A Light-Weight Grasping Pose Estimation Method for Mobile Robotic Arms Based on Depthwise Separable Convolution
The robotic arm frequently performs grasping tasks in unstructured environments. However, due to the complex network architecture and constantly changing operational environments, balancing between grasping accuracy and speed poses significant challenges. Unlike fixed robotic arms, mobile robotic ar...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2076-0825/14/2/50 |
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| author | Jianguo Duan Chuyan Ye Qin Wang Qinglei Zhang |
| author_facet | Jianguo Duan Chuyan Ye Qin Wang Qinglei Zhang |
| author_sort | Jianguo Duan |
| collection | DOAJ |
| description | The robotic arm frequently performs grasping tasks in unstructured environments. However, due to the complex network architecture and constantly changing operational environments, balancing between grasping accuracy and speed poses significant challenges. Unlike fixed robotic arms, mobile robotic arms offer flexibility but suffer from relatively unstable bases, necessitating improvements in disturbance resistance for grasping tasks. To address these issues, this paper proposes a light-weight grasping pose estimation method called Grasp-DSC, specifically tailored for mobile robotic arms. This method integrates the deep residual shrinkage network and depthwise separable convolution. Attention mechanisms and soft thresholding are employed to improve the arm’s ability to filter out interference, while parallel convolutions enhance computational efficiency. These innovations collectively enhance the grasping decision accuracy and efficiency of mobile robotic arms in complex environments. Grasp-DSC is evaluated using the Cornell Grasp Dataset and Jacquard Grasp Dataset, achieving 96.6% accuracy and a speed of 14.4 ms on the former one. Finally, grasping experiments conducted on the MR2000-UR5 validate the practical applicability of Grasp-DSC in practical scenarios, achieving an average grasping success rate of 96%. |
| format | Article |
| id | doaj-art-11dd7725b2e8440eb1ffb00fe088d36a |
| institution | DOAJ |
| issn | 2076-0825 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-11dd7725b2e8440eb1ffb00fe088d36a2025-08-20T02:44:57ZengMDPI AGActuators2076-08252025-01-011425010.3390/act14020050A Light-Weight Grasping Pose Estimation Method for Mobile Robotic Arms Based on Depthwise Separable ConvolutionJianguo Duan0Chuyan Ye1Qin Wang2Qinglei Zhang3China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, ChinaLogistics Engineering College, Shanghai Maritime University, Shanghai 201306, ChinaEconomics and Management College, Shanghai Maritime University, Shanghai 201306, ChinaChina Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, ChinaThe robotic arm frequently performs grasping tasks in unstructured environments. However, due to the complex network architecture and constantly changing operational environments, balancing between grasping accuracy and speed poses significant challenges. Unlike fixed robotic arms, mobile robotic arms offer flexibility but suffer from relatively unstable bases, necessitating improvements in disturbance resistance for grasping tasks. To address these issues, this paper proposes a light-weight grasping pose estimation method called Grasp-DSC, specifically tailored for mobile robotic arms. This method integrates the deep residual shrinkage network and depthwise separable convolution. Attention mechanisms and soft thresholding are employed to improve the arm’s ability to filter out interference, while parallel convolutions enhance computational efficiency. These innovations collectively enhance the grasping decision accuracy and efficiency of mobile robotic arms in complex environments. Grasp-DSC is evaluated using the Cornell Grasp Dataset and Jacquard Grasp Dataset, achieving 96.6% accuracy and a speed of 14.4 ms on the former one. Finally, grasping experiments conducted on the MR2000-UR5 validate the practical applicability of Grasp-DSC in practical scenarios, achieving an average grasping success rate of 96%.https://www.mdpi.com/2076-0825/14/2/50mobile robotic armgrasping posedeep residual shrinkage networkdepthwise separable convolutionlight-weight grasp |
| spellingShingle | Jianguo Duan Chuyan Ye Qin Wang Qinglei Zhang A Light-Weight Grasping Pose Estimation Method for Mobile Robotic Arms Based on Depthwise Separable Convolution Actuators mobile robotic arm grasping pose deep residual shrinkage network depthwise separable convolution light-weight grasp |
| title | A Light-Weight Grasping Pose Estimation Method for Mobile Robotic Arms Based on Depthwise Separable Convolution |
| title_full | A Light-Weight Grasping Pose Estimation Method for Mobile Robotic Arms Based on Depthwise Separable Convolution |
| title_fullStr | A Light-Weight Grasping Pose Estimation Method for Mobile Robotic Arms Based on Depthwise Separable Convolution |
| title_full_unstemmed | A Light-Weight Grasping Pose Estimation Method for Mobile Robotic Arms Based on Depthwise Separable Convolution |
| title_short | A Light-Weight Grasping Pose Estimation Method for Mobile Robotic Arms Based on Depthwise Separable Convolution |
| title_sort | light weight grasping pose estimation method for mobile robotic arms based on depthwise separable convolution |
| topic | mobile robotic arm grasping pose deep residual shrinkage network depthwise separable convolution light-weight grasp |
| url | https://www.mdpi.com/2076-0825/14/2/50 |
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