A Posture Measurement Approach for Robot Arms by RGB-D Cameras
| Autor: | Chia-Hung Hung, 洪嘉鴻 |
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| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 Robot arms bring a lot of conveniences to the industries in the recent years. However, there aren’t any functions to detect collision automatically in most robot arms at present. As the rates of robot arm applications are increasing, the density which humans work with robot arms is rising. Some important issues of safety should be concerned. Therefore, the objective of this thesis is using external observations to propose a method of geometric shape recognition which could find cylindrical parameters from point cloud data containing incompletely geometric shapes. This thesis uses Universal Robots 5 (UR5) for the research target to measure cylindrical parameters of three UR5 links and then compute three angles of joints which are corresponding to the UR5 links. The content of this thesis can be simply divided into two parts. The first part is image noise filtering, and the second part is image processing. At the beginning, images with point cloud data including the posture of the five UR5 links are captured respectively by two 3D depth sensors and Point Cloud Library (PCL). Then, images captured by the two 3D depth sensors are combined into an image. The first part is to separate the point cloud data from the combined images and use Gaussian noise filtering to obtain point cloud data of three links. The second part is to use the algorithm of vii random sample consensus (RANSAC) to segment point cloud data which consist with the ideally cylindrical model and get cylindrical parameters. Besides, the three angles of joints which are corresponding to the UR5 links are computed through the cylindrical parameters. Finally, the feasibility of the method is verified by comparing the three angles displayed on the UR5 system to achieve the purpose of posture measurement and benefits the research to detect collision automatically in the future. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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