
Conference
Conference  Australasian Conference on Robotics and Automation 2019 

Abbreviated title  ACRA 2019 
Country/Territory  Australia 
City  Adelaide 
Period  9/12/19 → 11/12/19 
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Dive into the research topics of 'Towards a probabilistic approach for quantifying uncertainty in robot soft tissue indentation via computational biomechanics models'. Together they form a unique fingerprint.Projects
 1 Finished

Biomechanics Meets Robotics: Methods for Accurate and Fast Needle Targeting
Wittek, A., Singh, S., Miller, K., Hannaford, B. & Fichtinger, G.
1/01/16 → 31/03/22
Project: Research