Grasp planning based on dynamics shaping

Yamawaki, Tasuku; Yashima, M.

doi:10.1109/aim.2011.6027063

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…The purpose here is to compute the pseudo-inverse matrix of the image Jacobian J † (G X , r T ob * ) for the object located at r T ob * and grasped by G X ∈ G A (G X ̸ = G rep ) (See Fig. 4) using the obtained data set shown in (7) and (9). For computing the pseudo-inverse matrix of the image Jacobian, we need to prepare the deviation of the images and the joint angles represented by ( 7) and ( 9), respectively.…”

Section: Conversion Of the Image Jacobianmentioning

confidence: 99%

“…This study focuses on "regrasping objects" in picking and placement tasks. The motivation why we focus on it is that the regrasping objects in the picking and placement tasks include important and essential technologies for robotics, such as 3D measurement [4], [5], random bin piking [6]- [8], grasping [9], [10], positioning [11], [12], and assembling industrial parts [13].…”

Section: Introductionmentioning

confidence: 99%

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Robust Regrasping against Error of Grasping for Bin-picking and Kitting

Arai¹,

Konada²,

Yoshinaga³

et al. 2021

Preprint

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<div>This study proposes a method of robust regrasping an object using a dual-arm robot with general-purpose hands, which is robust against the error of grasping. In this paper, one arm is assigned to hand over the object to the other arm that is named a receiver arm. The grasping error must be considered to increase the success rate of the regrasping since a hand-over arm first picks up the object with the general-purpose hand. In an online phase, the proposed method performs object positioning at an optimal pose at the time of regrasping using an image-based visual servoing (IBVS) approach to reduce the effect of the grasping error. In the planning phase, the proposed method computes the optimal pose for regrasping by maximizing the minimum singular values of the image Jacobian of IBVS to achieve a high positioning accuracy using a 3D model of the target object. To achieve the regrasping objects with various shapes robustly against image noises and changes in light environments, the image Jacobian of IBVS is computed by numerical differential using an actual data set. A large number of data sets corresponding to each candidate grasp are usually required for computing the image Jacobian. To reduce the number of data sets, we propose a conversion method of the image Jacobian requiring only one data set corresponding to one representative grasp. The experimental results show that the proposed method achieves regrasping of target objects with the general-purpose hands with high success rates and performs target object positioning with less than 0.7[mm] positioning error.</div>

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Section: Conversion Of the Image Jacobianmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Regrasping against Error of Grasping for Bin-picking and Kitting

Arai¹,

Konada²,

Yoshinaga³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Let x o ∈ 6 be the position and orientation of the target object; the equation of motion of the object can be described as follows [27]: where…”

Section: Contact Model Of Soft Fingersmentioning

confidence: 99%

Computation of minimum contact forces of multifingered robot hand with soft fingertips

Liu

Kim

Yee

et al. 2015

Intel Serv Robotics

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In the human hand, the softness of a fingertip plays a significant role on stable grasping and dexterous manipulation. This paper presents a method of computing minimum grasp forces of a multi-fingered hand with soft fingertips. In the first, we built up a simple linear contact model of a soft fingertip. Then, based on Pontryagin's principle, the problem of minimizing contact forces for realizing the stable grasping was formulated and the forces were computed. Finally, the experiments were carried out using a multifingered robot hand, called "Allegro Hand" with hard fingertips and soft ones, and the effectiveness of the proposed method was validated.

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“…V ISUAL servoing is a widely used method for positioning [1]- [7]. One important application of visual servoing is positioning task in factory automation since the positioning task is required after random bin-picking [8]- [11] to correct grasping error deviated from grasp planning [12] and perform assembling tasks [13]. This study, in particular, considers the problem of positioning an object grasped by a hand attached to a manipulator.…”

Section: Introductionmentioning

confidence: 99%

Optimal Projection Pattern for Active Visual Servoing (AVS)

Arai,

Miyamoto,

Kobayashi

et al. 2024

IEEE Access

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Visual servo control uses images that are obtained by a camera for robotic control. This study focuses on the problem of positioning a target object using a robotic manipulator with image-based visual servo (IBVS) control. To perform the positioning task, the image-based visual servoing requires visual features that can be extracted from the appearance of the target object. Therefore, a positioning error tends to increase especially for textureless objects, such as industrial parts. Since it is difficult to extract differences in the visual features between current and goal images. To solve these problems, this paper presents a novel visual servoing named "Active Visual Servoing." Active Visual Servoing (AVS) projects patterned light onto the target object using a projector. The design of the projection pattern affects the positioning error. AVS uses an optimal pattern that is theoretically derived and maximizes differences between current and goal images. The experimental results show that the proposed active visual servoing method reduces the positioning error by more than 97% compared to conventional image-based visual servoing.

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Grasp planning based on dynamics shaping

Cited by 4 publications

References 15 publications

Robust Regrasping against Error of Grasping for Bin-picking and Kitting

Robust Regrasping against Error of Grasping for Bin-picking and Kitting

Computation of minimum contact forces of multifingered robot hand with soft fingertips

Optimal Projection Pattern for Active Visual Servoing (AVS)

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