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King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Abstract<\/jats:title>\n                    <jats:p>To address the global path planning challenge for Unmanned Surface Vehicles in complex maritime environments characterized by dense islands and narrow waterways, this paper proposes a Hybrid Multi-Strategy Adaptive RRT* algorithm. The method combines a dynamic region-based sampling strategy with an improved artificial potential field based dynamic extension strategy, which introduces random-node attraction, dynamic repulsion adjustments, and additional repulsive forces. Additionally, a hierarchical side-retreat escape mechanism is applied to enhance obstacle avoidance and search efficiency in complex environments. The algorithm also incorporates heading-angle constraints and adaptive step-size adjustment to ensure the path complies with USV kinematic properties. Furthermore, an improved NSGA-II algorithm is proposed to perform multi-objective optimization of path length, smoothness, and safety, and B-spline interpolation is used to generate continuous and executable paths. Simulation results show that, compared with the standard RRT* algorithm, the proposed HMA-RRT* algorithm achieves average reductions of 7.85% in path length, 66.96% in node count, 48.73% in computation time, and 25.7% in mean turning angle across four representative complex maritime environments. These improvements significantly enhance search efficiency, path smoothness, and planning feasibility, thereby providing a reliable and efficient path-planning solution for autonomous USV navigation in complex maritime conditions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1007\/s44443-025-00393-9","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T12:46:04Z","timestamp":1765284364000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["HMA-RRT*: A hybrid multi-strategy adaptive RRT* algorithm for USV path planning in complex maritime environments"],"prefix":"10.1007","volume":"38","author":[{"given":"Zhenpeng","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4952-8079","authenticated-orcid":false,"given":"Qingquan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ende","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiyun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,9]]},"reference":[{"key":"393_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2023.113965","volume":"273","author":"X Bai","year":"2023","unstructured":"Bai X, Li B, Xu X, Xiao Y (2023) USV path planning algorithm based on plant growth. 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