Purpose

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/approach

First, 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.

Findings

In 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/value

In 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.

中文翻译：

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遮挡下基于点云投影的高斯混合模型云机器人抓取

目的

在多机器人合作中，云可以共享传感器数据，这可以帮助机器人更好地感知环境。对于云机器人，机器人抓取是必须掌握的一项重要功能。通常，抓取的信息源主要来自视觉传感器。但是，由于工作环境的不确定性，视觉传感器的信息获取可能会遇到被未知物体阻挡的情况。本文旨在通过共享云中多视力传感器的信息，提出一种解决方案，以解决视觉传感器信息受阻时机器人抓握的问题。

设计/方法/方法

首先，随机抽样共识算法和主成分分析（PCA）算法用于检测桌面范围。然后，通过PCA算法获得遮挡区域的最小边界矩形。通过平面分割获得候选摄像机视野范围。然后，将候选摄像机的视线范围与操纵器工作空间组合在一起，以获取摄像机的姿态，并驱动手臂为桌面遮挡区域拍照。最后，使用高斯混合模型（GMM）近似对象投影的形状，并且对于每个单个高斯模型，都会生成并评估抓取矩形以获得最合适的矩形。

发现

在本文中，对各种被阻止的云机器人进行了测试。实验结果表明，该算法能够捕获被遮挡的桌面图像，并成功捕获被遮挡区域中的物体。

创意/价值

在现有工作中，关于使用有源多传感器解决遮挡问题的研究很少。本文提出了一种解决遮挡问题的新方法。所提出的方法可以通过云共享应用于多云机器人工作环境，有助于机器人更好地感知环境。此外，本文提出了一种基于点云投影的GMM形状逼近的物体捕捉矩形的方法。实验表明，所提出的方法可以很好地工作。