Abstract
We present an embedded-based robot arm grasp detection system. This system has two subsystems: an embedded vision subsystem and an embedded robot arm control subsystem. In the former, a template matching algorithm is processed into an Nvidia Jetson TX2 developer kit to detect objects. And, then, control a robot arm to grasp and place. Although embedded systems have some preferred benefits, such as: cost, weight, size and power consumption; the slow processing speed is a significant drawback. To deal with this problem, we propose methods to reduce the number of calculations on the measurement of similarity. After testing on 40 templates with 200 test images, the result shows that the average of execution time is up to 10x faster than the original. The average execution time on middle size templates, (100 \sim 200) \times (100 \sim 200) pixels, obtains 0.176sec. In addition, the angle of objects is determined with a small angle interval: 1 degree.
| Original language | English |
|---|---|
| Title of host publication | 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019 |
| Publisher | IEEE Computer Society |
| Pages | 1296-1301 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781728103556 |
| DOIs | |
| Publication status | Published - 2019 Aug |
| Event | 15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada Duration: 2019 Aug 22 → 2019 Aug 26 |
Publication series
| Name | IEEE International Conference on Automation Science and Engineering |
|---|---|
| Volume | 2019-August |
| ISSN (Print) | 2161-8070 |
| ISSN (Electronic) | 2161-8089 |
Conference
| Conference | 15th IEEE International Conference on Automation Science and Engineering, CASE 2019 |
|---|---|
| Country | Canada |
| City | Vancouver |
| Period | 19-08-22 → 19-08-26 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
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Embedded-based object matching and robot arm control. / Le, Minh Tri; Li, Chih Hung G.; Guo, Shu Mei; Lien, Jenn Jier James.
2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019. IEEE Computer Society, 2019. p. 1296-1301 8843182 (IEEE International Conference on Automation Science and Engineering; Vol. 2019-August).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Embedded-based object matching and robot arm control
AU - Le, Minh Tri
AU - Li, Chih Hung G.
AU - Guo, Shu Mei
AU - Lien, Jenn Jier James
PY - 2019/8
Y1 - 2019/8
N2 - We present an embedded-based robot arm grasp detection system. This system has two subsystems: an embedded vision subsystem and an embedded robot arm control subsystem. In the former, a template matching algorithm is processed into an Nvidia Jetson TX2 developer kit to detect objects. And, then, control a robot arm to grasp and place. Although embedded systems have some preferred benefits, such as: cost, weight, size and power consumption; the slow processing speed is a significant drawback. To deal with this problem, we propose methods to reduce the number of calculations on the measurement of similarity. After testing on 40 templates with 200 test images, the result shows that the average of execution time is up to 10x faster than the original. The average execution time on middle size templates, (100 \sim 200) \times (100 \sim 200) pixels, obtains 0.176sec. In addition, the angle of objects is determined with a small angle interval: 1 degree.
AB - We present an embedded-based robot arm grasp detection system. This system has two subsystems: an embedded vision subsystem and an embedded robot arm control subsystem. In the former, a template matching algorithm is processed into an Nvidia Jetson TX2 developer kit to detect objects. And, then, control a robot arm to grasp and place. Although embedded systems have some preferred benefits, such as: cost, weight, size and power consumption; the slow processing speed is a significant drawback. To deal with this problem, we propose methods to reduce the number of calculations on the measurement of similarity. After testing on 40 templates with 200 test images, the result shows that the average of execution time is up to 10x faster than the original. The average execution time on middle size templates, (100 \sim 200) \times (100 \sim 200) pixels, obtains 0.176sec. In addition, the angle of objects is determined with a small angle interval: 1 degree.
UR - http://www.scopus.com/inward/record.url?scp=85072969619&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072969619&partnerID=8YFLogxK
U2 - 10.1109/COASE.2019.8843182
DO - 10.1109/COASE.2019.8843182
M3 - Conference contribution
AN - SCOPUS:85072969619
T3 - IEEE International Conference on Automation Science and Engineering
SP - 1296
EP - 1301
BT - 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PB - IEEE Computer Society
ER -