Abstract
Along with the development of deep learning, efforts are being made to grasping with the robot using only the camera. Above all, a lot of research is being done for grasping in an environment where various objects are mixed. To perform grasping in complex environments, it is necessary to train the grasping algorithm with vast amounts of data to ensure its robustness. However, collecting grasping data takes a lot of time and effort. In this paper, we proposed the depth tile that simply describes a complex situation by processing a depth image. Through this, the grasping algorithm can use a light artificial neural network, and training data can be generated automatically without grasping in real-world or simulation to minimize learning data collection costs. Artificial neural network trained through the depth tile can perform grasping with high success rate by estimating the grasping angle, which is less likely to interfere with obstacles. In this paper, the proposed grasping method, through experiments to empty randomly placed objects, is proved to be robust in complex environments.
| Original language | English |
|---|---|
| Title of host publication | 2019 16th International Conference on Ubiquitous Robots, UR 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 113-117 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781728132327 |
| DOIs | |
| Publication status | Published - 2019 Jun 1 |
| Event | 16th International Conference on Ubiquitous Robots, UR 2019 - Jeju, Korea, Republic of Duration: 2019 Jun 24 → 2019 Jun 27 |
Publication series
| Name | 2019 16th International Conference on Ubiquitous Robots, UR 2019 |
|---|
Conference
| Conference | 16th International Conference on Ubiquitous Robots, UR 2019 |
|---|---|
| Country | Korea, Republic of |
| City | Jeju |
| Period | 19/6/24 → 19/6/27 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Human-Computer Interaction
- Mechanical Engineering
- Control and Optimization
Cite this
Grasping Method in a Complex Environment using Convolutional Neural Network Based on Modified Average Filter. / Kim, Da Wit; Jo, Hyun Jun; Song, Jae-Bok.
2019 16th International Conference on Ubiquitous Robots, UR 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 113-117 8768779 (2019 16th International Conference on Ubiquitous Robots, UR 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Grasping Method in a Complex Environment using Convolutional Neural Network Based on Modified Average Filter
AU - Kim, Da Wit
AU - Jo, Hyun Jun
AU - Song, Jae-Bok
PY - 2019/6/1
Y1 - 2019/6/1
N2 - Along with the development of deep learning, efforts are being made to grasping with the robot using only the camera. Above all, a lot of research is being done for grasping in an environment where various objects are mixed. To perform grasping in complex environments, it is necessary to train the grasping algorithm with vast amounts of data to ensure its robustness. However, collecting grasping data takes a lot of time and effort. In this paper, we proposed the depth tile that simply describes a complex situation by processing a depth image. Through this, the grasping algorithm can use a light artificial neural network, and training data can be generated automatically without grasping in real-world or simulation to minimize learning data collection costs. Artificial neural network trained through the depth tile can perform grasping with high success rate by estimating the grasping angle, which is less likely to interfere with obstacles. In this paper, the proposed grasping method, through experiments to empty randomly placed objects, is proved to be robust in complex environments.
AB - Along with the development of deep learning, efforts are being made to grasping with the robot using only the camera. Above all, a lot of research is being done for grasping in an environment where various objects are mixed. To perform grasping in complex environments, it is necessary to train the grasping algorithm with vast amounts of data to ensure its robustness. However, collecting grasping data takes a lot of time and effort. In this paper, we proposed the depth tile that simply describes a complex situation by processing a depth image. Through this, the grasping algorithm can use a light artificial neural network, and training data can be generated automatically without grasping in real-world or simulation to minimize learning data collection costs. Artificial neural network trained through the depth tile can perform grasping with high success rate by estimating the grasping angle, which is less likely to interfere with obstacles. In this paper, the proposed grasping method, through experiments to empty randomly placed objects, is proved to be robust in complex environments.
UR - http://www.scopus.com/inward/record.url?scp=85070551071&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070551071&partnerID=8YFLogxK
U2 - 10.1109/URAI.2019.8768779
DO - 10.1109/URAI.2019.8768779
M3 - Conference contribution
AN - SCOPUS:85070551071
T3 - 2019 16th International Conference on Ubiquitous Robots, UR 2019
SP - 113
EP - 117
BT - 2019 16th International Conference on Ubiquitous Robots, UR 2019
PB - Institute of Electrical and Electronics Engineers Inc.
ER -