An optimum strategy for robotic tomato grasping based on real-time viscoelastic parameters estimation
It is a challenging task to achieve rapid and stable grasping of fruit and vegetable without damages for the agricultural robot. From the point of view of which most of fruits and vegetables are viscoelastic material, the viscoelastic characteristic of tomato was analyzed based on Burgers model in t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2017-08-01
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| Series: | International Journal of Advanced Robotic Systems |
| Online Access: | https://doi.org/10.1177/1729881417724190 |
| _version_ | 1829521158787039232 |
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| author | Yongting Tao Jun Zhou Mingjun Wang Na Zhang Yimeng Meng |
| author_facet | Yongting Tao Jun Zhou Mingjun Wang Na Zhang Yimeng Meng |
| author_sort | Yongting Tao |
| collection | DOAJ |
| description | It is a challenging task to achieve rapid and stable grasping of fruit and vegetable without damages for the agricultural robot. From the point of view of which most of fruits and vegetables are viscoelastic material, the viscoelastic characteristic of tomato was analyzed based on Burgers model in this article to provide a reference for the robotic grasping. First, the real-time viscoelastic parameters estimation model based on back-propagation neural network was established. The 3-11-4 network structure was applied, where the grasping force, displacement, and time were input to the model to estimate four viscoelastic parameters. The relative error was less than 15% at the 0.2-s estimation and correlation coefficient of fitting could reach to 0.99. Then, the expression of plastic deformation was derived by analyzing the dynamic characteristic of tomato based on Burgers model and Gripper’s model during grasping. The minimum plastic deformation was taken as the condition to optimize the grasping speed and operation time. Finally, the result of simulation and experiment showed the feasibility of the method proposed in this article. This research can achieve the goal of reducing the grasping time of robots without damaging the fruit and provide a reference for robots grasping process optimization. |
| first_indexed | 2024-12-16T15:10:38Z |
| format | Article |
| id | doaj.art-60be06c8d6fb4d91a07872156fe9f7b2 |
| institution | Directory Open Access Journal |
| issn | 1729-8814 |
| language | English |
| last_indexed | 2024-12-16T15:10:38Z |
| publishDate | 2017-08-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | International Journal of Advanced Robotic Systems |
| spelling | doaj.art-60be06c8d6fb4d91a07872156fe9f7b22022-12-21T22:26:58ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142017-08-011410.1177/1729881417724190An optimum strategy for robotic tomato grasping based on real-time viscoelastic parameters estimationYongting Tao0Jun Zhou1Mingjun Wang2Na Zhang3Yimeng Meng4 College of Engineering, Nanjing Agricultural University, Nanjing, China College of Engineering, Nanjing Agricultural University, Nanjing, China Department of Mechanical Engineering, Ningbo University of Technology, Ningbo, China College of Engineering, Nanjing Agricultural University, Nanjing, China College of Engineering, Nanjing Agricultural University, Nanjing, ChinaIt is a challenging task to achieve rapid and stable grasping of fruit and vegetable without damages for the agricultural robot. From the point of view of which most of fruits and vegetables are viscoelastic material, the viscoelastic characteristic of tomato was analyzed based on Burgers model in this article to provide a reference for the robotic grasping. First, the real-time viscoelastic parameters estimation model based on back-propagation neural network was established. The 3-11-4 network structure was applied, where the grasping force, displacement, and time were input to the model to estimate four viscoelastic parameters. The relative error was less than 15% at the 0.2-s estimation and correlation coefficient of fitting could reach to 0.99. Then, the expression of plastic deformation was derived by analyzing the dynamic characteristic of tomato based on Burgers model and Gripper’s model during grasping. The minimum plastic deformation was taken as the condition to optimize the grasping speed and operation time. Finally, the result of simulation and experiment showed the feasibility of the method proposed in this article. This research can achieve the goal of reducing the grasping time of robots without damaging the fruit and provide a reference for robots grasping process optimization.https://doi.org/10.1177/1729881417724190 |
| spellingShingle | Yongting Tao Jun Zhou Mingjun Wang Na Zhang Yimeng Meng An optimum strategy for robotic tomato grasping based on real-time viscoelastic parameters estimation International Journal of Advanced Robotic Systems |
| title | An optimum strategy for robotic tomato grasping based on real-time viscoelastic parameters estimation |
| title_full | An optimum strategy for robotic tomato grasping based on real-time viscoelastic parameters estimation |
| title_fullStr | An optimum strategy for robotic tomato grasping based on real-time viscoelastic parameters estimation |
| title_full_unstemmed | An optimum strategy for robotic tomato grasping based on real-time viscoelastic parameters estimation |
| title_short | An optimum strategy for robotic tomato grasping based on real-time viscoelastic parameters estimation |
| title_sort | optimum strategy for robotic tomato grasping based on real time viscoelastic parameters estimation |
| url | https://doi.org/10.1177/1729881417724190 |
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