Robotic Grasping of Unknown Objects

This paper focuses on the perception step of robotic grasping unknown objects in order to get a stable grasping hypothesis. At first, hierarchical shape context feature is proposed to depict the local and global shape character of a sample point along the edges of the object. Moreover a kind of random forests classifier is adopted to recognize the grasping candidates in the image from vision system so that a 2D grasping rectangle can be generated through kernel density estimation. Finally, by means of stereo matching, the grasping rectangle can be mapped into the 3D space. Thus, the center of the grasping rectangle can be applied as the center of the gripper. The approaching vector and the grasping rectangle direction can be employed to determine the pose of the gripper. Simulated experiments showed that a reasonable and stable grasping rectangle can be generated for various unknown objects.

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Robotic Grasping of Unknown Objects Using Novel Multilevel Convolutional Neural Networks: From Parallel Gripper to Dexterous Hand

IEEE Transactions on Automation Science and Engineering ◽

10.1109/tase.2020.3017022 ◽

2020 ◽

pp. 1-12

Author(s):

Qunchao Yu ◽

Weiwei Shang ◽

Zengzhi Zhao ◽

Shuang Cong ◽

Zhijun Li

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Robotic Grasping ◽

Dexterous Hand ◽

Unknown Objects

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Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review

Sensors ◽

10.3390/s20133707 ◽

2020 ◽

Vol 20 (13) ◽

pp. 3707

Author(s):

Chao Wang ◽

Xuehe Zhang ◽

Xizhe Zang ◽

Yubin Liu ◽

Guanwen Ding ◽

...

Keyword(s):

Recent Work ◽

Traditional Approach ◽

Uncertain Information ◽

Learning Networks ◽

Robotic Grasping ◽

Geometric Information ◽

Intelligent Robots ◽

The Future ◽

Physical Information ◽

Unknown Objects

As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We review the recent work on the feature sensing and robotic grasping of objects with uncertain information. In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach when the traditional approach fails. The uncertain information is classified into geometric information and physical information. Based on the type of uncertain information, the object is further classified into three categories, which are geometric-uncertain objects, physical-uncertain objects, and unknown objects. Furthermore, the approaches to the feature sensing and robotic grasping of these objects are presented based on the varied characteristics of each type of object. Finally, we summarize the reviewed approaches for uncertain objects and provide some interesting issues to be more investigated in the future. It is found that the object’s features, such as material and compactness, are difficult to be sensed, and the object grasping approach based on learning networks plays a more important role when the unknown degree of the task object increases.

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