{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T17:54:53Z","timestamp":1773856493356,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Autonomous Underwater Vehicles (AUVs) face significant challenges in obstacle recognition and path planning due to sonar image noise and uncertain underwater environments. This study proposes a robust target recognition and obstacle avoidance framework integrating forward-looking sonar image processing, probabilistic SLAM, and a fast global path planner. Sonar images are first segmented using Otsu thresholding and refined through K-means clustering to extract obstacle features. These features are then fused with odometry and inertial data using an Expectation-Maximization (EM) based data association module and Particle Filter (PF) for posterior state estimation, enabling accurate SLAM under sonar uncertainty. For navigation, an improved A*-JPS algorithm is applied to achieve time-efficient path planning. Experiments were conducted on the publicly available SeaNet Dataset, which contains diverse acoustic scenes from Northwest Pacific environments. Tests were run on a platform with an Intel i5-10210U CPU and 16 GB RAM, using evaluation metrics including recognition accuraacy, RMSE, absolute trajectory error (ATE), loop closure recall, and path smoothness. Results show that the proposed Otsu-K-means sonar segmentation achieves 99.3% recognition accuracy, outperforming standalone methods by over 25%. The EM-PF-SLAM system achieves an ATE of 0.42 m and a loop closure recall of 95.3%, reducing localization uncertainty by over 40% compared to PF-only baselines. The hybrid A*-JPS planner reduces path planning time to 0.12 s and achieves a smoothness score of 0.85. These findings highlight the method\u2019s suitability for real-time, high-precision AUV operations in complex acoustic environments.<\/jats:p>","DOI":"10.31449\/inf.v50i10.12725","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:12:37Z","timestamp":1773832357000},"source":"Crossref","is-referenced-by-count":0,"title":["Underwater Target Recognition and Path Planning Using Otsu-KMeans Segmentation and EM-PF-SLAM with Enhanced A-JPS Algorithm"],"prefix":"10.31449","volume":"50","author":[{"given":"Gang","family":"Ji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,3,18]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12725\/6621","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12725\/6621","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:12:37Z","timestamp":1773832357000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/12725"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,18]]},"references-count":0,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,3,18]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i10.12725","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,18]]}}}