Topography compensation for haptization of a mesh object and its stiffness distribution

This work was motivated by the need for perceptualizing nano-scale scientific data, e.g., those acquired by a scanning probe microscope, where collocated topography and stiffness distribution of a surface can be measured. Previous research showed that when the topography of a surface with spatially varying stiffness is rendered using the conventional penalty-based haptic rendering method, the topography perceived by the user could be significantly distorted from its original model. In the worst case, a higher region with a smaller stiffness value can be perceived to be lower than a lower region with a larger stiffness value. This problem was explained by the theory of force constancy: the user tends to maintain an invariant contact force when s/he strokes the surface to perceive its topography. In this paper, we present a haptization algorithm that can render the shape of a mesh surface and its stiffness distribution with high perceptual accuracy. Our algorithm adaptively changes the surface topography on the basis of the force constancy theory to deliver adequate shape information to the user while preserving the stiffness perception. We also evaluated the performance of the proposed haptization algorithm in comparison to the constraint-based algorithm by examining relevant proximal stimuli and carrying out a user experiment. Results demonstrated that our algorithm could improve the perceptual accuracy of shape and reduce the exploration time, thereby leading to more accurate and efficient haptization.