3D vision for object grasp and obstacle avoidance of a collaborative robot

Kai Tai Song*, Yu Hsien Chang, Jen Hao Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

This paper presents a design and experimental study of 3D robotic vision for bin picking and obstacle avoidance. Through the 3D vision algorithm, the robotic picking system is able to analyze the imagery of cluttered objects, classify the objects and estimate the pose of identified objects for grasping. In order to facilitate the robot to work with a human nearby, obstacle avoidance during task execution is developed based on 3D vision. In this design, a RealSense SR300 RGB-D camera is utilized to acquire RGB images and depth images of clustered workpieces. A deep neural network (DNN) approach to object recognition is designed and combined with point cloud segmentation to enhance 3D object-pose estimation for grasping The robot avoids obstacles to assure safe operation during execution of the bin picking task. Practical experiments using a Techman TM5 6-DOF robot arm show that the proposed method effectively accomplishes obstacle avoidance in pick-and-place operations.

Original languageEnglish
Title of host publicationProceedings of the 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages254-258
Number of pages5
ISBN (Electronic)9781728124933
DOIs
StatePublished - Jul 2019
Event2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019 - Hong Kong, China
Duration: 8 Jul 201912 Jul 2019

Publication series

NameIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
Volume2019-July

Conference

Conference2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019
CountryChina
CityHong Kong
Period8/07/1912/07/19

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