{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T21:47:21Z","timestamp":1773956841016,"version":"3.50.1"},"reference-count":37,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Ultrasonic thickness inspection of ship bulkheads poses significant challenges due to confined spaces, dynamic obstacles, and highly variable environments. This paper presents a novel autonomous robotic arm control framework tailored for such conditions, combining enhanced Unscented Kalman Filter (UKF) with a hierarchical Model Predictive Control (MPC) strategy. We introduce a residual-driven adaptive noise covariance UKF (RD-ANC) integrated with a Huber penalty function (HP-UKF), significantly improving robustness against sensor noise and outliers during real-time mapping and estimation. A Three-Layer Energy-Efficient MPC (TLE-MPC) is designed, comprising: a global planner using Differential Dynamic Programming (DDP) for energy budgeting and coarse path generation; a coordination layer using Sequential Quadratic Programming (SQP) for obstacle avoidance and adaptive energy trade-offs; and an execution layer leveraging Explicit MPC (eMPC) for sub-5\u2009ms control law computation. Simulation results show the framework achieves real-time obstacle avoidance, stable path tracking, and up to 15% energy reduction during inspection tasks in semi-structured and unpredictable ship environments. This research offers a robust and scalable method for autonomous robotic inspection and lays the foundation for future multi-arm cooperation and long-duration energy-aware deployments.<\/jats:p>","DOI":"10.1371\/journal.pone.0342222","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T17:36:53Z","timestamp":1773941813000},"page":"e0342222","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Energy-efficient path planning for Robotic Bulkhead Inspection using Residual-Enhanced UKF and Hierarchical MPC"],"prefix":"10.1371","volume":"21","author":[{"given":"Jiexin","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5638-000X","authenticated-orcid":true,"given":"Lei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Runlin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,3,19]]},"reference":[{"key":"pone.0342222.ref001","doi-asserted-by":"crossref","first-page":"159","DOI":"10.31181\/smeor11202414","article-title":"Exploring the role of robotics in maritime technology: Innovations, challenges, and future prospects","volume":"1","author":"SK Sahoo","year":"2024","journal-title":"Spectr Mech Eng Oper Res"},{"key":"pone.0342222.ref002","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.procir.2022.02.165","article-title":"Challenges in human-robot collaborative assembly in shipbuilding and ship maintenance, repair and conversion (SMRC) industry","volume":"106","author":"N Zacharaki","year":"2022","journal-title":"Procedia CIRP"},{"key":"pone.0342222.ref003","article-title":"3D Laser Scanning for Thickness Measurements of Hull Structures","author":"XY Zhang","year":"2021","journal-title":"Int. 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