Monocular-Based 6-Degree of Freedom Pose Estimation Technology for Robotic Intelligent Grasping Systems
Industrial robots are expected to undertake ever more advanced tasks in the modern manufacturing industry, such as intelligent grasping, in which robots should be capable of recognizing the position and orientation of a part before grasping it. In this paper, a monocular-based 6-degree of freedom (D...
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| Format: | Article |
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MDPI AG
2017-02-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/17/2/334 |
| _version_ | 1798025315799269376 |
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| author | Tao Liu Yin Guo Shourui Yang Shibin Yin Jigui Zhu |
| author_facet | Tao Liu Yin Guo Shourui Yang Shibin Yin Jigui Zhu |
| author_sort | Tao Liu |
| collection | DOAJ |
| description | Industrial robots are expected to undertake ever more advanced tasks in the modern manufacturing industry, such as intelligent grasping, in which robots should be capable of recognizing the position and orientation of a part before grasping it. In this paper, a monocular-based 6-degree of freedom (DOF) pose estimation technology to enable robots to grasp large-size parts at informal poses is proposed. A camera was mounted on the robot end-flange and oriented to measure several featured points on the part before the robot moved to grasp it. In order to estimate the part pose, a nonlinear optimization model based on the camera object space collinearity error in different poses is established, and the initial iteration value is estimated with the differential transformation. Measuring poses of the camera are optimized based on uncertainty analysis. Also, the principle of the robotic intelligent grasping system was developed, with which the robot could adjust its pose to grasp the part. In experimental tests, the part poses estimated with the method described in this paper were compared with those produced by a laser tracker, and results show the RMS angle and position error are about 0.0228° and 0.4603 mm. Robotic intelligent grasping tests were also successfully performed in the experiments. |
| first_indexed | 2024-04-11T18:16:38Z |
| format | Article |
| id | doaj.art-b2e07b3f087f454080708c9abcfc3d98 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:16:38Z |
| publishDate | 2017-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b2e07b3f087f454080708c9abcfc3d982022-12-22T04:09:52ZengMDPI AGSensors1424-82202017-02-0117233410.3390/s17020334s17020334Monocular-Based 6-Degree of Freedom Pose Estimation Technology for Robotic Intelligent Grasping SystemsTao Liu0Yin Guo1Shourui Yang2Shibin Yin3Jigui Zhu4State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, ChinaIndustrial robots are expected to undertake ever more advanced tasks in the modern manufacturing industry, such as intelligent grasping, in which robots should be capable of recognizing the position and orientation of a part before grasping it. In this paper, a monocular-based 6-degree of freedom (DOF) pose estimation technology to enable robots to grasp large-size parts at informal poses is proposed. A camera was mounted on the robot end-flange and oriented to measure several featured points on the part before the robot moved to grasp it. In order to estimate the part pose, a nonlinear optimization model based on the camera object space collinearity error in different poses is established, and the initial iteration value is estimated with the differential transformation. Measuring poses of the camera are optimized based on uncertainty analysis. Also, the principle of the robotic intelligent grasping system was developed, with which the robot could adjust its pose to grasp the part. In experimental tests, the part poses estimated with the method described in this paper were compared with those produced by a laser tracker, and results show the RMS angle and position error are about 0.0228° and 0.4603 mm. Robotic intelligent grasping tests were also successfully performed in the experiments.http://www.mdpi.com/1424-8220/17/2/334monocularindustrial robotintelligent graspingpose estimation |
| spellingShingle | Tao Liu Yin Guo Shourui Yang Shibin Yin Jigui Zhu Monocular-Based 6-Degree of Freedom Pose Estimation Technology for Robotic Intelligent Grasping Systems Sensors monocular industrial robot intelligent grasping pose estimation |
| title | Monocular-Based 6-Degree of Freedom Pose Estimation Technology for Robotic Intelligent Grasping Systems |
| title_full | Monocular-Based 6-Degree of Freedom Pose Estimation Technology for Robotic Intelligent Grasping Systems |
| title_fullStr | Monocular-Based 6-Degree of Freedom Pose Estimation Technology for Robotic Intelligent Grasping Systems |
| title_full_unstemmed | Monocular-Based 6-Degree of Freedom Pose Estimation Technology for Robotic Intelligent Grasping Systems |
| title_short | Monocular-Based 6-Degree of Freedom Pose Estimation Technology for Robotic Intelligent Grasping Systems |
| title_sort | monocular based 6 degree of freedom pose estimation technology for robotic intelligent grasping systems |
| topic | monocular industrial robot intelligent grasping pose estimation |
| url | http://www.mdpi.com/1424-8220/17/2/334 |
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