{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T13:07:33Z","timestamp":1771592853434,"version":"3.50.1"},"reference-count":37,"publisher":"Cambridge University Press (CUP)","issue":"12","license":[{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Grasp detection is a significant research direction in the field of robotics. Traditional analysis methods typically require prior knowledge of the object parameters, limiting grasp detection to structured environments and resulting in suboptimal performance. In recent years, the generative convolutional neural network (GCNN) has gained increasing attention, but they suffer from issues such as insufficient feature extraction capabilities and redundant noise. Therefore, we proposed an improved method for the GCNN, aimed at enabling fast and accurate grasp detection. First, a two-dimensional (2D) Gaussian kernel was introduced to re-encode grasp quality to address the issue of false positives in grasp rectangular metrics, emphasizing high-quality grasp poses near the central point. Additionally, to address the insufficient feature extraction capabilities of the shallow network, a receptive field module was added at the neck to enhance the network\u2019s ability to extract distinctive features. Furthermore, the rich feature information in the decoding phase often contains redundant noise. To address this, we introduced a global-local feature fusion module to suppress noise and enhance features, enabling the model to focus more on target information. Finally, relevant evaluation experiments were conducted on public grasping datasets, including Cornell, Jacquard, and GraspNet-1 Billion, as well as in real-world robotic grasping scenarios. All results showed that the proposed method performs excellently in both prediction accuracy and inference speed and is practically feasible for robotic grasping.<\/jats:p>","DOI":"10.1017\/s0263574725102750","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T08:30:43Z","timestamp":1763109043000},"page":"4191-4211","source":"Crossref","is-referenced-by-count":0,"title":["Research on robotic grasp detection using improved generative convolution neural network with Gaussian representation"],"prefix":"10.1017","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3343-7642","authenticated-orcid":false,"given":"Zhanglai","family":"Chen","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/006teas31","id-type":"ROR","asserted-by":"publisher"}],"name":"Shanghai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Zhang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/006teas31","id-type":"ROR","asserted-by":"publisher"}],"name":"Shanghai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dawei","family":"Tu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/006teas31","id-type":"ROR","asserted-by":"publisher"}],"name":"Shanghai University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"S0263574725102750_ref27","doi-asserted-by":"crossref","unstructured":"[27] Karaoguz, H. and Jensfelt, P. , \"Object Detection Approach for Robot Grasp Detection,\" In: 2019 International Conference on Robotics and Automation (ICRA), Montreal, Canada (2019).","DOI":"10.1109\/ICRA.2019.8793751"},{"key":"S0263574725102750_ref23","doi-asserted-by":"crossref","unstructured":"[23] Hu, J. , Shen, L. and Sun, G. , \"Squeeze-and-Excitation Networks,\" In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah (2018).","DOI":"10.1109\/CVPR.2018.00745"},{"key":"S0263574725102750_ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-51532-8_19"},{"key":"S0263574725102750_ref18","doi-asserted-by":"crossref","unstructured":"[18] Kumra, S. , Joshi, S. and Sahin, F. , \"Antipodal Robotic Grasping Using Generative Residual Convolutional Neural Network,\" In: 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, Nevada, USA (2020).","DOI":"10.1109\/IROS45743.2020.9340777"},{"key":"S0263574725102750_ref34","doi-asserted-by":"crossref","unstructured":"[34] Johns, E. , Leutenegger, S. and Davison, A. 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S. , Wang, C. , Gou, M. and Lu, C. , \"GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping,\" In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA (2020).","DOI":"10.1109\/CVPR42600.2020.01146"},{"key":"S0263574725102750_ref16","doi-asserted-by":"crossref","unstructured":"[16] Zhou, X. , Lan, X. , Zhang, H. , Tian, Z. , Zhang, Y. and Zheng, N. , \"Fully Convolutional Grasp Detection Network with Oriented Anchor Box,\" In: 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain (2018).","DOI":"10.1109\/IROS.2018.8594116"},{"key":"S0263574725102750_ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TMECH.2022.3224314"},{"key":"S0263574725102750_ref26","doi-asserted-by":"crossref","unstructured":"[26] Kumra, S. and Kanan, C. , \"Robotic Grasp Detection Using Deep Convolutional Neural Networks,\" In: 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada (2017).","DOI":"10.1109\/IROS.2017.8202237"},{"key":"S0263574725102750_ref32","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574724001358"},{"key":"S0263574725102750_ref12","unstructured":"[12] Yun, J. , Moseson, S. and Saxena, A. , \"Efficient Grasping from RGBD Images: Learning Using a New Rectangle Representation,\" In: 2011 IEEE International Conference on Robotics and Automation, Shanghai, China (2011)."},{"key":"S0263574725102750_ref15","unstructured":"[15] Chu, F.-J. , Xu, R. and Vela, P. 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