{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:14:51Z","timestamp":1758586491149,"version":"3.44.0"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T00:00:00Z","timestamp":1758499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Robot. AI"],"abstract":"<jats:p>Automated docking technologies for marine vessels have advanced significantly, yet trailer loading, a critical and routine task for autonomous surface vehicles (ASVs), remains largely underexplored. This paper presents a novel, vision-based framework for autonomous trailer loading that operates without GPS, making it adaptable to dynamic and unstructured environments. The proposed method integrates real-time computer vision with a finite state machine (FSM) control strategy to detect, approach, and align the ASV with the trailer using visual cues such as LED panels and bunk boards. A realistic simulation environment, modeled after real-world conditions and incorporating wave disturbances, was developed to validate the approach and is available<jats:xref><jats:sup>1<\/jats:sup><\/jats:xref>. Experimental results using the WAM-V 16 ASV in Gazebo demonstrated a 100% success rate under calm to medium wave disturbances and a 90% success rate under high wave conditions. These findings highlight the robustness and adaptability of the vision-driven system, offering a promising solution for fully autonomous trailer loading in GPS-denied scenarios.<\/jats:p>","DOI":"10.3389\/frobt.2025.1607676","type":"journal-article","created":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T04:11:56Z","timestamp":1758514316000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Vision driven trailer loading for autonomous surface vehicles in dynamic environments"],"prefix":"10.3389","volume":"12","author":[{"given":"Jianwen","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jalil","family":"Chavez-Galaviz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nina","family":"Mahmoudian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,9,22]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"984","DOI":"10.1109\/iv55156.2024.10588744","article-title":"Atls: automated trailer loading for surface vessels","author":"Abughaida","year":"2024"},{"key":"B2","doi-asserted-by":"publisher","first-page":"2287","DOI":"10.1016\/j.engappai.2013.08.009","article-title":"Automatic ship berthing using artificial neural network trained by consistent teaching data using nonlinear programming method","volume":"26","author":"Ahmed","year":"2013","journal-title":"Eng. 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