Abstract
In order to enhance the efficiency and accuracy of robots in automated production lines and address issues such as inaccurate positioning and limited real-time capabilities in robot-controlled grasping, a deep learning-based lightweight algorithm for robot object grasping is proposed. This algorithm optimizes the lightweight network GG-CNN2 as the base model. Firstly, the depth of the backbone network is increased, and transpose convolutions are replaced with dilated convolutions to enhance the network’s feature extraction for grasping detection. Secondly, the ASPP module is introduced to obtain a wider receptive field and multi-scale feature information. Furthermore, the shallow feature maps are merged with the deep feature maps to incorporate more semantic and detailed information from the images. Experimental results demonstrate that the algorithm achieves an accuracy of 81.27% on the Cornell dataset. Compared to the original GG-CNN2 network, the accuracy has improved by 11.68%, achieving a balance between speed and accuracy. Finally, grasping verification is conducted on the Panda robot arm, with an average success rate of 89.62%, which validates the superiority of the algorithm and showcases the theoretical and practical value of this research.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, J., Chen, J., Sheng, B., et al.: Automatic detection and classification system of domestic waste via multimodel cascaded convolutional neural network. IEEE Trans. Ind. Inform. 18(1), 163–173 (2022)
Lenz, I., Lee, H., Saxena, A.: Deep learning for detecting robotic grasps. Int. J. Robot. Res. 34(4–5), 705–724 (2015)
Ma, Q., Li, X., Shi, Z.: Lightweight convolutional neural networks for robot grasping detection. Comput. Eng. Appl. 56(10), 141–148 (2020)
Li, C.: Deep learning-based pose estimation for unknown targets. Southwest University of Science and Technology (2022)
Morrison, D., Corke, P., Leitner, J.: Learning robust, real-time, reactive robotic grasping. Int. J. Robot. Res. 39(2/3), 183–201 (2020)
Zhang, X., Xi, Y., Huang, Z., et al.: Active hand-eye calibration via online accuracy-driven next-best-view selection. Vis. Comput. 4–5, 1–11 (2022)
Wang, C.H.: Grabbing Configuration Prediction Based on RGB-D Images and Deep Learning. Shandong University (2020)
Zhang, X., Gao, H., Wan, L.: Classification of fine-grained crop disease by dilated convolution and improved channel attention module. Agriculture 12(10), 1727 (2022)
Chen, L.C., Papandreou, G., Kokkinos, I., et al.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2016)
Zhao, J., Du, B.: Development of small target detection technology based on deep learning [J/OL]. Electro-Optics Control 1–10 (2022)
Chu, H., Leng, Q., Zhang, X.Q.: Multimode feature robotic arm grasping pose detection incorporating attention mechanism[J/OL]. Control Decis. 1–9 (2022)
Du, S.Z.K.: Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review. Artif. Intell. Rev.: Int. Sci. Eng. J. 54(3) (2021)
Wang, D.: Research on robot grasping detection algorithm in stacked scenes based on deep learning. Shandong University (2022)
Hsiao, K., Lozano-Pérez, T.: Imitation Learning of Whole. In: IEEE/RSJ International Conference on Intelligent Robots & Systems (2006)
Chen, Z., Qiu, J., Sheng, B., et al.: GPSD: generative parking spot detection using multi-clue recovery model. Vis. Comput. 37(6), 2657–2669 (2021)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (51475251), the Shandong Province Key R&D Program (2023RZA02017) and the Livelihood Plan of Qingdao City (22-3-7-xdny-18-nsh).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhao, Y., Wei, T., Du, B., Zhao, J. (2024). Research on Deep Learning-Based Lightweight Object Grasping Algorithm for Robots. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14497. Springer, Cham. https://doi.org/10.1007/978-3-031-50075-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-031-50075-6_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50074-9
Online ISBN: 978-3-031-50075-6
eBook Packages: Computer ScienceComputer Science (R0)