A method for robotic grasping based on improved Gaussian mixture model
| Autor: | Yong Tao, Tian Miao Wang, You Dong Chen, Zou Yu, Shan Jiang, Fan Ren, Chao Yong Chen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
v-rep simulation
Computer science Gaussian Bayesian probability 02 engineering and technology Computer Science::Robotics symbols.namesake 0502 economics and business QA1-939 0202 electrical engineering electronic engineering information engineering improved gaussian models Computer vision robotic grasping business.industry Applied Mathematics 05 social sciences GRASP Observable General Medicine Object (computer science) Mixture model Computational Mathematics Modeling and Simulation symbols Robot 020201 artificial intelligence & image processing Artificial intelligence General Agricultural and Biological Sciences business Gaussian network model TP248.13-248.65 Mathematics 050203 business & management Biotechnology |
| Zdroj: | Mathematical Biosciences and Engineering, Vol 17, Iss 2, Pp 1495-1510 (2020) |
| ISSN: | 1551-0018 |
| Popis: | The present research envisages a method for the robotic grasping based on the improved Gaussian mixture model. The improved Gaussian mixture model is a method proposed by incorporating Bayesian ideas into the Gaussian model. It will use the Gaussian model to perform grasping training in a certain area which we called trained area. The improved Gaussian models utilized the trained Gaussian models as prior models. The proposed method improved the cumulative updates and the evaluation results of the improved models to make robots more adaptable to grasp in the untrained areas. The self-taught learning ability of the robot about grasping was semi-supervised. Firstly, the observable variables of objects were determined by a camera. Then, we dragged the robot to grasp object. The relationship between the variables and robot's joint angles were mapped. We obtained new samples in the close untrained area to improve the Gaussian model. With these new observable variables, the robot grasped it successfully. Finally, the effectiveness of the method was verified by experiments and comparative tests on grasping of real objects and grasping simulation of the improved Gaussian models through the virtual robot experimentation platform. |
| Databáze: | OpenAIRE |
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