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Register a new userPerception of Surface Defects by Active Exploration with a Biomimetic Tactile Sensor
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Authors
Raphael Candelier
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We investigate the transduction of tactile information during active exploration of finely textured surfaces using a novel tactile sensor mimicking the human fingertip. The sensor has been designed by integrating a linear array of 10 micro-force sensors in an elastomer layer. We measure the sensors response to the passage of elementary topographical features in the form of a small hole on a flat substrate. The response is found to strongly depend on the relative location of the sensor with respect to the substrate/skin contact zone. This result can be quantitatively interpreted within the scope of a linear model of mechanical transduction, taking into account both the intrinsic response of individual sensors and the context-dependent interfacial stress field within the contact zone. Consequences on robotics of touch are briefly discussed.
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An experimental biomimetic tongue-palate system has been developed to probe human in-mouth texture perception. Model tongues are made from soft elastomers patterned with fibrillar structures analogue to human filiform papillae. The palate is represented by a rigid flat plate parallel to the plane of the tongue. To probe the behavior under physiological flow conditions, deflections of model papillae are measured using a novel fluorescent imaging technique enabling sub-micrometer resolution of the displacements. Using optically transparent newtonian liquids under steady shear flow, we show that deformations of the papillae allow determining their viscosity from 1 Pa.s down to the viscosity of water of 1 mPa.s, in full quantitative agreement with a recently proposed model [Lauga et al., Frontiers in Physics, 2016, 4, 35]. The technique is further validated for a shear-thinning and optically opaque dairy system.
Haptic exploration is a key skill for both robots and humans to discriminate and handle unknown objects or to recognize familiar objects. Its active nature is evident in humans who from early on reliably acquire sophisticated sensory-motor capabilities for active exploratory touch and directed manual exploration that associates surfaces and object properties with their spatial locations. This is in stark contrast to robotics. In this field, the relative lack of good real-world interaction models - along with very restricted sensors and a scarcity of suitable training data to leverage machine learning methods - has so far rendered haptic exploration a largely underdeveloped skill. In the present work, we connect recent advances in recurrent models of visual attention with previous insights about the organisation of human haptic search behavior, exploratory procedures and haptic glances for a novel architecture that learns a generative model of haptic exploration in a simulated three-dimensional environment. The proposed algorithm simultaneously optimizes main perception-action loop components: feature extraction, integration of features over time, and the control strategy, while continuously acquiring data online. We perform a multi-module neural network training, including a feature extractor and a recurrent neural network module aiding pose control for storing and combining sequential sensory data. The resulting haptic meta-controller for the rigid $16 times 16$ tactile sensor array moving in a physics-driven simulation environment, called the Haptic Attention Model, performs a sequence of haptic glances, and outputs corresponding force measurements. The resulting method has been successfully tested with four different objects. It achieved results close to $100 %$ while performing object contour exploration that has been optimized for its own sensor morphology.
The modification of the wetting properties of marble surfaces by a biomimetic laser processing approach has been investigated. Marble surfaces have been irradiated with ultrashort laser pulses in different conditions to analyze the effect of laser treatment on their wetting properties with the aim of evaluating its potential for surface protection. The contact angle of a water drop placed on the surface was used to assess the wettability of the processed areas versus the pristine surface. Although the surfaces are initially hydrophilic upon laser treatment, after a few days they develop a strong hydrophobic behavior. The time evolution of the contact angle has been monitored up to 11 months after treatment. A short and a long-term evolution, associated to the combined effect of multi-scale roughness (nano- and micro-roughness) and the attachment of chemical species at the surface over the time, has been observed. Through an analysis of the temporal evolution of surfaces processed with different laser scan line separations, the relative roles of multi-scale roughness and chemical changes has been elucidated and correlated to the induced morphologies. Micro-roughness manifests as a noticeable micro-scale topography modulation, leading to a hydrophobic behavior consistent with the Cassie-Baxter model. In turn, the superimposed nano-roughness leads to an increase of the effective surface area, enhancing the attachment of chemical functional groups over time that progressively modify the surface energy. The durability of the improved surface properties (hydrophobicity) has been tested by measurements of the contact angle of the stabilized (aged) surfaces after cleaning in different conditions with very positive results.
We present a modified TacTip biomimetic optical tactile sensor design which demonstrates the ability to induce and detect incipient slip, as confirmed by recording the movement of markers on the sensors external surface. Incipient slip is defined as slippage of part, but not all, of the contact surface between the sensor and object. The addition of ridges - which mimic the friction ridges in the human fingertip - in a concentric ring pattern allowed for localised shear deformation to occur on the sensor surface for a significant duration prior to the onset of gross slip. By detecting incipient slip we were able to predict when several differently shaped objects were at risk of falling and prevent them from doing so. Detecting incipient slip is useful because a corrective action can be taken before slippage occurs across the entire contact area thus minimising the risk of objects been dropped.
Reproducing the capabilities of the human sense of touch in machines is an important step in enabling robot manipulation to have the ease of human dexterity. A combination of robotic technologies will be needed, including soft robotics, biomimetics and the high-resolution sensing offered by optical tactile sensors. This combination is considered here as a SoftBOT (Soft Biomimetic Optical Tactile) sensor. This article reviews the BRL TacTip as a prototypical example of such a sensor. Topics include the relation between artificial skin morphology and the transduction principles of human touch, the nature and benefits of tactile shear sensing, 3D printing for fabrication and integration into robot hands, the application of AI to tactile perception and control, and the recent step-change in capabilities due to deep learning. This review consolidates those advances from the past decade to indicate a path for robots to reach human-like dexterity.
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