Active shaping of an unknown rheological object based on deformation decomposition into elasticity and plasticity

Higashimori, Mitsuru; Yoshimoto, Kayo; Kaneko, Makoto

doi:10.1109/robot.2010.5509462

Cited by 45 publications

(34 citation statements)

References 12 publications

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“…Wang and Hirai have discussed the modeling of a rheological object by finit element (FE) method [9]. Our former work has discussed an active shaping method focusing on one-dimensional deformation [12]. Das and Sarkar have developed an optimization-based motion planning scheme to achieve shape control of deformable objects by multiple manipulators [10].…”

Section: Related Workmentioning

confidence: 99%

“…In order to express the outline shape of the object, we allocate one node at the object's center and six nodes inscribed in the object's surface on the x, y, and z-axes. In general, in order to express the dynamic characteristic of the rheological object with a viscoelastic model, four elements (elasticity×2, viscosity×2) are used [9], [12]. In this work, we utilize the four-element Burger model, as shown in Fig.…”

Section: Analytical Modelmentioning

confidence: 99%

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Active outline shaping of a rheological object based on plastic deformation distribution

Yoshimoto

Higashimori

Tadakuma

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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This paper discusses a shaping strategy of a rheological object whose deformation characteristic includes both elasticity and plasticity. We first introduce a sevennodes viscoelastic model for approximating the outline and the dynamic characteristics of the object. Then, we show a shaping method of the object's outline, where the ratio between the object's length in the grasping direction and that in the perpendicular one is controlled by using a parallel jaw gripper. Based on the plastic deformation distribution for the integrated input stress, the proposed method can actively manage the final object's outline. In addition to the contribution on simplifying the gripper's degree of freedom, this method has the advantage that the handling time is drastically reduced, compared with the position based passive method. We finally show the experimental results for confirming the validity of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Section: Analytical Modelmentioning

confidence: 99%

Active outline shaping of a rheological object based on plastic deformation distribution

Yoshimoto

Higashimori

Tadakuma

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…9 According to these deficiencies, the researchers began to analyze the grasping operation based on the viscoelastic characteristics of fruit. Higashimori et al 10 analyzed the relationship between the imposed grasping force and plastic deformation based on the four-unit viscoelastic model parameters of the rheological objects as well as decreased the grasping operation time as avoiding the excessive deformation of objects. Saeidirad et al 11 were used to make stress relaxation test for different varieties of pomegranate using texture analyzer, and artificial neural network (ANN) was used to replace the Maxwell model to predict the viscoelastic properties of pomegranate.…”

Section: Introductionmentioning

confidence: 99%

An optimum strategy for robotic tomato grasping based on real-time viscoelastic parameters estimation

Tao

Zhou

Wang

et al. 2017

International Journal of Advanced Robotic Systems

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It is a challenging task to achieve rapid and stable grasping of fruit and vegetable without damages for the agricultural robot. From the point of view of which most of fruits and vegetables are viscoelastic material, the viscoelastic characteristic of tomato was analyzed based on Burgers model in this article to provide a reference for the robotic grasping. First, the realtime viscoelastic parameters estimation model based on back-propagation neural network was established. The 3-11-4 network structure was applied, where the grasping force, displacement, and time were input to the model to estimate four viscoelastic parameters. The relative error was less than 15% at the 0.2-s estimation and correlation coefficient of fitting could reach to 0.99. Then, the expression of plastic deformation was derived by analyzing the dynamic characteristic of tomato based on Burgers model and Gripper's model during grasping. The minimum plastic deformation was taken as the condition to optimize the grasping speed and operation time. Finally, the result of simulation and experiment showed the feasibility of the method proposed in this article. This research can achieve the goal of reducing the grasping time of robots without damaging the fruit and provide a reference for robots grasping process optimization.

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“…Some researchers have studied the active shaping of rheological materials, see e.g. [6], [7]. To control deformations, these methods must first identify the object's visco-elastic properties.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, most existing methods only use the manipulator's linear displacements to actively control deformations; since these control algorithms do not consider rotations/torsions, they can only impose a limited set of D. Navarro-Alarcon and Y.-H. Liu shapes onto the object. For example, [7] presents a method to control one-dimensional deformations (i.e. linear compressions), whereas [8]- [10] formulate the deformation controller considering plane motion of the manipulator (these methods can only control up-to two degree-of-freedom (DOF) with a single manipulator).…”

Section: Introductionmentioning

confidence: 99%

A dynamic and uncalibrated method to visually servo-control elastic deformations by fully-constrained robotic grippers

Navarro-Alarcon

Liu

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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In this paper, we address the set-point deformation control of elastic objects by fully-constrained grippers. We propose an uncalibrated Lyapunov-based algorithm that iteratively estimates the deformation Jacobian matrix, with no prior knowledge of the deformation and camera models. With this new method we show how, by combining pose information of the grippers with several visual measurements, we can independently control elastic deformations of unknown objects. We report experiments with a 6-DOF robot manipulator to validate this control approach.

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Active shaping of an unknown rheological object based on deformation decomposition into elasticity and plasticity

Cited by 45 publications

References 12 publications

Active outline shaping of a rheological object based on plastic deformation distribution

Active outline shaping of a rheological object based on plastic deformation distribution

An optimum strategy for robotic tomato grasping based on real-time viscoelastic parameters estimation

A dynamic and uncalibrated method to visually servo-control elastic deformations by fully-constrained robotic grippers

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