{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:25:11Z","timestamp":1760145911742,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T00:00:00Z","timestamp":1727049600000},"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>With the development of automation and intelligent technologies, the demand for autonomous mobile robots in the industry has surged to alleviate labor-intensive tasks and mitigate labor shortages. However, conventional industrial mobile robots\u2019 route-tracking algorithms typically rely on passive markers, leading to issues such as inflexibility in changing routes and high deployment costs. To address these challenges, this study proposes a novel approach utilizing active landmarks\u2014battery-powered luminous landmarks that enable robots to recognize and adapt to flexible navigation requirements. However, the reliance on batteries necessitates frequent recharging, prompting the development of an automatic power supply system. This system integrates omnidirectional contact electrodes on mobile robots, allowing to recharge active landmarks without precise positional alignment. Despite these advancements, challenges such as the large size of electrodes and non-adaptive battery charging across landmarks persist, affecting system efficiency. To mitigate these issues, this research focuses on miniaturizing active landmarks and optimizing power distribution among landmarks. The experimental results of this study demonstrated the effectiveness of our automatic power supply method and the high accuracy of landmark detection. Our power distribution calculation method can adaptively manage energy distribution, improving the system\u2019s persistence by nearly three times. This study aims to enhance the practicality and efficiency of mobile robot remote control systems utilizing active landmarks by simplifying installation processes and extending operational durations with adaptive and automatic power supply distribution.<\/jats:p>","DOI":"10.3390\/s24186152","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T08:56:06Z","timestamp":1727168166000},"page":"6152","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Adaptive and Automatic Power Supply Distribution System with Active Landmarks for Autonomous Mobile Robots"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4814-4225","authenticated-orcid":false,"given":"Zhen","family":"Li","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Automation, Nantong University, Nantong 226021, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9477-1550","authenticated-orcid":false,"given":"Yuliang","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Engineering, Kyushu Institute of Technology, Kitakyushu 804-0015, Japan"}]},{"given":"Miaomiao","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Changshu Institute of Technology, Suzhou 215500, China"}]},{"given":"Haonan","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Engineering, Kyushu Institute of Technology, Kitakyushu 804-0015, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7528-8051","authenticated-orcid":false,"given":"Lifeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Engineering, Kyushu Institute of Technology, Kitakyushu 804-0015, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Raj, R., and Kos, A. (2022). A Comprehensive Study of Mobile Robot: History, Developments, Applications, and Future Research Perspectives. Appl. Sci., 12.","DOI":"10.3390\/app12146951"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100651","DOI":"10.1016\/j.cosrev.2024.100651","article-title":"Mobile robot localization: Current challenges and future prospective","volume":"53","author":"Inam","year":"2024","journal-title":"Comput. Sci. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hercik, R., Byrtus, R., Jaros, R., and Koziorek, J. (2022). Implementation of Autonomous Mobile Robot in SmartFactory. Appl. Sci., 12.","DOI":"10.3390\/app12178912"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"106534","DOI":"10.1016\/j.engappai.2023.106534","article-title":"Smart mobile robot fleet management based on hierarchical multi-agent deep Q network towards intelligent manufacturing","volume":"124","author":"Yue","year":"2023","journal-title":"Eng. Appl. Artif. Intel."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102229","DOI":"10.1016\/j.rcim.2021.102229","article-title":"Learning-based object detection and localization for a mobile robot manipulator in SME production","volume":"73","author":"Zhengxue","year":"2022","journal-title":"Robot. Comput. -Integr. Manuf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1007\/s10514-022-10039-8","article-title":"Motion planning and control for mobile robot navigation using machine learning: A survey","volume":"46","author":"Xiao","year":"2022","journal-title":"Auton. Robot"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.autcon.2018.07.003","article-title":"Localisation of a mobile robot for bridge bearing inspection","volume":"94","author":"Peel","year":"2018","journal-title":"Automat Constr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1109\/TMECH.2012.2213263","article-title":"Landmark-Based Particle Localization Algorithm for Mobile Robots with a Fish-Eye Vision System","volume":"18","author":"Han","year":"2013","journal-title":"IEEE-ASME T. Mech."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"127871","DOI":"10.1109\/ACCESS.2022.3226784","article-title":"An open-source low-cost mobile robot system with an RGB-D camera and efficient real-time navigation algorithm","volume":"10","author":"Kim","year":"2022","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, C., Guo, J., Guo, S., and Fu, Q. (2023, January 6\u20139). Study on a Two-layer Path Planning Method of Spherical Multi-robot. Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA), Harbin, China.","DOI":"10.1109\/ICMA57826.2023.10216169"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103799","DOI":"10.1016\/j.robot.2021.103799","article-title":"Enhancing continuous control of mobile robots for end-to-end visual active tracking","volume":"142","author":"Alessandro","year":"2021","journal-title":"Robot Auton. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bavelos, A.C., Kousi, N., Gkournelos, C., Lotsaris, K., Aivaliotis, S., Michalos, G., and Makris, S. (2021). Enabling Flexibility in Manufacturing by Integrating Shopfloor and Process Perception for Mobile Robot Workers. Appl. Sci., 11.","DOI":"10.3390\/app11093985"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/s10458-024-09635-y","article-title":"Landmark-based distributed topological mapping and navigation in GPS-denied urban environments using teams of low-cost robots","volume":"38","author":"Teymouri","year":"2024","journal-title":"Auton. Agent. Multi-Ag."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, X., Sun, Y., Xie, Y., Bin, J., and Xiao, J. (2023). Deep reinforcement learning-aided autonomous navigation with landmark generators. Front. Neurorobotics, 17.","DOI":"10.3389\/fnbot.2023.1200214"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1109\/TASE.2022.3233662","article-title":"Robust Data Association Against Detection Deficiency for Semantic SLAM","volume":"21","author":"Lin","year":"2024","journal-title":"IEEE T. Autom. Sci. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Alhmiedat, T., Marei, A.M., Messoudi, W., Albelwi, S., Bushnag, A., Bassfar, Z., Alnajjar, F., and Elfaki, A.O. (2023). A SLAM-Based Localization and Navigation System for Social Robots: The Pepper Robot Case. Machines, 11.","DOI":"10.3390\/machines11020158"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"122688","DOI":"10.1016\/j.eswa.2023.122688","article-title":"LSMCL: Long-term Static Mapping and Cloning Localization for autonomous robot navigation using 3D LiDAR in dynamic environments","volume":"241","author":"Lee","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/JSEN.2023.3341832","article-title":"An Indoor 2-D LiDAR SLAM and Localization Method Based on Artificial Landmark Assistance","volume":"24","author":"Zeng","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"107530","DOI":"10.1016\/j.ress.2021.107530","article-title":"Machine learning for reliability engineering and safety applications: Review of current status and future opportunities","volume":"211","author":"Zhaoyi","year":"2021","journal-title":"Reliab. Eng. Syst. Safe."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Payette, M., and Abdul-Nour, G. (2023). Machine Learning Applications for Reliability Engineering: A Review. Sustainability, 15.","DOI":"10.3390\/su15076270"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mishu, M.K., Rokonuzzaman, M., Pasupuleti, J., Shakeri, M., Rahman, K.S., Hamid, F.A., Tiong, S.K., and Amin, N. (2020). Prospective Efficient Ambient Energy Harvesting Sources for IoT-Equipped Sensor Applications. Electronics, 9.","DOI":"10.3390\/electronics9091345"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Al-Saadi, M., Al-Greer, M., and Short, M. (2023). Reinforcement Learning-Based Intelligent Control Strategies for Optimal Power Management in Advanced Power Distribution Systems: A Survey. Energies, 16.","DOI":"10.3390\/en16041608"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"108289","DOI":"10.1016\/j.ijepes.2022.108289","article-title":"Transactive energy in power distribution systems: Paving the path towards cyber-physical-social system","volume":"142","author":"Meng","year":"2022","journal-title":"Int. J. Electr. Power Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/6152\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:00:49Z","timestamp":1760112049000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/6152"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,23]]},"references-count":23,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24186152"],"URL":"https:\/\/doi.org\/10.3390\/s24186152","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,9,23]]}}}