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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 non-linear 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 (μF , σF2 )) and material stiffness (k ∼ N (μk, σk2 )) 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 non-linear, 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.
|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||Australasian Conference on Robotics and Automation 2019|
|Abbreviated title||ACRA 2019|
|Period||9/12/19 → 11/12/19|
FingerprintDive 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.
- 1 Finished
1/01/16 → 31/03/22