Research on Robotic Arm Grasping Algorithm Based on an Enhanced Edge Network
DOI:
https://doi.org/10.54097/9phdd715Keywords:
Robotic Arm Grasp Detection, Edge Grasp Detection Network, Self-Attention Mechanism, Graph Neural Network ModuleAbstract
To address the issue of low grasp detection accuracy in robotic arms, an improved point cloud-based grasp detection model, TES-Net (TransformEdgeSAGE Network), is proposed, which builds upon the Edge Grasp Network architecture. In this model, PointTransformerConv layers are first employed to extract local features from the point cloud, integrating a self-attention mechanism to capture the complex relationships inherent within the point cloud data. Subsequently, the SAGEConv graph neural network module is utilized to perform feature aggregation on the adjacency graph, thereby mitigating the issue of neglecting the inter-point relationships during the feature extraction phase and enhancing the overall network performance. Experimental results demonstrate that, in comparison to existing state-of-the-art point cloud grasp detection methods, the proposed model exhibits superior generalization capability, improved robustness and stability, as well as higher accuracy.
Downloads
References
[1] Liu J, Sun W, Yang H, et al. Deep Learning-Based Object Pose Estimation: A Comprehensive Survey[J]. arXiv preprint arXiv: 2405. 07801, 2024.
[2] Ten Pas A, Gualtieri M, Saenko K, et al. Grasp pose detection in point clouds[J]. The International Journal of Robotics Research, 2017, 36(13-14): 1455-1473.
[3] Liang H, Ma X, Li S, et al. Pointnetgpd: Detecting grasp configurations from point sets[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 3629-3635.
[4] Mousavian A, Eppner C, Fox D. 6-dof graspnet: Variational grasp generation for object manipulation[C]//Proceedings of the IEEE/ CVF international conference on computer vision. 2019: 2901-2910.
[5] Qin Y, Chen R, Zhu H, et al. S4g: Amodal single-view single-shot se (3) grasp detection in cluttered scenes[C]//Conference on robot learning. PMLR, 2020: 53-65.
[6] Qi C R, Yi L, Su H, et al. Pointnet++: Deep hierarchical feature learning on point sets in a metric space[J]. Advances in neural information processing systems, 2017, 30.
[7] Xu J, Pan Y, Pan X, et al. RegNet: Self-regulated network for image classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 34(11): 9562-9567.
[8] Huang H, Wang D, Zhu X, et al. Edge grasp network: A graph-based se (3)-invariant approach to grasp detection[C]//2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023: 3882-3888.
[9] Hamilton W, Ying Z, Leskovec J. Inductive representation learning on large graphs[J]. Advances in neural information processing systems, 2017, 30.
[10] Xiang Y, Schmidt T, Narayanan V, et al. Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes[J]. arXiv preprint arXiv:1711.00199, 2017.
[11] Singh A, Sha J, Narayan K S, et al. Bigbird:(big) berkeley instance recognition dataset[C]//2014 IEEE International Conference on Robotics and Automation. 2014: 509-516.
[12] Kasper A, Xue Z, Dillmann R. The kit object models database: An object model database for object recognition, localization and manipulation in service robotics[J]. The International Journal of Robotics Research, 2012, 31(8): 927-934.
[13] Breyer M, Chung J J, Ott L, et al. Volumetric grasping network: Real-time 6 dof grasp detection in clutter[C]//Conference on Robot Learning. PMLR, 2021: 1602-1611.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Frontiers in Computing and Intelligent Systems

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.