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A two-stage grasp detection method for sequential robotic grasping in stacking scenarios.
Jing Zhang1,2, Baoqun Yin1, Yu Zhong2
1Department of Automation, University of Science and Technology of China, Hefei 230027, China.
Mathematical Biosciences and Engineering : MBE
|March 8, 2024
Summary
This study introduces a two-phase robotic grasping method for stacked objects, achieving high success rates in simulations and real-world experiments. The approach enhances robot manipulation in complex stacking scenarios.
Area of Science:
- Robotics
- Computer Vision
- Artificial Intelligence
Background:
- Dexterous grasping is crucial for robotic fine manipulation but challenging in stacking scenarios.
- Existing methods struggle with sequential grasping of stacked objects.
Purpose of the Study:
- To propose a novel two-phase approach for grasp detection in sequential robotic grasping for stacking tasks.
- To enhance the accuracy and success rate of robotic grasping in complex stacked environments.
Main Methods:
- Developed a rotated-YOLOv3 (R-YOLOv3) model for detecting top-layer objects in stacked scenarios.
- Created a stacked scenario dataset for training and testing the R-YOLOv3 network.
- Utilized a G-ResNet50 model to determine optimal grasping poses for the uppermost objects.
Main Results:
- The R-YOLOv3 model achieved an average grasping prediction success rate of 96.60% on the Cornell grasping dataset.
- In real-world experiments, the robot achieved a maximum grasping success rate of 95.00% and an average handling success rate of 83.93% in stacked scenarios.
Conclusions:
- The proposed two-phase approach effectively enables robots to perform sequential grasping in complex stacked environments.
- The methodology demonstrates high efficacy and competitiveness for robotic manipulation tasks involving stacked objects.
