Fusion-Mask-RCNN: Visual robotic grasping in cluttered scenes

Ge, Junyan; Ling-bo, Mao; Shi, Jinlong; Jiang, Yijian

doi:10.1007/s11042-023-16365-y

Multimed Tools Appl

2023

DOI: 10.1007/s11042-023-16365-y

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Fusion-Mask-RCNN: Visual robotic grasping in cluttered scenes

Junyan Ge

Mao Ling-bo

Jinlong Shi

et al.

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2024

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Cited by 1 publication

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A two-stage grasp detection method for sequential robotic grasping in stacking scenarios

Zhang,

Yin,

Zhong

et al. 2024

MBE

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<abstract> <p>Dexterous grasping is essential for the fine manipulation tasks of intelligent robots; however, its application in stacking scenarios remains a challenge. In this study, we aimed to propose a two-phase approach for grasp detection of sequential robotic grasping, specifically for application in stacking scenarios. In the initial phase, a rotated-YOLOv3 (R-YOLOv3) model was designed to efficiently detect the category and position of the top-layer object, facilitating the detection of stacked objects. Subsequently, a stacked scenario dataset with only the top-level objects annotated was built for training and testing the R-YOLOv3 network. In the next phase, a G-ResNet50 model was developed to enhance grasping accuracy by finding the most suitable pose for grasping the uppermost object in various stacking scenarios. Ultimately, a robot was directed to successfully execute the task of sequentially grasping the stacked objects. The proposed methodology demonstrated the average grasping prediction success rate of 96.60% as observed in the Cornell grasping dataset. The results of the 280 real-world grasping experiments, conducted in stacked scenarios, revealed that the robot achieved a maximum grasping success rate of 95.00%, with an average handling grasping success rate of 83.93%. The experimental findings demonstrated the efficacy and competitiveness of the proposed approach in successfully executing grasping tasks within complex multi-object stacked environments.</p> </abstract>

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A two-stage grasp detection method for sequential robotic grasping in stacking scenarios

Zhang,

Yin,

Zhong

et al. 2024

MBE

View full text Add to dashboard Cite

show abstract

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Fusion-Mask-RCNN: Visual robotic grasping in cluttered scenes

Cited by 1 publication

References 32 publications

A two-stage grasp detection method for sequential robotic grasping in stacking scenarios

A two-stage grasp detection method for sequential robotic grasping in stacking scenarios

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