Hermawan, Hendra and Berchmans, Hanny J. and Baskoro, Gembong (2016) Experimental Vision Guided ARM Robot Using Raspberry PI for General Working Application. Masters thesis, Swiss German University.
|
Text
Hendra Hermawan 220141204 TOC.pdf Download (1MB) | Preview |
|
Text
Hendra Hermawan 220141204 1.pdf Restricted to Registered users only Download (490kB) |
||
Text
Hendra Hermawan 220141204 2.pdf Restricted to Registered users only Download (3MB) |
||
Text
Hendra Hermawan 220141204 3.pdf Restricted to Registered users only Download (2MB) |
||
Text
Hendra Hermawan 220141204 4.pdf Restricted to Registered users only Download (1MB) |
||
Text
Hendra Hermawan 220141204 5.pdf Restricted to Registered users only Download (390kB) |
||
|
Text
Hendra Hermawan 220141204 Ref.pdf Download (310kB) | Preview |
Abstract
Development a visual-guided autonomous arm robot for general working application require some preliminary works/research to ensure the quality and reliability of robot. This research is experimental approach by developing a prototype on computer vision using Raspberry Pi and single web camera supported by Python-OpenCV programming and tested using Mitsubishi arm robot RV-M1in SGU laboratory. This research focus on experiments in object detection, object location estimation and object grasping tasks of robot. Experiment results showed that color-base detection is 22% faster than contour-based object detection for colorful tooling object without disturbance same color from environment. However, contour-base detection is more effective for target working object detection than color-base. Light illumination and disturbance from environment should be managed for successful object detection. Triangulation linearity method is simple and fastest method for tooling object position estimation when tooling object is a known sized object. Experiment result showed error only 3% for distance estimation using this method compared with actual. Tooling object must be placed horizontal when Grasped by robot RV-M1gripper in order to bring it in proper position toward target working object.
Item Type: | Thesis (Masters) |
---|---|
Uncontrolled Keywords: | Raspberry Pi; Object-detection; Python-OpenCV; Threshold; Keypoints; Color-based; contour-Feature base; Canny-edge; ORB,; SURF |
Subjects: | T Technology > TJ Mechanical engineering and machinery > TJ163.12 Mechatronics T Technology > TJ Mechanical engineering and machinery > TJ210 Robots (General) |
Divisions: | Faculty of Engineering and Information Technology > Department of Mechatronics Engineering |
Depositing User: | Atroridho Rizky |
Date Deposited: | 08 Jul 2020 12:15 |
Last Modified: | 08 Jul 2020 12:15 |
URI: | http://repository.sgu.ac.id/id/eprint/1087 |
Actions (login required)
View Item |