Model of tactile sensors using soft contacts and its application in robot grasping simulation

Moisio, Sami; León, Beatriz; Korkealaakso, Pasi; Morales, Antonio

doi:10.1016/j.robot.2012.10.008

Cited by 22 publications

(9 citation statements)

References 38 publications

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“…In [4], Finite Element Analysis was used instead to model the deformations of the elastomer. In addition to the distribution of the deformations, the holding torque around the contact surface and the stick-slip phenomenon are modeled using the LuGre dynamic friction model in [3]. For simulating marker-based sensors, the markers displacement can be obtained and tracked [23].…”

Section: Related Work a Simulation Of Optical Tactile Sensorsmentioning

confidence: 99%

“…There have been some works to simulate certa in properties of touch sensors, such as friction forces for resistive sensors [3], surface deformations for optical marker-based tactile sensors [4], and tactile contacts (both detection of contacts and contact locations) for capacitive tactile cells in grasping [5]. However, due to the use of dielectrics [4], [5] or tracking sparse points [4], [6], these tactile sensors suffer from low resolution, for instance, a commercial Weiss tactile sensor of 14x6 tactile cells simulated in [3].…”

Section: Introductionmentioning

confidence: 99%

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Generation of GelSight Tactile Images for Sim2Real Learning

Gomes

Paoletti

Luo

2021

IEEE Robot. Autom. Lett.

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Most current works in Sim2Real learning for robotic manipulation tasks leverage camera vision that may be significantly occluded by robot hands during the manipulation. Tactile sensing offers complementary information to vision and can compensate for the information loss caused by the occlusions. However, the use of tactile sensing is restricted in the Sim2Real research due to no simulated tactile sensors being available. To mitigate the gap, we introduce a novel approach for simulating a GelSight tactile sensor in the commonly used Gazebo simulator. Similar to the real GelSight sensor, the simulated sensor can produce high-resolution images from depth-maps captured by a simulated optical sensor, and reconstruct the interaction between the touched object and an opaque soft membrane. It can indirectly sense forces, geometry, texture and other properties of the object and enables Sim2Real learning with tactile sensing. Preliminary experimental results have shown that the simulated sensor could generate realistic outputs similar to the ones captured by a real GelSight sensor. All the materials used in this paper are available at https://danfergo.github.io/gelsightsimulation.

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Section: Related Work a Simulation Of Optical Tactile Sensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generation of GelSight Tactile Images for Sim2Real Learning

Gomes

Paoletti

Luo

2021

IEEE Robot. Autom. Lett.

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“…The goal of our research is to construct a versatile gripping system [ 10 , 11 ] at a low cost using a commercially available robot hand. Therefore, a cushioning function is also required so that a soft object is not crushed when it is gripped.…”

Section: Introductionmentioning

confidence: 99%

Relationship between Photoelasticity of Polyurethane and Dielectric Anisotropy of Diisocyanate, and Application of High-Photoelasticity Polyurethane to Tactile Sensor for Robot Hands

et al. 2020

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Eight types of polyurethane were synthesized using seven types of diisocyanate. It was found that the elasto-optical constant depends on the concentration of diisocyanate groups in a unit volume of a polymer and the magnitude of anisotropy of the dielectric constant of diisocyanate groups. It was also found that incident light scattered when bending stress was generated inside photoelastic polyurethanes. A high sensitive tactile sensor for robot hands was devised using one of the developed polyurethanes with high photoelasticity.

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“…In contrast to SkinSim, the simulation model of the OpenGrasp skin [7] includes one rigid body only. The body is represented by a triangularization of its surface.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Tactile Sensing Arrays for Physical Interaction Tasks

Kappassov

Ramón

Perdereau

2020

2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

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Simulated worlds are important enablers and accelerators of new algorithms for autonomous robot applications. A framework for tactile servoing in the simulated world is presented. This framework includes a general model of tactile sensing arrays that can simulate the behavior of a real tactile sensing array thanks to an empirical characterization procedure. After obtaining the precise sensor model, different tactile servoing schemes can be implemented in the framework by controlling contact features, including points and lines extracted from the simulated contact images. Several experiments have been performed in order to guarantee the correspondence between the simulated results generated by the framework and the real ones executed with different sensors and robots.

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Model of tactile sensors using soft contacts and its application in robot grasping simulation

Cited by 22 publications

References 38 publications

Generation of GelSight Tactile Images for Sim2Real Learning

Generation of GelSight Tactile Images for Sim2Real Learning

Relationship between Photoelasticity of Polyurethane and Dielectric Anisotropy of Diisocyanate, and Application of High-Photoelasticity Polyurethane to Tactile Sensor for Robot Hands

Simulation of Tactile Sensing Arrays for Physical Interaction Tasks

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