Online elasticity estimation and material sorting using standard robot grippers

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F24%3A00374998" target="_blank" >RIV/68407700:21220/24:00374998 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/24:00374998

Výsledek na webu

<a href="https://doi.org/10.1007/s00170-024-13678-6" target="_blank" >https://doi.org/10.1007/s00170-024-13678-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00170-024-13678-6" target="_blank" >10.1007/s00170-024-13678-6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Online elasticity estimation and material sorting using standard robot grippers

Popis výsledku v původním jazyce

Stiffness or elasticity estimation of everyday objects using robot grippers is highly desired for object recognition or classification in application areas like food handling and single-stream object sorting. However, standard robot grippers are not designed for material recognition. We experimentally evaluated the accuracy with which material properties can be estimated through object compression by two standard parallel jaw grippers and a force/torque sensor mounted at the robot wrist, with a professional biaxial compression device used as reference. Gripper effort versus position curves were obtained and transformed into stress/strain curves. The modulus of elasticity was estimated at different strain points and the effect of multiple compression cycles (precycling), compression speed, and the gripper surface area on estimation was studied. Viscoelasticity was estimated using the energy absorbed in a compression/decompression cycle, the Kelvin-Voigt, and Hunt-Crossley models. We found that (1) slower compression speeds improved elasticity estimation, while precycling or surface area did not; (2) the robot grippers, even after calibration, were found to have a limited capability of delivering accurate estimates of absolute values of Young’s modulus and viscoelasticity; (3) relative ordering of material characteristics was largely consistent across different grippers; (4) despite the nonlinear characteristics of deformable objects, fitting linear stress/strain approximations led to more stable results than local estimates of Young’s modulus; and (5) the Hunt-Crossley model worked best to estimate viscoelasticity, from a single object compression. A two-dimensional space formed by elasticity and viscoelasticity estimates obtained from a single grasp is advantageous for the discrimination of the object material properties. We demonstrated the applicability of our findings in a mock single-stream recycling scenario, where plastic, paper, and metal objects were correctly separated from a single grasp, even when compressed at different locations on the object. The data and code are publicly available.

Název v anglickém jazyce

Online elasticity estimation and material sorting using standard robot grippers

Popis výsledku anglicky

Stiffness or elasticity estimation of everyday objects using robot grippers is highly desired for object recognition or classification in application areas like food handling and single-stream object sorting. However, standard robot grippers are not designed for material recognition. We experimentally evaluated the accuracy with which material properties can be estimated through object compression by two standard parallel jaw grippers and a force/torque sensor mounted at the robot wrist, with a professional biaxial compression device used as reference. Gripper effort versus position curves were obtained and transformed into stress/strain curves. The modulus of elasticity was estimated at different strain points and the effect of multiple compression cycles (precycling), compression speed, and the gripper surface area on estimation was studied. Viscoelasticity was estimated using the energy absorbed in a compression/decompression cycle, the Kelvin-Voigt, and Hunt-Crossley models. We found that (1) slower compression speeds improved elasticity estimation, while precycling or surface area did not; (2) the robot grippers, even after calibration, were found to have a limited capability of delivering accurate estimates of absolute values of Young’s modulus and viscoelasticity; (3) relative ordering of material characteristics was largely consistent across different grippers; (4) despite the nonlinear characteristics of deformable objects, fitting linear stress/strain approximations led to more stable results than local estimates of Young’s modulus; and (5) the Hunt-Crossley model worked best to estimate viscoelasticity, from a single object compression. A two-dimensional space formed by elasticity and viscoelasticity estimates obtained from a single grasp is advantageous for the discrimination of the object material properties. We demonstrated the applicability of our findings in a mock single-stream recycling scenario, where plastic, paper, and metal objects were correctly separated from a single grasp, even when compressed at different locations on the object. The data and code are publicly available.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotika a pokročilá průmyslová výroba</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

The International Journal of Advanced Manufacturing Technology

ISSN

0268-3768

e-ISSN

1433-3015

Svazek periodika

132

Číslo periodika v rámci svazku

11-12

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

19

Strana od-do

6033-6051

Kód UT WoS článku

001216673400007

EID výsledku v databázi Scopus

2-s2.0-85190864783