Recordable Haptic textures
01 Jan 2006-pp 130-133
TL;DR: A method to record the surface texture of real life objects like metal files, sandpaper etc that can subsequently be played back on virtual surfaces using commonly available haptic hardware and the 3DOF SensAble PHANToM is presented.
Abstract: In this paper we present a method to record the surface texture of real life objects like metal files, sandpaper etc. These textures can subsequently be played back on virtual surfaces. Our method has the advantage that it can record textures using commonly available haptic hardware. We use the 3DOF SensAble PHANToM to record the textures. The algorithm involves creating recordings of the frequency content of a real surface, by exploring it with a haptic device. We estimate the frequency spectra at two different velocities, and subsequently interpolate between them on a virtual surface. The extent of correlation between real and simulated spectra was estimated and a near exact spectral match was obtained. The simulated texture was played back using the same haptic device. The algorithm to record and playback textures is simple and can be easily implemented for planar surfaces with uniform textures
Citations
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TL;DR: This paper presents a set of methods for creating a haptic texture model from tool-surface interaction data recorded by a human in a natural and unconstrained manner and uses these texture model sets to render synthetic vibration signals in real time as a user interacts with the TexturePad system.
Abstract: Texture gives real objects an important perceptual dimension that is largely missing from virtual haptic interactions due to limitations of standard modeling and rendering approaches. This paper presents a set of methods for creating a haptic texture model from tool-surface interaction data recorded by a human in a natural and unconstrained manner. The recorded high-frequency tool acceleration signal, which varies as a function of normal force and scanning speed, is segmented and modeled as a piecewise autoregressive (AR) model. Each AR model is labeled with the source segment's median force and speed values and stored in a Delaunay triangulation to create a model set for a given texture. We use these texture model sets to render synthetic vibration signals in real time as a user interacts with our TexturePad system, which includes a Wacom tablet and a stylus augmented with a Haptuator. We ran a human-subject study with two sets of ten participants to evaluate the realism of our virtual textures and the strengths and weaknesses of this approach. The results indicated that our virtual textures accurately capture and recreate the roughness of real textures, but other modeling and rendering approaches are required to completely match surface hardness and slipperiness.
140 citations
Cites background from "Recordable Haptic textures"
...types of data used to model textures include friction variations [25] and vertical perturbations [26] resulting from dragging across a surface....
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TL;DR: The tactual scanning of five naturalistic textures was recorded with an apparatus that showed that the transformation from the geometry of a surface to the force of traction and, hence, to the skin deformation experienced by a finger is a highly nonlinear process and speculated that the mechanical properties of the finger enables spatial information to be used for perceptual purposes in humans with no distributed sensing.
Abstract: The tactual scanning of five naturalistic textures was recorded with an apparatus that is capable of measuring the tangential interaction force with a high degree of temporal and spatial resolution. The resulting signal showed that the transformation from the geometry of a surface to the force of traction and, hence, to the skin deformation experienced by a finger is a highly nonlinear process. Participants were asked to identify simulated textures reproduced by stimulating their fingers with rapid, imposed lateral skin displacements as a function of net position. They performed the identification task with a high degree of success, yet not perfectly. The fact that the experimental conditions eliminated many aspects of the interaction, including low-frequency finger deformation, distributed information, as well as normal skin movements, shows that the nervous system is able to rely on only two cues: amplitude and spectral information. The examination of the “spatial spectrograms” of the imposed lateral skin displacement revealed that texture could be represented spatially, despite being sensed through time and that these spectrograms were distinctively organized into what could be called “spatial formants.” This finding led us to speculate that the mechanical properties of the finger enables spatial information to be used for perceptual purposes in humans with no distributed sensing, which is a principle that could be applied to robots.
122 citations
Cites background from "Recordable Haptic textures"
...eration of a stylus is measured, or in [24], where the scanning velocity is measured....
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20 Mar 2014
TL;DR: A method for resampling the texture models so they can be rendered at a sampling rate other than the 10 kHz used when recording data, to increase the adaptability and utility of HaTT.
Abstract: This paper introduces the Penn Haptic Texture Toolkit (HaTT), a publicly available repository of haptic texture models for use by the research community. HaTT includes 100 haptic texture and friction models, the recorded data from which the models were made, images of the textures, and the code and methods necessary to render these textures using an impedance-type haptic interface such as a SensAble Phantom Omni. This paper reviews our previously developed methods for modeling haptic virtual textures, describes our technique for modeling Coulomb friction between a tooltip and a surface, discusses the adaptation of our rendering methods for display using an impedance-type haptic device, and provides an overview of the information included in the toolkit. Each texture and friction model was based on a ten-second recording of the force, speed, and high-frequency acceleration experienced by a handheld tool moved by an experimenter against the surface in a natural manner. We modeled each texture's recorded acceleration signal as a piecewise autoregressive (AR) process and stored the individual AR models in a Delaunay triangulation as a function of the force and speed used when recording the data. To increase the adaptability and utility of HaTT, we developed a method for resampling the texture models so they can be rendered at a sampling rate other than the 10 kHz used when recording data. Measurements of the user's instantaneous normal force and tangential speed are used to synthesize texture vibrations in real time. These vibrations are transformed into a texture force vector that is added to the friction and normal force vectors for display to the user.
101 citations
14 Apr 2013
TL;DR: A new method for creating haptic texture models from data recorded during natural and unconstrained motions using a new haptic recording device and presents a new spectral metric for determining perceptual match of the models in order to evaluate the effectiveness and consistency of the segmenting and modeling approach.
Abstract: If you pick up a tool and drag its tip across a table, a rock, or a swatch of fabric, you are able to feel variations in the textures even though you are not directly touching them. These vibrations are characteristic of the material and the motions made when interacting with the surface. This paper presents a new method for creating haptic texture models from data recorded during natural and unconstrained motions using a new haptic recording device. The recorded vibration data is parsed into short segments that represent the feel of the surface at the associated tool force and speed. We create a low-order auto-regressive (AR) model for each data segment and construct a Delaunay triangulation of models in force-speed space for each surface. During texture rendering, we stably interpolate between these models using barycentric coordinates and drive the interpolated model with white noise to output synthetic vibrations. Our methods were validated through application to data recorded by eight human subjects and the experimenter interacting with six textures. We present a new spectral metric for determining perceptual match of the models in order to evaluate the effectiveness and consistency of the segmenting and modeling approach. Multidimensional scaling (MDS) on the pairwise differences in the synthesized vibrations shows that the 54 created texture models cluster by texture in a two-dimensional perceptual space.
61 citations
Cites background from "Recordable Haptic textures"
...Previous work in data-driven texture modeling [11,24,26,29] has shown that high-frequency vibrations created during interactions with real textures can be recreated from models made with recorded data....
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04 Mar 2012
TL;DR: The TexturePad system as mentioned in this paper uses a low-order auto-regressive moving-average (ARMA) model for texture modeling and rendering to generate a stable and spectrally accurate vibration waveform in real time.
Abstract: Dragging a tool across a textured object creates rich high-frequency vibrations that distinctly convey the physical interaction between the tool tip and the object surface. Varying one's scanning speed and normal force alters these vibrations, but it does not change the perceived identity of the tool or the surface. Previous research developed a promising data-driven approach to embedding this natural complexity in a haptic virtual environment: the approach centers on recording and modeling the tool contact accelerations that occur during real texture interactions at a limited set of force-speed combinations. This paper aims to optimize these prior methods of texture modeling and rendering to improve system performance and enable potentially higher levels of haptic realism. The key elements of our approach are drawn from time series analysis, speech processing, and discrete-time control. We represent each recorded texture vibration with a low-order auto-regressive moving-average (ARMA) model, and we optimize this set of models for a specific tool-surface pairing (plastic stylus and textured ABS plastic) using metrics that depend on spectral match, final prediction error, and model order. For rendering, we stably resample the texture models at the desired output rate, and we derive a new texture model at each time step using bilinear interpolation on the line spectral frequencies of the resampled models adjacent to the user's current force and speed. These refined processes enable our TexturePad system to generate a stable and spectrally accurate vibration waveform in real time, moving us closer to the goal of virtual textures that are indistinguishable from their real counterparts.
52 citations
Cites methods from "Recordable Haptic textures"
...Vasudevan and Manivannan [26] used a SensAble PHANToM to drag across a textured surface and modeled the resulting haptic texture from the frequency spectrum of the tooltip’s vertical displacements....
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References
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10 Oct 2004
TL;DR: This work presents a new algorithm to display haptic texture information resulting from the interaction between two textured objects that is able to haptically display interaction due to fine surface textures that previous algorithms do not capture.
Abstract: Surface texture is among the most salient haptic characteristics of objects; it can induce vibratory contact forces that lead to perception of roughness In this paper, we present a new algorithm to display haptic texture information resulting from the interaction between two textured objects We compute contact forces and torques using low-resolution geometric representations along with texture images that encode surface details We also introduce a novel force model based on directional penetration depth and describe an efficient implementation on programmable graphics hardware that enables interactive haptic texture rendering of complex models Our force model takes into account important factors identified by psychophysics studies and is able to haptically display interaction due to fine surface textures that previous algorithms do not capture
74 citations
"Recordable Haptic textures" refers methods in this paper
...Minsky.et.al [1] present a ‘Sandpaper’ system of displaying haptic texture where a gradient technique is used to display a 3D texture in terms of 2D forces....
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TL;DR: This work systematically investigates the unrealistic behavior of virtual haptic textures in haptic texture rendering, the goal of which is to introduce micro-geometry-scale features on object surfaces.
Abstract: New sophisticated haptic-rendering algorithms let users experience virtual objects through touch. We systematically investigate the unrealistic behavior of virtual haptic textures. The emerging science of haptic rendering consists of delivering properties of physical objects through the sense of touch. Owing to the recent development of sophisticated haptic-rendering algorithms, users can now experience virtual objects through touch in many exciting applications, including surgical simulations, virtual prototyping, and data perceptualization. Haptics holds great promise to enrich the sensory attributes of virtual objects that these systems can produce. One area that has received increasing attention in the haptics community is haptic texture rendering, the goal of which is to introduce micro-geometry-scale features on object surfaces. Haptic objects rendered without textures usually feel smooth, and sometimes slippery. Appropriate haptic textures superimposed on haptic objects enhance an object's realism.
54 citations
31 Jul 2005
TL;DR: This work systematically investigates the unrealistic behavior of virtual haptic textures in haptic texture rendering, the goal of which is to introduce micro-geometry-scale features on object surfaces.
Abstract: The emerging science of haptic rendering consists of delivering properties of physical objects through the sense of touch. Owing to the recent development of sophisticated haptic-rendering algorithms, users can now experience virtual objects through touch in many exciting applications, including surgical simulations, virtual prototyping, and data per-ceptualization. Haptics holds great promise to enrich the sensory attributes of virtual objects that these systems can produce.
44 citations
"Recordable Haptic textures" refers background in this paper
...Ho et.al [2] present a modification of ‘bump maps’ borrowed from computer graphics to create texture in haptic environments....
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07 Aug 2004
TL;DR: The first force model for haptic display of interaction between two textured objects is developed and it is shown that the model captures similar effects to those observed in the earlier experiments on roughness perception.
Abstract: One of the most salient haptic characteristics of objects is surface texture. Psychophysics studies have identified several key factors that affect perception of roughness during exploration of surface textures. Inspired by these recent findings, we develop the first force model for haptic display of interaction between two textured objects. We describe how our force model accounts for important elements identified by psychophysics studies. We then analyze and validate our model by comparing our simulation results against actual perceptual studies. We show that our model captures similar effects to those observed in the earlier experiments on roughness perception.
40 citations
"Recordable Haptic textures" refers methods in this paper
...The performance of the algorithm is estimated by comparing the frequency spectrum of the texture on real and virtual surfaces....
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25 Mar 2006
TL;DR: The enhancement procedures focused on here include techniques for pseudo-texturing in virtual environments that include stochastic, correlated, and harmonic models as well as several popular halftoning algorithms such as Amplitude Modulation, Bayer's, and Error Diffusion.
Abstract: The haptic environment, due to the limited bandwidth of haptic sense, must be enhanced to aid comprehension. The enhancement procedures focused on here include techniques for pseudo-texturing in virtual environments. Generated texture models are automatically tunable according to the characteristics or properties of data to which it is applied. The proposed methods include stochastic, correlated, and harmonic models as well as several popular halftoning algorithms such as Amplitude Modulation (AM), Bayer's, and Error Diffusion. Presented analysis determines the parameter change required to yield the minimal perceivable change in texture for each texture level and proposed texturing method. We also consider edge and boundary enhancement. Finally, user evaluations are presented comparing the presented methods in a haptic visualization application.
12 citations
"Recordable Haptic textures" refers methods in this paper
...Minsky.et.al [1] present a ‘Sandpaper’ system of displaying haptic texture where a gradient technique is used to display a 3D texture in terms of 2D forces....
[...]