AI summary
Problem
Existing elastography techniques rely on expensive, bulky medical imaging equipment, while traditional robot palpation only yields low-dimensional or binary lump detection rather than rich 3D material mapping.
Approach
The method captures high-resolution tactile deformation data during active pressing and uses an inverse finite element method to solve for the object's internal Young's modulus distribution.
Key results
- Dense tactile data enables accurate 3D elasticity reconstruction
- Inverse FEM physics model reliably estimates internal stiffness
- Method validated on both simulated and physical soft objects
- Outperforms learning-based approaches in lump detection tasks
Why it matters
Provides robots with an accessible, low-cost method to perceive internal material properties, advancing soft object interaction and robot-assisted medical palpation.
Abstract
Elasticity is one of the representative parameters that reflect the mechanical properties of soft materials. Detecting the underneath elasticity distribution called elastography is a key step for understanding and interacting with objects. Existing solutions for capturing the interior elasticity distribution typically rely on expensive apparatus. In this work, the dense tactile signal captured by the high-resolution vision-based tactile sensor is introduced as a new modality for reconstructing 3-D elasticity distribution. We propose a model-based method, which exploits the tactile maps from active pressing trials for the elastography task. The interior elasticity distribution for nonrigid objects is reconstructed from an inverse physics model. We analyze the credibility of the estimated elasticity distribution obtained from our method. Varying design factors are also discussed. We experiment our method on a set of synthesized 3-D models and physical models in robot-assisted scenes. Various experimental results have been gathered, demon- strating the efficacy of our approach in perceiving elasticity distri- bution.