Object Singulation by Nonlinear Pushing for Robotic Grasping
- Title
- Object Singulation by Nonlinear Pushing for Robotic Grasping
- Author
- 서일홍
- Keywords
- image segmentation; learning (artificial intelligence); manipulators; robot vision
- Issue Date
- 2019-11
- Publisher
- IEEE/RSJ
- Citation
- 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Page. 2402-2407
- Abstract
- In this study, we aim at grasping a single target object in a cluttered environment using a robotic arm. While dexterous grasp for various shapes of objects is not considered in this work, we focus on developing the method to mitigate clutter near the target object as soon as quickly. For this purpose, we propose a method to generate nonlinear pushing motions for object singulation based on an off-the-shelf machine learning algorithm and a typical semantic segmentation algorithm. Through experiments, we show that the success rate of robotic grasping is considerably improved by the proposed pushing behavior. And notably, the nonlinear pushing trajectories allows the robot to perform singulation of the target object in a cluttered environment with fewer trials than linear pushing usually pursued in related works.
- URI
- https://ieeexplore.ieee.org/document/8968077https://repository.hanyang.ac.kr/handle/20.500.11754/155442
- ISBN
- 978-1-7281-4004-9
- ISSN
- 2153-0866
- DOI
- 10.1109/IROS40897.2019.8968077
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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