Assembly Challenge: a robot competition of the Industrial Robotics Category, World Robot Summit – summary of the pre-competition in 2018

Yokokohji, Yasuyoshi; Kawai, Yoshihiro; Shibata, Masaaki; Aiyama, Yasumichi; Kotosaka, Shinya; Uemura, Wataru; Noda, Akio; Dobashi, Hiroki; Sakaguchi, Toshifumi; Yokoi, Kazuhito

doi:10.1080/01691864.2019.1663609

Cited by 61 publications

(32 citation statements)

References 8 publications

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“…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%

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: 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

“…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)

ARAI¹,

Miyamoto²,

Kobayashi³

et al. 2021

Preprint

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<p>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 of 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 which 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.</p>

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“…Industrial robots have been used in various fields and applications, such as cooperative tasks [1,2], visual servoing [3][4][5], robotic bin-picking [6,7], and assembly line [8,9]. Among these applications and tasks, grasp detection is essentially necessary and important.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-based Fast Grasp Planning for Robotic Bin-picking by Small Data Set without GPU

Arai¹,

Zhao²,

Purnomo³

et al. 2021

Preprint

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<div>This paper proposes a deep learning-based fast grasp detection method with a small dataset for robotic bin-picking. We consider the problem of grasping stacked up mechanical parts on a planar workspace using a parallel gripper. In this paper, we use a deep neural network to solve the problem with a single depth image. To reduce the computation time, we propose an edge-based algorithm to generate potential grasps. Then, a convolutional neural network (CNN) is applied to evaluate the robustness of all potential grasps for bin-picking. Finally, the proposed method ranks them and the object is grasped by using the grasp with the highest score. In bin-picking experiments, we evaluate the proposed method with a 7-DOF manipulator using textureless mechanical parts with complex shapes. The success ratio of grasping is 97%, and the average computation time of CNN inference is less than 0.23[s] on a laptop PC without a GPU array. In addition, we also confirm that the proposed method can be applied to unseen objects which are not included in the training dataset. </div>

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Assembly Challenge: a robot competition of the Industrial Robotics Category, World Robot Summit – summary of the pre-competition in 2018

Cited by 61 publications

References 8 publications

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

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

Optimal Projection Pattern for Active Visual Servoing (AVS)

Deep Learning-based Fast Grasp Planning for Robotic Bin-picking by Small Data Set without GPU

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