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Existing methods require extensive real-world training data and well-focused images to achieve robust and accurate localization. However, both requirements are difficult to meet under variable and unpredictable conditions. This paper proposes a 2-stage vision-based localization approach. Firstly, the image synthesis technique is used to reduce the cost of real-world data collection. A task-oriented parameterization protocol (TOPP) is proposed to optimize the quality of the synthetic images. Secondly, an autofocus and servoing strategy is proposed. A hybrid detector is employed to enhance sharpness assessment performance, while a visual servoing method based on single exponential smoothing (SES) is developed to enhance stability and efficiency during the search process. Experiments were conducted to evaluate image synthesis efficiency, detection accuracy, and servoing performance. The proposed method achieved 99% detection accuracy on the real-world port images, and guided the robot to the optimal imaging position within 16 s, outperforming comparable approaches. These results highlight its potential for robust automated charging in real-world scenarios.<\/jats:p>","DOI":"10.1017\/s0263574725102038","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T07:45:26Z","timestamp":1754293526000},"page":"2992-3010","source":"Crossref","is-referenced-by-count":1,"title":["A 2-stage vision-based localization methodology for efficient automatic charging of electric vehicles in uncertain environments"],"prefix":"10.1017","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3166-1893","authenticated-orcid":false,"given":"Qi","family":"Chen","sequence":"first","affiliation":[{"name":"SHIEN-MING WU School of Intelligent Engineering"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Wu","sequence":"additional","affiliation":[{"name":"SHIEN-MING WU School of Intelligent Engineering"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Zhong","sequence":"additional","affiliation":[{"name":"SHIEN-MING WU School of Intelligent Engineering"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihua","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6551-0325","authenticated-orcid":false,"given":"Mingfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Brunel University London"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2025,8,4]]},"reference":[{"key":"S0263574725102038_ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02857-7"},{"key":"S0263574725102038_ref29","doi-asserted-by":"crossref","unstructured":"[29] Prakash, A. , Boochoon, S. , Brophy, M. , Acuna, D. , Cameracci, E. , State, G. , Shapira, O. and Birchfield, S. , \u201cStructured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data,\u201d In: International Conference on Robotics and Automation (ICRA) (2019) pp. 7249\u20137255.","DOI":"10.1109\/ICRA.2019.8794443"},{"key":"S0263574725102038_ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01228-7"},{"key":"S0263574725102038_ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-12191-w"},{"key":"S0263574725102038_ref3","doi-asserted-by":"crossref","unstructured":"[3] Lv, X. , Chen, G. , Hu, H. and Lou, Y. , \u201cA Robotic Charging Scheme for Electric Vehicles Based on Monocular Vision and Force Perception,\u201d In: IEEE International Conference on Robotics and Biomimetics (ROBIO) (2019) pp. 2958\u20132963.","DOI":"10.1109\/ROBIO49542.2019.8961689"},{"key":"S0263574725102038_ref1","doi-asserted-by":"crossref","unstructured":"[1] Ismail, A. , Mehri, M. , Sahbani, A. and Amara, N. 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