Contact Modelling and Tactile Data Processing for Robot Skins

Wasko, Wojciech; Albini, Alessandro; Maiolino, Perla; Mastrogiovanni, Fulvio; Cannata, Giorgio

doi:10.3390/s19040814

Cited by 8 publications

(4 citation statements)

References 38 publications

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“…We recorded the sensor frequency-response function one-shot over the whole frequency range. The numerical model described in Section 2.3 has been integrated into the LabVIEW software, directly giving the frequency behavior of the d 33 piezoelectric modulus (both real and imaginary parts) of each solicited sensor, calculated from the sensor frequency response function as for (2). Sigma values have been extracted from Figure 7, each time in accordance with the specific preload and sensor radius.…”

Section: Experimental Testsmentioning

confidence: 99%

“…each solicited sensor, calculated from the sensor frequency response function as for (2). Sigma values have been extracted from Figure 7, each time in accordance with the specific preload and sensor radius.…”

Section: Frequency Range Selectionmentioning

confidence: 99%

“…Electronic skin (e-skin) is a touch-sensitive, electronic system that incorporates functional and structural materials coupled to a suitable electronic interface for sensor signal acquisition. Tactile data processing algorithms might provide information about contact properties (e.g., contact force [1] or contact shape [2]), given properties of the contact object (such as surface texture [3], object shape [4]), or contact events (e.g., discrimination between touch modalities [5]), to cite some examples. Artificial skin systems are implemented in a wide range of applications, such as robotics, prosthetics and teleoperation systems [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Validation of Screen-Printed Electronic Skin Based on Piezoelectric Polymer Sensors

Fares

Abbass

Valle

et al. 2020

Sensors

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This paper proposes a validation method of the fabrication technology of a screen-printed electronic skin based on polyvinylidene fluoride-trifluoroethylene P(VDF-TrFE) piezoelectric polymer sensors. This required researchers to insure, through non-direct sensor characterization, that printed sensors were working as expected. For that, we adapted an existing model to non-destructively extract sensor behavior in pure compression (i.e., the d33 piezocoefficient) by indentation tests over the skin surface. Different skin patches, designed to sensorize a glove and a prosthetic hand (11 skin patches, 104 sensors), have been tested. Reproducibility of the sensor response and its dependence upon sensor position on the fabrication substrate were examined, highlighting the drawbacks of employing large A3-sized substrates. The average value of d33 for all sensors was measured at incremental preloads (1–3 N). A systematic decrease has been checked for patches located at positions not affected by substrate shrinkage. In turn, sensor reproducibility and d33 adherence to literature values validated the e-skin fabrication technology. To extend the predictable behavior to all skin patches and thus increase the number of working sensors, the size of the fabrication substrate is to be decreased in future skin fabrication. The tests also demonstrated the efficiency of the proposed method to characterize embedded sensors which are no more accessible for direct validation.

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Section: Experimental Testsmentioning

confidence: 99%

Section: Frequency Range Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Validation of Screen-Printed Electronic Skin Based on Piezoelectric Polymer Sensors

Fares

Abbass

Valle

et al. 2020

Sensors

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“…At the lowest level, tactile sensor arrays can be used to find contact locations with a high resolution, and to track variation of contact points. Model-based approaches can be used to retrieve higher-level information related to contact features, such as contact force distributions (Seminara et al, 2015) or contact shape (Khan et al, 2016; Wasko et al, 2019). In all cases, such algorithms are needed to embed 3D sensor locations into a lower dimensional space representing the robot surface, and a further processing step is required to move toward a higher dimensional space by computing features.…”

Section: Closed-loop Sensorimotor Control Of Robot Hands: a New Tamentioning

confidence: 99%

Active Haptic Perception in Robots: A Review

et al. 2019

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In the past few years a new scenario for robot-based applications has emerged. Service and mobile robots have opened new market niches. Also, new frameworks for shop-floor robot applications have been developed. In all these contexts, robots are requested to perform tasks within open-ended conditions, possibly dynamically varying. These new requirements ask also for a change of paradigm in the design of robots: on-line and safe feedback motion control becomes the core of modern robot systems. Future robots will learn autonomously, interact safely and possess qualities like self-maintenance. Attaining these features would have been relatively easy if a complete model of the environment was available, and if the robot actuators could execute motion commands perfectly relative to this model. Unfortunately, a complete world model is not available and robots have to plan and execute the tasks in the presence of environmental uncertainties which makes sensing an important component of new generation robots. For this reason, today's new generation robots are equipped with more and more sensing components, and consequently they are ready to actively deal with the high complexity of the real world. Complex sensorimotor tasks such as exploration require coordination between the motor system and the sensory feedback. For robot control purposes, sensory feedback should be adequately organized in terms of relevant features and the associated data representation. In this paper, we propose an overall functional picture linking sensing to action in closed-loop sensorimotor control of robots for touch (hands, fingers). Basic qualities of haptic perception in humans inspire the models and categories comprising the proposed classification. The objective is to provide a reasoned, principled perspective on the connections between different taxonomies used in the Robotics and human haptic literature. The specific case of active exploration is chosen to ground interesting use cases. Two reasons motivate this choice. First, in the literature on haptics, exploration has been treated only to a limited extent compared to grasping and manipulation. Second, exploration involves specific robot behaviors that exploit distributed and heterogeneous sensory data.

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