{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T02:55:37Z","timestamp":1764212137939,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,3,2]],"date-time":"2020-03-02T00:00:00Z","timestamp":1583107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In order to save people\u2019s shopping time and reduce labor cost of supermarket operations, this paper proposes to design a supermarket service robot based on deep convolutional neural networks (DCNNs). Firstly, according to the shopping environment and needs of supermarket, the hardware and software structure of supermarket service robot is designed. The robot uses a robot operating system (ROS) middleware on Raspberry PI as a control kernel to implement wireless communication with customers and staff. So as to move flexibly, the omnidirectional wheels symmetrically installed under the robot chassis are adopted for tracking. The robot uses an infrared detection module to detect whether there are commodities in the warehouse or shelves or not, thereby grasping and placing commodities accurately. Secondly, the recently-developed single shot multibox detector (SSD), as a typical DCNN model, is employed to detect and identify objects. Finally, in order to verify robot performance, a supermarket environment is designed to simulate real-world scenario for experiments. Experimental results show that the designed supermarket service robot can automatically complete the procurement and replenishment of commodities well and present promising performance on commodity detection and recognition tasks.<\/jats:p>","DOI":"10.3390\/sym12030360","type":"journal-article","created":{"date-parts":[[2020,3,2]],"date-time":"2020-03-02T07:50:53Z","timestamp":1583135453000},"page":"360","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Designing a Supermarket Service Robot Based on Deep Convolutional Neural Networks"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8397-1456","authenticated-orcid":false,"given":"Aihua","family":"Chen","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Taizhou University, Taizhou 318017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benquan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Taizhou University, Taizhou 318017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueli","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Taizhou University, Taizhou 318017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuefen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Taizhou University, Taizhou 318017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Taizhou University, Taizhou 318017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoming","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Taizhou University, Taizhou 318017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.procir.2018.03.224","article-title":"Deep learning-based multimodal control interface for human-robot collaboration","volume":"72","author":"Liu","year":"2018","journal-title":"Procedia CIRP"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Fang, Z., Weng, W., Wang, W., Zhang, C., and Yang, G. (2019). A Vision-Based Robotic Laser Welding System for Insulated Mugs with Fuzzy Seam Tracking Control. Symmetry, 11.","DOI":"10.3390\/sym11111385"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104973","DOI":"10.1016\/j.compag.2019.104973","article-title":"Deep learning-based visual recognition of rumex for robotic precision farming","volume":"165","author":"Kounalakis","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.mechmachtheory.2018.12.035","article-title":"Distal-end force prediction of tendon-sheath mechanisms for flexible endoscopic surgical robots using deep learning","volume":"134","author":"Li","year":"2019","journal-title":"Mech. Mach. Theory"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.robot.2019.03.005","article-title":"A Survey of Knowledge Representation in Service Robotics","volume":"118","author":"Paulius","year":"2019","journal-title":"Robot. Auton. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.chb.2017.02.064","article-title":"Shopping with a robotic companion","volume":"77","author":"Bertacchini","year":"2017","journal-title":"Comput. Hum. Behav."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cheng, C.-H., Chen, C.-Y., Liang, J.-J., Tsai, T.-N., Liu, C.-Y., and Li, T.-H.S. (2017, January 6\u20138). Design and implementation of prototype service robot for shopping in a supermarket. Proceedings of the 2017 International Conference on Advanced Robotics and Intelligent Systems (ARIS), Taipei, Taiwan.","DOI":"10.1109\/ARIS.2017.8297181"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.robot.2018.11.001","article-title":"Are we done with object recognition? The iCub robot\u2019s perspective","volume":"112","author":"Pasquale","year":"2019","journal-title":"Robot. Auton. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cartucho, J., Ventura, R., and Veloso, M. (2018, January 1\u20135). Robust Object Recognition Through Symbiotic Deep Learning In Mobile Robots. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594067"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale invariant key points","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_11","unstructured":"Ke, Y., and Sukthankar, R. (July, January 27). PCA-SIFT: A more distinctive representation for local image descriptors. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA."},{"key":"ref_12","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_13","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u22128). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, CA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3030","DOI":"10.1109\/TCSVT.2017.2719043","article-title":"Learning affective features with a hybrid deep model for audio\u2013visual emotion recognition","volume":"28","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_16","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_17","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 8\u201316). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_21","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201322). Cascade r-cnn: Delving into high quality object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_25","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (2016, January 5\u201310). R-fcn: Object detection via region-based fully convolutional networks. Proceedings of the Advances in Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_26","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, Y., Dai, S., Zhao, L., Yan, X., and Shi, Y. (2019). Topological Design Methods for Mecanum Wheel Configurations of an Omnidirectional Mobile Robot. Symmetry, 11.","DOI":"10.3390\/sym11101268"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chang, Y.-H., Chung, P.-L., and Lin, H.-W. (2018, January 13\u201317). Deep learning for object identification in ROS-based mobile robots. Proceedings of the 2018 IEEE International Conference on Applied System Invention (ICASI), Chiba, Japan.","DOI":"10.1109\/ICASI.2018.8394348"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bayar, V., Akar, B., Yayan, U., Yavuz, H.S., and Yazici, A. (2014, January 23\u201325). Fuzzy logic based design of classical behaviors for mobile robots in ROS middleware. Proceedings of the IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, Alberobello, Italy.","DOI":"10.1109\/INISTA.2014.6873613"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1109\/TVT.2018.2885978","article-title":"Context-Aware Wireless-Protocol Selection in Heterogeneous Public Safety Networks","volume":"68","author":"Sikeridis","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_31","unstructured":"Butt, T.A., Phillips, I., Guan, L., and Oikonomou, G. (2013, January 28\u201330). Adaptive and context-aware service discovery for the internet of things. Proceedings of the 13th International Conference, NEW2AN 2013 and 6th Conference, ruSMART 2013, Petersburg, Russia."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tsiropoulou, E.E., Baras, J.S., Papavassiliou, S., and Qu, G. (2016, January 2\u20134). On the mitigation of interference imposed by intruders in passive RFID networks. Proceedings of the International Conference on Decision and Game Theory for Security, New York, NY, USA.","DOI":"10.1007\/978-3-319-47413-7_4"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1109\/JIOT.2014.2344013","article-title":"Sybil attacks and their defenses in the internet of things","volume":"1","author":"Zhang","year":"2014","journal-title":"IEEE Internet Things J."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/3\/360\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:03:11Z","timestamp":1760173391000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/3\/360"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,2]]},"references-count":33,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["sym12030360"],"URL":"https:\/\/doi.org\/10.3390\/sym12030360","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2020,3,2]]}}}