Shi, Chaoquan and Miao, Chunxiao and Zhong, Xungao and Zhong, Xunyu and Hu, Huosheng and Liu, Qiang (2022) Pixel-Reasoning-Based Robotics Fine Grasping for Novel Objects with Deep EDINet Structure. Sensors, 22 (11). p. 4283. DOI https://doi.org/10.3390/s22114283
Shi, Chaoquan and Miao, Chunxiao and Zhong, Xungao and Zhong, Xunyu and Hu, Huosheng and Liu, Qiang (2022) Pixel-Reasoning-Based Robotics Fine Grasping for Novel Objects with Deep EDINet Structure. Sensors, 22 (11). p. 4283. DOI https://doi.org/10.3390/s22114283
Shi, Chaoquan and Miao, Chunxiao and Zhong, Xungao and Zhong, Xunyu and Hu, Huosheng and Liu, Qiang (2022) Pixel-Reasoning-Based Robotics Fine Grasping for Novel Objects with Deep EDINet Structure. Sensors, 22 (11). p. 4283. DOI https://doi.org/10.3390/s22114283
Abstract
Robotics grasp detection has mostly used the extraction of candidate grasping rectangles; those discrete sampling methods are time-consuming and may ignore the potential best grasp synthesis. This paper proposes a new pixel-level grasping detection method on RGB-D images. Firstly, a fine grasping representation is introduced to generate the gripper configurations of parallel-jaw, which can effectively resolve the gripper approaching conflicts and improve the applicability to unknown objects in cluttered scenarios. Besides, the adaptive grasping width is used to adaptively represent the grasping attribute, which is fine for objects. Then, the encoder-decoder-inception convolution neural network (EDINet) is proposed to predict the fine grasping configuration. In our findings, EDINet uses encoder, decoder, and inception modules to improve the speed and robustness of pixel-level grasping detection. The proposed EDINet structure was evaluated on the Cornell and Jacquard dataset; our method achieves 98.9% and 96.1% test accuracy, respectively. Finally, we carried out the grasping experiment on the unknown objects, and the results show that the average success rate of our network model is 97.2% in a single object scene and 93.7% in a cluttered scene, which out-performs the state-of-the-art algorithms. In addition, EDINet completes a grasp detection pipeline within only 25 ms.
Item Type: | Article |
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Uncontrolled Keywords: | Hand Strength; Neural Networks, Computer; Robotics |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 07 Oct 2022 14:30 |
Last Modified: | 07 Aug 2024 19:35 |
URI: | http://repository.essex.ac.uk/id/eprint/33632 |
Available files
Filename: sensors-22-04283-v2.pdf
Licence: Creative Commons: Attribution 3.0