Flexible grasping of robot arm based on improved Informed-RRT star
With advancements in science and technology, collaborative and industrial robotic arms are increasingly gaining popularity. Enhancing the intelligence and autonomy of robot arms, particularly in autonomous grasping, has become one of the research hotspots in robotics research. To improve the efficie...
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| Format: | Article |
| Language: | zho |
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Science Press
2025-01-01
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| Series: | 工程科学学报 |
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| Online Access: | http://cje.ustb.edu.cn/article/doi/10.13374/j.issn2095-9389.2024.04.07.001 |
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| _version_ | 1846114582180921344 |
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| author | Xiong YIN Yan CHEN Wenhao GUO Zichen YANG Hanxin CHEN An LIAO Daojin YAO |
| author_facet | Xiong YIN Yan CHEN Wenhao GUO Zichen YANG Hanxin CHEN An LIAO Daojin YAO |
| author_sort | Xiong YIN |
| collection | DOAJ |
| description | With advancements in science and technology, collaborative and industrial robotic arms are increasingly gaining popularity. Enhancing the intelligence and autonomy of robot arms, particularly in autonomous grasping, has become one of the research hotspots in robotics research. To improve the efficiency and success rate of industrial robot arms in grasping target objects and avoiding obstacles, a three-finger pneumatic flexible clamp was selected, and a flexible grasping module was designed. Communication between the upper computer and the single-chip computer via a serial port enables clamping and loosening actions, constructing an autonomous grasping system based on the traditional Informed -RRT* algorithm. An improved info-RRT * algorithm (Grasping informed-RRT *, GI-RRT*) for the GR-ConvNet model is proposed. First, the maximum number of iterations and the adaptive function are pre-set to shorten the generation time of the manipulator’s motion trajectory and enhance sampling guidance and quality. Second, direct sampling of elliptical subsets constrains the position of sampling points, improving sampling efficiency. Finally, a greedy algorithm deletes redundant path points, and a cubic B-spline curve smoothly constrains the trajectory of the robot arm, shortening its length and improving flexibility. The generated residual convolutional neural network (GR-ConvNet) model predicts inputs from color and depth images captured by a depth camera, outputting the appropriate mapping grab pose of the object in the field of view. To verify the grasping effect of the robot arm, simulation and grasping experiments were conducted on the cooperative robot arm FR3. Simulation results show that, compared with the traditional Informed-RRT* algorithm, the improved algorithm shortens trajectory length by 10.11% and reduces trajectory generation time by 62.68%. The robot arm independently avoids obstacles and grasps target objects, meeting the requirements for autonomous grasping. Experiments with the cooperative robot arm demonstrate its ability to independently grasp objects independently and successfully avoid obstacles. This further validates the algorithm’s effectiveness on a real robot arm, bringing hope for its further development and use. It reduces the difficulty for operators to use the robot arm and accelerates the wide application of domestic robot arms in factories. This paper aims to promote the practical application of robot arms. |
| format | Article |
| id | doaj-art-4f8f65af699748ac921e80c5cfc8db1d |
| institution | Kabale University |
| issn | 2095-9389 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Science Press |
| record_format | Article |
| series | 工程科学学报 |
| spelling | doaj-art-4f8f65af699748ac921e80c5cfc8db1d2024-12-20T09:19:13ZzhoScience Press工程科学学报2095-93892025-01-0147111312010.13374/j.issn2095-9389.2024.04.07.001240407-0001Flexible grasping of robot arm based on improved Informed-RRT starXiong YIN0Yan CHEN1Wenhao GUO2Zichen YANG3Hanxin CHEN4An LIAO5Daojin YAO6China School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, ChinaChina School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, ChinaChina School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, ChinaChina School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, ChinaChina School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, ChinaChina School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, ChinaChina School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, ChinaWith advancements in science and technology, collaborative and industrial robotic arms are increasingly gaining popularity. Enhancing the intelligence and autonomy of robot arms, particularly in autonomous grasping, has become one of the research hotspots in robotics research. To improve the efficiency and success rate of industrial robot arms in grasping target objects and avoiding obstacles, a three-finger pneumatic flexible clamp was selected, and a flexible grasping module was designed. Communication between the upper computer and the single-chip computer via a serial port enables clamping and loosening actions, constructing an autonomous grasping system based on the traditional Informed -RRT* algorithm. An improved info-RRT * algorithm (Grasping informed-RRT *, GI-RRT*) for the GR-ConvNet model is proposed. First, the maximum number of iterations and the adaptive function are pre-set to shorten the generation time of the manipulator’s motion trajectory and enhance sampling guidance and quality. Second, direct sampling of elliptical subsets constrains the position of sampling points, improving sampling efficiency. Finally, a greedy algorithm deletes redundant path points, and a cubic B-spline curve smoothly constrains the trajectory of the robot arm, shortening its length and improving flexibility. The generated residual convolutional neural network (GR-ConvNet) model predicts inputs from color and depth images captured by a depth camera, outputting the appropriate mapping grab pose of the object in the field of view. To verify the grasping effect of the robot arm, simulation and grasping experiments were conducted on the cooperative robot arm FR3. Simulation results show that, compared with the traditional Informed-RRT* algorithm, the improved algorithm shortens trajectory length by 10.11% and reduces trajectory generation time by 62.68%. The robot arm independently avoids obstacles and grasps target objects, meeting the requirements for autonomous grasping. Experiments with the cooperative robot arm demonstrate its ability to independently grasp objects independently and successfully avoid obstacles. This further validates the algorithm’s effectiveness on a real robot arm, bringing hope for its further development and use. It reduces the difficulty for operators to use the robot arm and accelerates the wide application of domestic robot arms in factories. This paper aims to promote the practical application of robot arms.http://cje.ustb.edu.cn/article/doi/10.13374/j.issn2095-9389.2024.04.07.001flexible jawrobotic armmotion planninginformed-rrt* algorithmneural network |
| spellingShingle | Xiong YIN Yan CHEN Wenhao GUO Zichen YANG Hanxin CHEN An LIAO Daojin YAO Flexible grasping of robot arm based on improved Informed-RRT star 工程科学学报 flexible jaw robotic arm motion planning informed-rrt* algorithm neural network |
| title | Flexible grasping of robot arm based on improved Informed-RRT star |
| title_full | Flexible grasping of robot arm based on improved Informed-RRT star |
| title_fullStr | Flexible grasping of robot arm based on improved Informed-RRT star |
| title_full_unstemmed | Flexible grasping of robot arm based on improved Informed-RRT star |
| title_short | Flexible grasping of robot arm based on improved Informed-RRT star |
| title_sort | flexible grasping of robot arm based on improved informed rrt star |
| topic | flexible jaw robotic arm motion planning informed-rrt* algorithm neural network |
| url | http://cje.ustb.edu.cn/article/doi/10.13374/j.issn2095-9389.2024.04.07.001 |
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