CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
A Vision-Based Robotic Grasping Approach under the Disturbance of Obstacles
Xionglei, Zhao1,2; Zhiqiang, Cao1,2; Qun, Jia1,2; Lei, Pang1,2; Yingying, Yu1,2; Min, Tan1,2
2018-08
Conference NameIEEE International Conference on Mechatronics and Automation
Conference DateAugust 5-8, 2018
Conference PlaceChangchun, China
Abstract

This paper presents a vision-based robotic grasping approach with complex obstacles environments. A deep learning-based object detection algorithm is used to detect object in the image and obtain the object's category and position. Then the Euclidean cluster extraction algorithm is adopted to segment scenes composed of 3D point clouds to obtain the positions and size of obstacles. According to the acquired information of the object and obstacles, one can judge whether the object can be directly grasped. If there is no direct solution, the obstacles that interfere
with the grasping shall be firstly moved to other positions, then the object is grasped. These new positions of interference obstacles are selected based on artificial potential field. The experimental results on the Kinova MICO2 arm demonstrate that the approach can effectively achieve the grasping of target object even with severe interference from obstacles.
 

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23610
Collection复杂系统管理与控制国家重点实验室_先进机器人
复杂系统管理与控制国家重点实验室
Corresponding AuthorZhiqiang, Cao
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Xionglei, Zhao,Zhiqiang, Cao,Qun, Jia,et al. A Vision-Based Robotic Grasping Approach under the Disturbance of Obstacles[C],2018.
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