Purpose

The purpose of this paper is to propose and evaluate the method for grasping a defined set of objects in an unstructured environment. To this end, the authors propose the method of integrating convolutional neural network (CNN)-based object detection and the category-free grasping method. The considered scenario is related to mobile manipulating platforms that move freely between workstations and manipulate defined objects. In this application, the robot is not positioned with respect to the table and manipulated objects. The robot detects objects in the environment and uses grasping methods to determine the reference pose of the gripper.

Design/methodology/approach

The authors implemented the whole pipeline which includes object detection, grasp planning and motion execution on the real robot. The selected grasping method uses raw depth images to find the configuration of the gripper. The authors compared the proposed approach with a representative grasping method that uses a 3D point cloud as an input to determine the grasp for the robotic arm equipped with a two-fingered gripper. To measure and compare the efficiency of these methods, the authors measured the success rate in various scenarios. Additionally, they evaluated the accuracy of object detection and pose estimation modules.

Findings

The performed experiments revealed that the CNN-based object detection and the category-free grasping methods can be integrated to obtain the system which allows grasping defined objects in the unstructured environment. The authors also identified the specific limitations of neural-based and point cloud-based methods. They show how the determined properties influence the performance of the whole system.

Research limitations/implications

The authors identified the limitations of the proposed methods and the improvements are envisioned as part of future research.

Practical implications

The evaluation of the grasping and object detection methods on the mobile manipulating robot may be useful for all researchers working on the autonomy of similar platforms in various applications.

Social implications

The proposed method increases the autonomy of robots in applications in the small industry which is related to repetitive tasks in a noisy and potentially risky environment. This allows reducing the human workload in these types of environments.

Originality/value

The main contribution of this research is the integration of the state-of-the-art methods for grasping objects with object detection methods and evaluation of the whole system on the industrial robot. Moreover, the properties of each subsystem are identified and measured.

中文翻译：

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移动操纵机器人检测和抓取物体的实际应用

目的

本文的目的是提出并评估在非结构化环境中抓取一组已定义对象的方法。为此，作者提出了将基于卷积神经网络（CNN）的物体检测与无类别抓取方法相结合的方法。所考虑的场景与在工作站之间自由移动并操纵定义对象的移动操纵平台有关。在此应用中，机器人未相对于工作台和操作对象进行定位。机器人检测环境中的物体并使用抓取方法来确定抓手的参考位姿。

设计/方法/方法

作者在真实机器人上实现了包括目标检测、抓取规划和运动执行在内的整个流程。选定的抓取方法使用原始深度图像来查找抓手的配置。作者将所提出的方法与代表性的抓取方法进行了比较，该方法使用 3D 点云作为输入来确定配备两指抓手的机械臂的抓取情况。为了衡量和比较这些方法的效率，作者测量了各种场景下的成功率。此外，他们还评估了对象检测和姿势估计模块的准确性。

发现

所进行的实验表明，可以将基于 CNN 的物体检测和无类别抓取方法相结合，以获得允许在非结构化环境中抓取定义物体的系统。作者还确定了基于神经和基于点云的方法的具体局限性。它们显示了确定的属性如何影响整个系统的性能。

研究限制/影响

作者确定了所提出方法的局限性，并将改进设想为未来研究的一部分。

实际影响

对移动操纵机器人的抓取和物体检测方法的评估可能对所有研究人员在各种应用中研究类似平台的自主性都有用。

社会影响

所提出的方法增加了机器人在小型工业应用中的自主性，这与嘈杂和潜在风险环境中的重复任务有关。这允许减少这些类型环境中的人类工作量。

原创性/价值

这项研究的主要贡献是将最先进的物体抓取方法与物体检测方法相结合，并对工业机器人的整个系统进行评估。此外，每个子系统的属性被识别和测量。