{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T16:26:21Z","timestamp":1776270381990,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,3,18]],"date-time":"2020-03-18T00:00:00Z","timestamp":1584489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Robotics R&amp;D Program Office","award":["No.RGAST1907"],"award-info":[{"award-number":["No.RGAST1907"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This work presents a table cleaning and inspection method using a Human Support Robot (HSR) which can operate in a typical food court setting. The HSR is able to perform a cleanliness inspection and also clean the food litter on the table by implementing a deep learning technique and planner framework. A lightweight Deep Convolutional Neural Network (DCNN) has been proposed to recognize the food litter on top of the table. In addition, the planner framework was proposed to HSR for accomplishing the table cleaning task which generates the cleaning path according to the detection of food litter and then the cleaning action is carried out. The effectiveness of the food litter detection module is verified with the cleanliness inspection task using Toyota HSR, and its detection results are verified with standard quality metrics. The experimental results show that the food litter detection module achieves an average of     96 %     detection accuracy, which is more suitable for deploying the HSR robots for performing the cleanliness inspection and also helps to select the different cleaning modes. Further, the planner part has been tested through the table cleaning tasks. The experimental results show that the planner generated the cleaning path in real time and its generated path is optimal which reduces the cleaning time by grouping based cleaning action for removing the food litters from the table.<\/jats:p>","DOI":"10.3390\/s20061698","type":"journal-article","created":{"date-parts":[[2020,3,19]],"date-time":"2020-03-19T03:54:14Z","timestamp":1584590054000},"page":"1698","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Table Cleaning Task by Human Support Robot Using Deep Learning Technique"],"prefix":"10.3390","volume":"20","author":[{"given":"Jia","family":"Yin","sequence":"first","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"given":"Koppaka Ganesh Sai","family":"Apuroop","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5607-9246","authenticated-orcid":false,"given":"Yokhesh Krishnasamy","family":"Tamilselvam","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Clemson University, Clemson, SC 29631, USA"}]},{"given":"Rajesh Elara","family":"Mohan","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3243-9814","authenticated-orcid":false,"given":"Balakrishnan","family":"Ramalingam","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4804-7540","authenticated-orcid":false,"given":"Anh Vu","family":"Le","sequence":"additional","affiliation":[{"name":"Optoelectronics Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,18]]},"reference":[{"key":"ref_1","unstructured":"(2020, January 06). The 5 Most Unwanted Jobs in Singapore. Available online: https:\/\/sg.finance.yahoo.com\/news\/5-most-unwanted-jobs-singapore-160000715.html."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Le, A., Prabakaran, V., Sivanantham, V., and Mohan, R. (2018). Modified a-star algorithm for efficient coverage path planning in tetris inspired self-reconfigurable robot with integrated laser sensor. Sensors, 18.","DOI":"10.3390\/s18082585"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Le, A.V., Ku, P.C., Than Tun, T., Huu Khanh Nhan, N., Shi, Y., and Mohan, R.E. (2018). Realization energy optimization of complete path planning in differential drive based self-reconfigurable floor cleaning robot. Energies, 12.","DOI":"10.3390\/en12061136"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102959","DOI":"10.1016\/j.autcon.2019.102959","article-title":"Self-reconfigurable fa\u00e7ade-cleaning robot equipped with deep-learning-based crack detection based on convolutional neural networks","volume":"108","author":"Kouzehgar","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8190802","DOI":"10.1155\/2018\/8190802","article-title":"Design of sTetro: A Modular, Reconfigurable, and Autonomous Staircase Cleaning Robot","volume":"2018","author":"Ilyas","year":"2018","journal-title":"J. Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"159402","DOI":"10.1109\/ACCESS.2019.2950675","article-title":"Reconfigurable Pavement Sweeping Robot and Pedestrian Cohabitant Framework by Vision Techniques","volume":"7","author":"Le","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hayat, A.A., Parween, R., Elara, M.R., Parsuraman, K., and Kandasamy, P.S. (2019, January 20\u201324). Panthera: Design of a reconfigurable pavement sweeping robot. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8794268"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1109\/TCE.2018.2859629","article-title":"Deep Learning Based Robot for Automatically Picking Up Garbage on the Grass","volume":"64","author":"Bai","year":"2018","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_9","unstructured":"(2020, January 06). The Partnership for Robotics in Europe, Robotics 2020 Multi-Annual Roadmap For Robotics in Europe Horizon 2020 Call ICT-2016 (ICT-25 & ICT-26)l. Available online: https:\/\/www.eu-robotics.net\/sparc\/upload\/about\/files\/H2020-Robotics-Multi-Annual-Roadmap-ICT-2016.pdf."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yi, J., and Yi, S. (2019, January 24\u201327). Mobile Manipulation for the HSR Intelligent Home Service Robot. Proceedings of the 2019 16th International Conference on Ubiquitous Robots (UR), Jeju, Korea.","DOI":"10.1109\/URAI.2019.8768782"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chalup, S., Niemueller, T., Suthakorn, J., and Williams, M.A. (2019). \u201cLucy, Take the Noodle Box!\u201d: Domestic Object Manipulation Using Movement Primitives and Whole Body Motion. RoboCup 2019: Robot World Cup XXIII, Springer International Publishing.","DOI":"10.1007\/978-3-030-35699-6"},{"key":"ref_12","unstructured":"Park, J.H., and Park, D.R. (2011). Dust Detection Method and Apparatus for Cleaning Robot. (7,920,941), U.S. Patent."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ramalingam, B., Lakshmanan, A.K., Ilyas, M., Le, A.V., and Elara, M.R. (2018). Cascaded Machine-Learning Technique for Debris Classification in Floor-Cleaning Robot Application. Appl. Sci., 8.","DOI":"10.3390\/app8122649"},{"key":"ref_14","unstructured":"Bormann, R., Fischer, J., Arbeiter, G., Weisshardt, F., and Verl, A. (2012, January 21\u201322). A visual dirt detection system for mobile service robots. Proceedings of the ROBOTIK 2012, Munich, Germany."},{"key":"ref_15","unstructured":"Andersen, N.A., Braithwaite, I.D., Blanke, M., and Sorensen, T. (2005, January 12\u201315). Combining a novel computer vision sensor with a cleaning robot to achieve autonomous pig house cleaning. Proceedings of the Decision and Control, 2005 and 2005 European Control Conference, Seville, Spain."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.engappai.2014.11.004","article-title":"Planning robot manipulation to clean planar surfaces","volume":"39","author":"Alenya","year":"2015","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1109\/TASE.2017.2665460","article-title":"Automated Planning for Robotic Cleaning Using Multiple Setups and Oscillatory Tool Motions","volume":"14","author":"Kabir","year":"2017","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_18","unstructured":"Hess, J., Sturm, J., and Burgard, W. (2011, January 13). Learning the State Transition Model to Efficiently Clean Surfaces with Mobile Manipulation Robots. Proceedings of the Workshop on Manipulation under Uncertainty at the IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/3035128","article-title":"Vision-Based Dirt Detection and Adaptive Tiling Scheme for Selective Area Coverage","volume":"2018","author":"Ramalingam","year":"2018","journal-title":"J. Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hess, J., Beinhofer, M., Kuhner, D., Ruchti, P., and Burgard, W. (2013, January 6\u201310). Poisson-driven dirt maps for efficient robot cleaning. Proceedings of the Robotics and Automation (ICRA), Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6630880"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lee, H., and Banerjee, A. (2015, January 6\u20139). Intelligent scheduling and motion control for household vacuum cleaning robot system using simulation based optimization. Proceedings of the Winter Simulation Conference (WSC), Huntington Beach, CA, USA.","DOI":"10.1109\/WSC.2015.7408242"},{"key":"ref_22","unstructured":"Geng, X., and Kang, B.H. (2018). Reinforcement Learning for Mobile Robot Obstacle Avoidance Under Dynamic Environments. PRICAI 2018: Trends in Artificial Intelligence, Springer International Publishing."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kumar, R., Kumar, S., Lal, S., and Chand, P. (2014, January 4\u20135). Object Detection and Recognition for a Pick and Place Robot. Proceedings of the Asia-Pacific World Congress on Computer Science and Engineering, Nadi, Fiji.","DOI":"10.1109\/APWCCSE.2014.7053853"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.autcon.2018.10.009","article-title":"Vision-based integrated mobile robotic system for real-time applications in construction","volume":"96","author":"Asadi","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"94642","DOI":"10.1109\/ACCESS.2019.2928467","article-title":"Graph theory-based approach to accomplish complete coverage path planning tasks for reconfigurable robots","volume":"7","author":"Cheng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fulton, M., Hong, J., Islam, M.J., and Sattar, J. (2018). Robotic Detection of Marine Litter Using Deep Visual Detection Models. arXiv.","DOI":"10.1109\/ICRA.2019.8793975"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Rad, M.S., von Kaenel, A., Droux, A., Ti\u00e8che, F., Ouerhani, N., Ekenel, H.K., and Thiran, J.P. (2017). A Computer Vision System to Localize and Classify Wastes on the Streets. International Conference on Computer Vision Systems, Springer.","DOI":"10.1007\/978-3-319-68345-4_18"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lee, J., Park, G., Moon, Y., Lee, S., and Seo, T. (2019). Robust design of detecting contaminants in fa\u00e7ade cleaning applications. IEEE Access.","DOI":"10.1109\/ACCESS.2019.2962131"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhihong, C., Hebin, Z., Yanbo, W., Binyan, L., and Yu, L. (2017, January 26\u201328). A vision-based robotic grasping system using deep learning for garbage sorting. Proceedings of the 36th Chinese Control Conference (CCC), Da lian, China.","DOI":"10.23919\/ChiCC.2017.8029147"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ravankar, A., Ravankar, A.A., Kobayashi, Y., Hoshino, Y., and Peng, C.C. (2018). Path smoothing techniques in robot navigation: State-of-the-art, current and future challenges. Sensors, 18.","DOI":"10.3390\/s18093170"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, N., Chen, C., and Di Nuovo, A. (2020). A Framework of Hybrid Force\/Motion Skills Learning for Robots. IEEE Trans. Cogn. Dev. Syst., in press.","DOI":"10.1109\/TCDS.2020.2968056"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yamamoto, T., Terada, K., Ochiai, A., Saito, F., Asahara, Y., and Murase, K. (2019). Development of Human Support Robot as the research platform of a domestic mobile manipulator. ROBOMECH J., 6.","DOI":"10.1186\/s40648-019-0132-3"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hidayatullah, A.S., Jati, A.N., and Setianingsih, C. (2017, January 26\u201328). Realization of depth first search algorithm on line maze solver robot. Proceedings of the 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC), Yogyakarta, Indonesia.","DOI":"10.1109\/ICCEREC.2017.8226690"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"583","DOI":"10.4236\/wjet.2019.74042","article-title":"Development of Path Planning Algorithm Using Probabilistic Roadmap Based on Modified Ant Colony Optimization","volume":"7","author":"Raheem","year":"2019","journal-title":"World J. Eng. Technol."},{"key":"ref_35","unstructured":"(2020, March 10). YOLO Machine Learning. Available online: https:\/\/scholar.google.ca\/scholar?hl=en&as_sdt=0,5&q=YOLO+machine+learning#d=gs_qabs&u=%23p%3DWYCdg0zDi5EJ.html."},{"key":"ref_36","unstructured":"Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J.R., Wheeler, R., and Ng, A.Y. (2009, January 12\u201317). ROS: An open-source Robot Operating System. Proceedings of the ICRA Workshop on Open Source Software, Kobe, Japan."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MRA.2011.2181749","article-title":"Moveit![ros topics]","volume":"19","author":"Chitta","year":"2012","journal-title":"IEEE Rob. Autom Mag."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Milinda, H., and Madhusanka, B. (2017, January 5\u20136). Mud and dirt separation method for floor cleaning robot. Proceedings of the Engineering Research Conference (MERCon), Moratuwa, Sri Lankah.","DOI":"10.1109\/MERCon.2017.7980502"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Mittal, G., Yagnik, K.B., Garg, M., and Krishnan, N.C. (2016, January 12\u201316). Spotgarbage: smartphone app to detect garbage using deep learning. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany.","DOI":"10.1145\/2971648.2971731"},{"key":"ref_40","unstructured":"Yang, G.T.M., and Thung, G. (2020, January 06). Classification of Trash for Recyclability Status. CS229 Project Report. Available online: https:\/\/pdfs.semanticscholar.org\/c908\/11082924011c73fea6252f42b01af9076f28.pdf."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Valdenegro-Toro, M. (2016, January 8\u201320). Submerged marine debris detection with autonomous underwater vehicles. Proceedings of the Robotics and Automation for Humanitarian Applications (RAHA), Kerala, India.","DOI":"10.1109\/RAHA.2016.7931907"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. arXiv.","DOI":"10.1109\/CVPR.2018.00474"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/6\/1698\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:07:48Z","timestamp":1760173668000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/6\/1698"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,18]]},"references-count":42,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["s20061698"],"URL":"https:\/\/doi.org\/10.3390\/s20061698","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,18]]}}}