An efficient approach to closed-loop shape control of deformable objects using finite element models

Koessler, Adrien; Filella, N. Roca; Bouzgarrou, Belhassen-Chedli; Lequièvre, Laurent; Ramón, Juan Antonio Corrales

doi:10.1109/icra48506.2021.9560919

Cited by 21 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The assumptions of our work are as follows. We assume that the linear object lies in a 2-D workskpace, which is still a complex and interesting challenge ( Koessler et al, 2021 ; Zhu et al, 2018 ). The object is represented as a continuous curve and its state by the position and orientation (tangent angle of the curve) at each point along its length.…”

Section: Problem Definitionmentioning

confidence: 99%

Adaptive Deformation Control for Elastic Linear Objects

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Problem Definitionmentioning

confidence: 99%

Adaptive Deformation Control for Elastic Linear Objects

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The high accuracy of these methods is at the cost of long simulation times caused by huge amounts of computations. To address that, authors of [38] proposed a method for shape control based on FEM, which does not require real-time simulation of the deformable object in the control algorithm. To achieve real-time performance, they simplified the model of the manipulated deformable object to a 1D case, i.e., a chain of nodes.…”

Section: Related Work a Physics-based Dlo Modelsmentioning

confidence: 99%

Robotic Manipulation of Elongated and Elastic Objects

Kicki

Bednarek

Walas

2019

2019 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)

View full text Add to dashboard Cite

The robotic manipulation of Deformable Linear Objects (DLOs) is a vital and challenging task that is important in many practical applications. Classical model-based approaches to this problem require an accurate model to capture how robot motions affect the deformation of the DLO. Nowadays, data-driven models offer the best tradeoff between quality and computation time. This paper analyzes several learning-based 3D models of the DLO and proposes a new one based on the Transformer architecture that achieves superior accuracy, even on the DLOs of different lengths, thanks to the proposed scaling method. Moreover, we introduce a data augmentation technique, which improves the prediction performance of almost all considered DLO data-driven models. Thanks to this technique, even a simple Multilayer Perceptron (MLP) achieves close to state-of-the-art performance while being significantly faster to evaluate. In the experiments, we compare the performance of the learning-based 3D models of the DLO on several challenging datasets quantitatively and demonstrate their applicability in the task of shaping a DLO.

show abstract

“…However, depending of its complexity, it may be difficult to run this model in realtime. The authors of [9] consider control points velocities as features and use a Finite Element Model that formulates locally the static equilibrium equations for these points to avoid real-time model simulations. In some cases the control input can be derived directly from model equations as it is done in [5] and [11] with a catenary/chain curve.…”

Section: Introductionmentioning

confidence: 99%

Shape visual servoing of a tether cable from parabolic features

Smolentsev,

Krupa,

Chaumette

2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

In this paper we propose a visual servoing approach that controls the deformation of a suspended tether cable subject to gravity from visual data provided by a RGB-D camera. The cable shape is modelled with a parabolic curve together with the orientation of the plane containing the tether. The visual features considered are the parabolic coefficients and the yaw angle of that plane. We derive the analytical expression of the interaction matrix that relates the variation of the visual features to the velocities of the cable extremities. Singularities are demonstrated to occur if and only if the cable is taut horizontally or vertically. An image processing algorithm is also developed to extract in real-time the current features fitting the parabola to the cable from the observed point cloud. Simulations and experimental results demonstrate the efficiency of our visual servoing approach to deform the tether cable toward a desired shape configuration.

show abstract

An efficient approach to closed-loop shape control of deformable objects using finite element models

Cited by 21 publications

References 19 publications

Adaptive Deformation Control for Elastic Linear Objects

Adaptive Deformation Control for Elastic Linear Objects

Robotic Manipulation of Elongated and Elastic Objects

Shape visual servoing of a tether cable from parabolic features

Contact Info

Product

Resources

About