標題: 3D Vision for Object Grasp and Obstacle Avoidance of a Collaborative Robot
作者: Song, Kai-Tai
Chang, Yu-Hsien
Chen, Jen-Hao
電控工程研究所
Institute of Electrical and Control Engineering
公開日期: 1-Jan-2019
摘要: 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.
URI: http://hdl.handle.net/11536/154282
ISBN: 978-1-7281-2493-3
ISSN: 2159-6255
期刊: 2019 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM)
起始頁: 254
結束頁: 258
Appears in Collections:Conferences Paper