Evidence map›Paper›PMID 35572374›Full record

ArticleFrontiers in robotics and AI2022

Adaptive Deformation Control for Elastic Linear Objects.

Omid Aghajanzadeh, Miguel Aranda, Juan Antonio Corrales Ramon, Christophe Cariou, Roland Lenain, Youcef Mezouar

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Omid AghajanzadehCNRS, Clermont Auvergne INP, Institut Pascal, Université Clermont Auvergne, Clermont-Ferrand, France.
Miguel ArandaInstituto de Investigación en Ingeniería de Aragón, Universidad de Zaragoza, Zaragoza, Spain.
Juan Antonio Corrales RamonCentro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain.
Christophe CariouUniversité Clermont Auvergne, INRAE, URTSCF, Clermont-Ferrand, France.
Roland LenainUniversité Clermont Auvergne, INRAE, URTSCF, Clermont-Ferrand, France.
Youcef MezouarCNRS, Clermont Auvergne INP, Institut Pascal, Université Clermont Auvergne, Clermont-Ferrand, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper addresses the general problem of deformable linear object manipulation. The main application we consider is in the field of agriculture, for plant grasping, but may have interests in other tasks such as human daily activities and industrial production. We specifically consider an elastic linear object where one of its endpoints is fixed, and another point can be grasped by a robotic arm. To deal with the mentioned problem, we propose a model-free method to control the state of an arbitrary point that can be at any place along the object's length. Our approach allows the robot to manipulate the object without knowing any model parameters or offline information of the object's deformation. An adaptive control strategy is proposed for regulating the state of any point automatically deforming the object into the desired location. A control law is developed to regulate the object's shape thanks to the adaptive estimation of the system parameters and its states. This method can track a desired manipulation trajectory to reach the target point, which leads to a smooth deformation without drastic changes. A Lyapunov-based argument is presented for the asymptotic convergence of the system that shows the process's stability and convergence to desired state values. To validate the controller, numerical simulations involving two different deformation models are conducted, and performances of the proposed algorithm are investigated through full-scale experiments.

Indexed as

adaptive controldeformable linear objectLyapunov stabilityparameters estimationrobot manipulationtracking of deformation

Identifiers

PMID35572374
PMCPMC9096453

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.