{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:52:34Z","timestamp":1760151154186,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T00:00:00Z","timestamp":1645747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Electronics"],"abstract":"<jats:p>In this paper, the robot grasping for stacked objects is studied based on object detection and grasping order planning. Firstly, a novel stacked object classification network (SOCN) is proposed to realize stacked object recognition. The network takes into account the visible volume of the objects to further adjust its inverse density parameters, which makes the training process faster and smoother. At the same time, SOCN adopts the transformer architecture and has a self-attention mechanism for feature learning. Subsequently, a grasping order planning method is investigated, which depends on the security score and extracts the geometric relations and dependencies between stacked objects, it calculates the security score based on object relation, classification, and size. The proposed method is evaluated by using a depth camera and a UR-10 robot to complete grasping tasks. The results show that our method has high accuracy for stacked object classification, and the grasping order effectively and successfully executes safely.<\/jats:p>","DOI":"10.3390\/electronics11050706","type":"journal-article","created":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T10:00:40Z","timestamp":1645783240000},"page":"706","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Robot Grasping Based on Stacked Object Classification Network and Grasping Order Planning"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4826-772X","authenticated-orcid":false,"given":"Chenlu","family":"Liu","sequence":"first","affiliation":[{"name":"Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin 150001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0141-1672","authenticated-orcid":false,"given":"Di","family":"Jiang","sequence":"additional","affiliation":[{"name":"Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin 150001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0493-1289","authenticated-orcid":false,"given":"Weiyang","family":"Lin","sequence":"additional","affiliation":[{"name":"Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin 150001, China"},{"name":"Ningbo Institute of Intelligent Equipment Technology Company Ltd., Ningbo 315200, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4299-8270","authenticated-orcid":false,"given":"Luis","family":"Gomes","sequence":"additional","affiliation":[{"name":"Centre of Technology and Systems, NOVA School of Sciences and Technology, NOVA University Lisbon\/UNINOVA, 2829-516 Monte de Caparica, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, J.Y., Chen, U.K., Chang, K.C., and Chen, Y.J. (2020, January 19\u201321). A Novel Robotic Grasp Detection Technique by Integrating YOLO and Grasp Detection Deep Neural Networks. Proceedings of the 2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS), Taipei, Taiwan.","DOI":"10.1109\/ARIS50834.2020.9205791"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bae, J.H., Jo, H., Kim, D.W., and Song, J.B. (2020, January 13\u201316). Grasping System for Industrial Application Using Point Cloud-Based Clustering. Proceedings of the 2020 20th International Conference on Control, Automation and Systems (ICCAS), Busan, Korea.","DOI":"10.23919\/ICCAS50221.2020.9268284"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3715","DOI":"10.1109\/LRA.2020.2980984","article-title":"Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planning","volume":"5","author":"Zagoruyko","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sun, G.J., and Lin, H.Y. (2020, January 12\u201315). Robotic Grasping Using Semantic Segmentation and Primitive Geometric Model Based 3D Pose Estimation. Proceedings of the 2020 IEEE\/SICE International Symposium on System Integration (SII), Honolulu, HI, USA.","DOI":"10.1109\/SII46433.2020.9026297"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Adamini, R., Antonini, N., Borboni, A., Medici, S., Nuzzi, C., Pagani, R., Pezzaioli, A., and Tonola, C. (2021, January 18\u201322). User-friendly human-robot interaction based on voice commands and visual systems. Proceedings of the 2021 24th International Conference on Mechatronics Technology (ICMT), Singapore.","DOI":"10.1109\/ICMT53429.2021.9687192"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, J., Duan, J., Ou, Y., Xu, S., and Wang, Z. (2020, January 28\u201329). Intelligent Visual Servoing Using Learning-Based Grasping Configurations and Adaptive Controller Gain. Proceedings of the 2020 IEEE International Conference on Real-time Computing and Robotics (RCAR), Asahikawa, Japan.","DOI":"10.1109\/RCAR49640.2020.9303301"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1109\/LRA.2021.3056370","article-title":"Human-Aware Robot Task Planning Based on a Hierarchical Task Model","volume":"6","author":"Cheng","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s10846-015-0330-z","article-title":"Single and multiple view support order prediction in clutter for manipulation","volume":"83","author":"Panda","year":"2016","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jiang, D., and Yi, J. (2012, January 11\u201313). Comparison and Study of Classic Feature Point Detection Algorithm. Proceedings of the 2012 International Conference on Computer Science and Service System, Nanjing, China.","DOI":"10.1109\/CSSS.2012.572"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, L., Ouyang, W., Wang, X., and Lu, H. (2015, January 7\u201313). Visual tracking with fully convolutional networks. Proceedings of the IEEE International Conference On Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.357"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Grotz, M., Kaiser, P., Aksoy, E.E., Paus, F., and Asfour, T. (2017, January 15\u201317). Graph-based visual semantic perception for humanoid robots. Proceedings of the 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), Birgmingham, UK.","DOI":"10.1109\/HUMANOIDS.2017.8246974"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/LRA.2020.3026970","article-title":"Object-independent human-to-robot handovers using real time robotic vision","volume":"6","author":"Rosenberger","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhuang, Z., Yu, X., and Mahony, R. (August, January 31). LyRN (Lyapunov Reaching Network): A Real-Time Closed Loop approach from Monocular Vision. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9196781"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Shi, J., Zhou, Y., and Zhang, W.X.Q. (2019, January 27\u201330). Target detection based on improved mask rcnn in service robot. Proceedings of the 2019 Chinese Control Conference (CCC), Guangzhou, China.","DOI":"10.23919\/ChiCC.2019.8866278"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"11870","DOI":"10.1109\/JSEN.2020.3030791","article-title":"Robotic Grasping with Multi-View Image Acquisition and Model-Based Pose Estimation","volume":"21","author":"Lin","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Matsuno, D., Hachiuma, R., Saito, H., Sugano, J., and Adachi, H. (2020, January 17\u201319). Pose Estimation of Stacked Rectangular Objects from Depth Images. Proceedings of the 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE), Delft, The Netherlands.","DOI":"10.1109\/ISIE45063.2020.9152510"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yang, C., Lan, X., Zhang, H., and Zheng, N. (2019, January 3\u20138). Task-oriented Grasping in Object Stacking Scenes with CRF-based Semantic Model. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967992"},{"key":"ref_18","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 5\u20138). Pointnet: Deep learning on point sets for 3d classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Penang, Malaysia."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, C.H., and Lin, P.C. (2020, January 6\u20139). Q-PointNet: Intelligent Stacked-Objects Grasping Using a RGBD Sensor and a Dexterous Hand. Proceedings of the 2020 IEEE\/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Boston, MA, USA.","DOI":"10.1109\/AIM43001.2020.9158850"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., and Fuxin, L. (2019, January 16\u201317). Pointconv: Deep convolutional networks on 3d point clouds. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00985"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-End Object Detection with Transformers. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lin, K., Wang, L., and Liu, Z. (2020). End-to-End Human Pose and Mesh Reconstruction with Transformers. arXiv.","DOI":"10.1109\/CVPR46437.2021.00199"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Guo, D., and Terzopoulos, D. (2021, January 10\u201315). A Transformer-Based Network for Anisotropic 3D Medical Image Segmentation. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9411990"},{"key":"ref_24","unstructured":"Guo, D., Kong, T., Sun, F., and Liu, H. (2016, January 16\u201321). Object discovery and grasp detection with a shared convolutional neural network. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, H., Lan, X., Zhou, X., Tian, Z., Zhang, Y., and Zheng, N. (2018, January 6\u20139). Visual manipulation relationship network for autonomous robotics. Proceedings of the 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), Beijing, China.","DOI":"10.1109\/HUMANOIDS.2018.8625071"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, H., Lan, X., Bai, S., Wan, L., Yang, C., and Zheng, N. (2019, January 3\u20138). A multi-task convolutional neural network for autonomous robotic grasping in object stacking scenes. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967977"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1109\/LRA.2020.2970622","article-title":"A deep learning approach to grasping the invisible","volume":"5","author":"Yang","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3991","DOI":"10.1109\/LRA.2018.2859448","article-title":"Extraction of Physically Plausible Support Relations to Predict and Validate Manipulation Action Effects","volume":"3","author":"Kartmann","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tsujibayashi, T., Inoue, K., and Yoshioka, M. (2018, January 8). Normal Estimation of Surface in PointCloud Data for 3D Parts Segmentation. Proceedings of the 2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Kota Kinabalu, Malaysia.","DOI":"10.1109\/IICAIET.2018.8638451"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Miknis, M., Davies, R., Plassmann, P., and Ware, A. (2015, January 10\u201312). Near real-time point cloud processing using the PCL. Proceedings of the 2015 International Conference on Systems, Signals and Image Processing (IWSSIP), London, UK.","DOI":"10.1109\/IWSSIP.2015.7314200"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2771","DOI":"10.1109\/TCYB.2014.2316282","article-title":"NCC-RANSAC: A fast plane extraction method for 3-D range data segmentation","volume":"44","author":"Qian","year":"2014","journal-title":"IEEE Trans. Cybern."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Stein, S.C., Schoeler, M., Papon, J., and W\u00f6rg\u00f6tter, F. (2014, January 23\u201328). Object Partitioning Using Local Convexity. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.46"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_34","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."}],"container-title":["Electronics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-9292\/11\/5\/706\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:27:09Z","timestamp":1760135229000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-9292\/11\/5\/706"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,25]]},"references-count":34,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["electronics11050706"],"URL":"https:\/\/doi.org\/10.3390\/electronics11050706","relation":{},"ISSN":["2079-9292"],"issn-type":[{"type":"electronic","value":"2079-9292"}],"subject":[],"published":{"date-parts":[[2022,2,25]]}}}