Dynamic Workpiece Modeling with Robotic Pick-Place Based on Stereo Vision Scanning Using Fast Point-Feature Histogram Algorithm
In the era of rapid development in industry, an automatic production line is the fundamental and crucial mission for robotic pick-place. However, most production works for picking and placing workpieces are still manual operations in the stamping industry. Therefore, an intelligent system that is fu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/23/11522 |
| _version_ | 1797508015477227520 |
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| author | Quoc-Trung Do Wen-Yang Chang Li-Wei Chen |
| author_facet | Quoc-Trung Do Wen-Yang Chang Li-Wei Chen |
| author_sort | Quoc-Trung Do |
| collection | DOAJ |
| description | In the era of rapid development in industry, an automatic production line is the fundamental and crucial mission for robotic pick-place. However, most production works for picking and placing workpieces are still manual operations in the stamping industry. Therefore, an intelligent system that is fully automatic with robotic pick-place instead of human labor needs to be developed. This study proposes a dynamic workpiece modeling integrated with a robotic arm based on two stereo vision scans using the fast point-feature histogram algorithm for the stamping industry. The point cloud models of workpieces are acquired by leveraging two depth cameras, type Azure Kinect Microsoft, after stereo calibration. The 6D poses of workpieces, including three translations and three rotations, can be estimated by applying algorithms for point cloud processing. After modeling the workpiece, a conveyor controlled by a microcontroller will deliver the dynamic workpiece to the robot. In order to accomplish this dynamic task, a formula related to the velocity of the conveyor and the moving speed of the robot is implemented. The average error of 6D pose information between our system and the practical measurement is lower than 7%. The performance of the proposed method and algorithm has been appraised on real experiments of a specified stamping workpiece. |
| first_indexed | 2024-03-10T04:56:36Z |
| format | Article |
| id | doaj.art-ca4049d253824819ab61fcbbf666e157 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:56:36Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ca4049d253824819ab61fcbbf666e1572023-11-23T02:09:37ZengMDPI AGApplied Sciences2076-34172021-12-0111231152210.3390/app112311522Dynamic Workpiece Modeling with Robotic Pick-Place Based on Stereo Vision Scanning Using Fast Point-Feature Histogram AlgorithmQuoc-Trung Do0Wen-Yang Chang1Li-Wei Chen2Department of Mechanical and Computer-Aided Engineering, College of Engineering, National Formosa University, Yunlin 632301, TaiwanDepartment of Mechanical and Computer-Aided Engineering, College of Engineering, National Formosa University, Yunlin 632301, TaiwanDepartment of Mechanical and Computer-Aided Engineering, College of Engineering, National Formosa University, Yunlin 632301, TaiwanIn the era of rapid development in industry, an automatic production line is the fundamental and crucial mission for robotic pick-place. However, most production works for picking and placing workpieces are still manual operations in the stamping industry. Therefore, an intelligent system that is fully automatic with robotic pick-place instead of human labor needs to be developed. This study proposes a dynamic workpiece modeling integrated with a robotic arm based on two stereo vision scans using the fast point-feature histogram algorithm for the stamping industry. The point cloud models of workpieces are acquired by leveraging two depth cameras, type Azure Kinect Microsoft, after stereo calibration. The 6D poses of workpieces, including three translations and three rotations, can be estimated by applying algorithms for point cloud processing. After modeling the workpiece, a conveyor controlled by a microcontroller will deliver the dynamic workpiece to the robot. In order to accomplish this dynamic task, a formula related to the velocity of the conveyor and the moving speed of the robot is implemented. The average error of 6D pose information between our system and the practical measurement is lower than 7%. The performance of the proposed method and algorithm has been appraised on real experiments of a specified stamping workpiece.https://www.mdpi.com/2076-3417/11/23/11522dynamic modelingpick and placepoint cloud processingpose estimationstereo calibration |
| spellingShingle | Quoc-Trung Do Wen-Yang Chang Li-Wei Chen Dynamic Workpiece Modeling with Robotic Pick-Place Based on Stereo Vision Scanning Using Fast Point-Feature Histogram Algorithm Applied Sciences dynamic modeling pick and place point cloud processing pose estimation stereo calibration |
| title | Dynamic Workpiece Modeling with Robotic Pick-Place Based on Stereo Vision Scanning Using Fast Point-Feature Histogram Algorithm |
| title_full | Dynamic Workpiece Modeling with Robotic Pick-Place Based on Stereo Vision Scanning Using Fast Point-Feature Histogram Algorithm |
| title_fullStr | Dynamic Workpiece Modeling with Robotic Pick-Place Based on Stereo Vision Scanning Using Fast Point-Feature Histogram Algorithm |
| title_full_unstemmed | Dynamic Workpiece Modeling with Robotic Pick-Place Based on Stereo Vision Scanning Using Fast Point-Feature Histogram Algorithm |
| title_short | Dynamic Workpiece Modeling with Robotic Pick-Place Based on Stereo Vision Scanning Using Fast Point-Feature Histogram Algorithm |
| title_sort | dynamic workpiece modeling with robotic pick place based on stereo vision scanning using fast point feature histogram algorithm |
| topic | dynamic modeling pick and place point cloud processing pose estimation stereo calibration |
| url | https://www.mdpi.com/2076-3417/11/23/11522 |
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