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Cloud robotic grasping of Gaussian mixture model based on point cloud projection under occlusion

Cloud robotic grasping of Gaussian mixture model based on point cloud projection under occlusion In multi-robot cooperation, the cloud can share sensor data, which can help robots better perceive the environment. For cloud robotics, robot grasping is an important ability that must be mastered. Usually, the information source of grasping mainly comes from visual sensors. However, due to the uncertainty of the working environment, the information acquisition of the vision sensor may encounter the situation of being blocked by unknown objects. This paper aims to propose a solution to the problem in robot grasping when the vision sensor information is blocked by sharing the information of multi-vision sensors in the cloud.Design/methodology/approachFirst, the random sampling consensus algorithm and principal component analysis (PCA) algorithms are used to detect the desktop range. Then, the minimum bounding rectangle of the occlusion area is obtained by the PCA algorithm. The candidate camera view range is obtained by plane segmentation. Then the candidate camera view range is combined with the manipulator workspace to obtain the camera posture and drive the arm to take pictures of the desktop occlusion area. Finally, the Gaussian mixture model (GMM) is used to approximate the shape of the object projection and for every single Gaussian model, the grabbing rectangle is generated and evaluated to get the most suitable one.FindingsIn this paper, a variety of cloud robotic being blocked are tested. Experimental results show that the proposed algorithm can capture the image of the occluded desktop and grab the objects in the occluded area successfully.Originality/valueIn the existing work, there are few research studies on using active multi-sensor to solve the occlusion problem. This paper presents a new solution to the occlusion problem. The proposed method can be applied to the multi-cloud robotics working environment through cloud sharing, which helps the robot to perceive the environment better. In addition, this paper proposes a method to obtain the object-grabbing rectangle based on GMM shape approximation of point cloud projection. Experiments show that the proposed methods can work well. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Assembly Automation Emerald Publishing

Cloud robotic grasping of Gaussian mixture model based on point cloud projection under occlusion

Lin, Shifeng; Wang, Ning
Assembly Automation , Volume 41 (3): 12 – Jul 22, 2021
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References (59)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0144-5154
eISSN
0144-5154
DOI
10.1108/aa-11-2020-0170
Publisher site
See Article on Publisher Site

Abstract

In multi-robot cooperation, the cloud can share sensor data, which can help robots better perceive the environment. For cloud robotics, robot grasping is an important ability that must be mastered. Usually, the information source of grasping mainly comes from visual sensors. However, due to the uncertainty of the working environment, the information acquisition of the vision sensor may encounter the situation of being blocked by unknown objects. This paper aims to propose a solution to the problem in robot grasping when the vision sensor information is blocked by sharing the information of multi-vision sensors in the cloud.Design/methodology/approachFirst, the random sampling consensus algorithm and principal component analysis (PCA) algorithms are used to detect the desktop range. Then, the minimum bounding rectangle of the occlusion area is obtained by the PCA algorithm. The candidate camera view range is obtained by plane segmentation. Then the candidate camera view range is combined with the manipulator workspace to obtain the camera posture and drive the arm to take pictures of the desktop occlusion area. Finally, the Gaussian mixture model (GMM) is used to approximate the shape of the object projection and for every single Gaussian model, the grabbing rectangle is generated and evaluated to get the most suitable one.FindingsIn this paper, a variety of cloud robotic being blocked are tested. Experimental results show that the proposed algorithm can capture the image of the occluded desktop and grab the objects in the occluded area successfully.Originality/valueIn the existing work, there are few research studies on using active multi-sensor to solve the occlusion problem. This paper presents a new solution to the occlusion problem. The proposed method can be applied to the multi-cloud robotics working environment through cloud sharing, which helps the robot to perceive the environment better. In addition, this paper proposes a method to obtain the object-grabbing rectangle based on GMM shape approximation of point cloud projection. Experiments show that the proposed methods can work well.

Journal

Assembly Automation – Emerald Publishing

Published: Jul 22, 2021

Keywords: Gaussian mixture model; Multi-camera; Occlusion; Robot grasp planning

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