{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T02:51:54Z","timestamp":1777949514502,"version":"3.51.4"},"reference-count":25,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T00:00:00Z","timestamp":1777766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Mobile manipulators have become essential platforms for autonomous tasks that demand high-quality performance and efficient operational processes. This paper presents a complete grocery pick-and-pack system for a mobile manipulator, integrating a graphical user interface (GUI) with an end-to-end vision-based grasp detection pipeline designed for lightweight computation. The system is evaluated on the Grocery Pick-and-Pack Benchmark (Level-3), the most challenging level due to deformable objects, dimensional constraints, and strict grasp-point requirements. Experimental results demonstrate an average success rate of 92% across five item classes, with the deformable sweet bag the most challenging at 60% and an average execution time of 7.5 s on an edge device. The system achieves strong computational efficiency, reflected by a compute-to-speed ratio (CSR) of 0.008, with a total model size of only 30.9 MB. Performance is further validated across multiple hardware platforms and under real competition scenarios in the European Robotics League 2025. The findings highlight the practical impact of lightweight, vision-based mobile manipulation and provide insights into current challenges and future research directions for autonomous robotic applications.<\/jats:p>","DOI":"10.3390\/s26092860","type":"journal-article","created":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T01:12:09Z","timestamp":1777857129000},"page":"2860","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Complete Grocery Pick-and-Pack Application Using a Computationally Lightweight Vision-Based Mobile Manipulator"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5792-5120","authenticated-orcid":false,"given":"Thanavin","family":"Mansakul","sequence":"first","affiliation":[{"name":"Centre for Robotics and Assembly, Faculty of Engineering and Applied Sciences, Cranfield University, Bedford MK43 0AL, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5497-7269","authenticated-orcid":false,"given":"Gilbert","family":"Tang","sequence":"additional","affiliation":[{"name":"Centre for Robotics and Assembly, Faculty of Engineering and Applied Sciences, Cranfield University, Bedford MK43 0AL, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Phil","family":"Webb","sequence":"additional","affiliation":[{"name":"Centre for Robotics and Assembly, Faculty of Engineering and Applied Sciences, Cranfield University, Bedford MK43 0AL, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jamie","family":"Rice","sequence":"additional","affiliation":[{"name":"Centre for Robotics and Assembly, Faculty of Engineering and Applied Sciences, Cranfield University, Bedford MK43 0AL, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Oakley","sequence":"additional","affiliation":[{"name":"Centre for Robotics and Assembly, Faculty of Engineering and Applied Sciences, Cranfield University, Bedford MK43 0AL, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Fowler","sequence":"additional","affiliation":[{"name":"Centre for Robotics and Assembly, Faculty of Engineering and Applied Sciences, Cranfield University, Bedford MK43 0AL, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,3]]},"reference":[{"key":"ref_1","unstructured":"Department for Work and Pensions (2026, March 10). Economic Labour Market Status of Individuals Aged 50 and over, Trends over Time: September 2025. Official Statistics, Published 30 September 2025, Available online: https:\/\/www.gov.uk\/government\/statistics\/economic-labour-market-status-of-individuals-aged-50-and-over-trends-over-time-september-2025\/economic-labour-market-status-of-individuals-aged-50-and-over-trends-over-time-september-2025."},{"key":"ref_2","unstructured":"Smart City Consultancy (2026, March 10). Smart City Robotics Competition 2025. Available online: https:\/\/www.smartcityconsultancy.co.uk\/robotcomp2025."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Uj, A., Lintfert, B., and K\u00e4\u00dfer-Pawelka, G. (2025). Skilled Labor Shortage and Artificial Intelligence: Challenges and Opportunities for the Regional Labor Market. Proceedings of the International Conference on Human-Computer Interaction, Springer.","DOI":"10.1007\/978-3-031-92823-9_15"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mansakul, T., Tang, G., Webb, P., Rice, J., Oakley, D., and Fowler, J. (2025). An End-to-End Computationally Lightweight Vision-Based Grasping System for Grocery Items. Sensors, 25.","DOI":"10.3390\/s25175309"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Out\u00f3n, J.L., Villaverde, I., Herrero, H., Esnaola, U., and Sierra, B. (2019). Innovative mobile manipulator solution for modern flexible manufacturing processes. Sensors, 19.","DOI":"10.3390\/s19245414"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ghodsian, N., Benfriha, K., Olabi, A., Gopinath, V., and Arnou, A. (2023). Mobile manipulators in industry 4.0: A review of developments for industrial applications. Sensors, 23.","DOI":"10.3390\/s23198026"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Out\u00f3n, J.L., Merino, I., Villaverde, I., Ibarguren, A., Herrero, H., Daelman, P., and Sierra, B. (2021). A real application of an autonomous industrial mobile manipulator within industrial context. Electronics, 10.","DOI":"10.3390\/electronics10111276"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bajracharya, M., Borders, J., Cheng, R., Helmick, D., Kaul, L., Kruse, D., Leichty, J., Ma, J., Matl, C., and Michel, F. (2024). Demonstrating mobile manipulation in the wild: A metrics-driven approach. arXiv.","DOI":"10.15607\/RSS.2023.XIX.055"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"68","DOI":"10.3390\/automation5020006","article-title":"Vision-based object manipulation for activities of daily living assistance using assistive robot","volume":"5","author":"Shahria","year":"2024","journal-title":"Automation"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1677","DOI":"10.1007\/s10462-020-09888-5","article-title":"Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: A review","volume":"54","author":"Du","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3846","DOI":"10.1017\/S0263574723001285","article-title":"A review of robotic grasp detection technology","volume":"41","author":"Dong","year":"2023","journal-title":"Robotica"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9103","DOI":"10.1007\/s11042-023-15547-y","article-title":"Robot grasping based on object shape approximation and LightGBM","volume":"83","author":"Lin","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s40430-025-06055-1","article-title":"Real-time 3D vision-based robotic grasping system for low-cost industrial production lines","volume":"48","author":"Cong","year":"2026","journal-title":"J. Braz. Soc. Mech. Sci. Eng."},{"key":"ref_14","unstructured":"Zhao, X., Ding, W., An, Y., Du, Y., Yu, T., Li, M., Tang, M., and Wang, J. (2023). Fast segment anything. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"109968","DOI":"10.1016\/j.compag.2025.109968","article-title":"A review on the recent developments in vision-based apple-harvesting robots for recognizing fruit and picking pose","volume":"231","author":"Zhang","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1007\/s11633-025-1542-8","article-title":"A survey of embodied learning for object-centric robotic manipulation","volume":"22","author":"Zheng","year":"2025","journal-title":"Mach. Intell. Res."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jegham, N., Koh, C.Y., Abdelatti, M., and Hendawi, A. (2024). Yolo evolution: A comprehensive benchmark and architectural review of yolov12, yolo11, and their previous versions. arXiv.","DOI":"10.2139\/ssrn.5175639"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hunt, J., and Tkinter GUI Library (2023). Advanced Guide to Python 3 Programming, Springer International Publishing.","DOI":"10.1007\/978-3-031-40336-1_17"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Essick, J. (2024). Graphical User Interface Using Tkinter. The Python-Based Laboratory: A Hands-On Guide for Scientists and Engineers, Oxford University Press.","DOI":"10.1093\/9780191998478.001.0001"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"David, H., Gorbatov, E., Hanebutte, U.R., Khanna, R., and Le, C. (2010, January 18\u201320). RAPL: Memory power estimation and capping. Proceedings of the 16th ACM\/IEEE International Symposium on Low Power Electronics and Design, Austin, TX, USA.","DOI":"10.1145\/1840845.1840883"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kasichayanula, K., Terpstra, D., Luszczek, P., Tomov, S., Moore, S., and Peterson, G.D. (2012). Power aware computing on GPUs. Proceedings of the 2012 Symposium on Application Accelerators in High Performance Computing, IEEE.","DOI":"10.1109\/SAAHPC.2012.26"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Valladares, S., Toscano, M., Tufi\u00f1o, R., Morillo, P., and Vallejo-Huanga, D. (2021). Performance evaluation of the Nvidia Jetson Nano through a real-time machine learning application. Proceedings of the International Conference on Intelligent Human Systems Integration, Springer.","DOI":"10.1007\/978-3-030-68017-6_51"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1214\/ss\/1009213286","article-title":"Interval Estimation for a Binomial Proportion","volume":"16","author":"Brown","year":"2001","journal-title":"Stat. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Asif, U., Tang, J., and Harrer, S. (2018, January 13\u201319). Graspnet: An efficient convolutional neural network for real-time grasp detection for low-powered devices. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/677"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tadic, V., Toth, A., Vizvari, Z., Klincsik, M., Sari, Z., Sarcevic, P., Sarosi, J., and Biro, I. (2022). Perspectives of realsense and zed depth sensors for robotic vision applications. Machines, 10.","DOI":"10.3390\/machines10030183"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/9\/2860\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T01:17:50Z","timestamp":1777857470000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/9\/2860"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,3]]},"references-count":25,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["s26092860"],"URL":"https:\/\/doi.org\/10.3390\/s26092860","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,3]]}}}