Zhang Jing, Yin Baoqun, Zhong Yu, Wei Qiang, Zhao Jia, Bilal Hazrat
Department of Automation, University of Science and Technology of China, Hefei 230027, China.
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China.
Math Biosci Eng. 2024 Feb 5;21(2):3448-3472. doi: 10.3934/mbe.2024152.
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.
灵巧抓取对于智能机器人的精细操作任务至关重要；然而，其在堆叠场景中的应用仍然是一项挑战。在本研究中，我们旨在提出一种用于顺序机器人抓取的抓取检测的两阶段方法，特别是用于堆叠场景。在初始阶段，设计了一种旋转YOLOv3（R-YOLOv3）模型，以有效地检测顶层物体的类别和位置，便于检测堆叠物体。随后，构建了一个仅标注顶层物体的堆叠场景数据集，用于训练和测试R-YOLOv3网络。在下一阶段，开发了一个G-ResNet50模型，通过在各种堆叠场景中找到抓取最上层物体的最合适姿态来提高抓取精度。最终，引导机器人成功执行顺序抓取堆叠物体的任务。在康奈尔抓取数据集中观察到，所提出的方法展示了96.60%的平均抓取预测成功率。在堆叠场景中进行的280次实际抓取实验结果表明，机器人实现了95.00%的最大抓取成功率，平均操作抓取成功率为83.93%。实验结果证明了所提出方法在复杂多物体堆叠环境中成功执行抓取任务的有效性和竞争力。
