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In global path planning, first, a bidirectional search strategy was introduced into to improve the searching efficiency, and an adaptive heuristic function was designed to reduce redundant search nodes. In the meantime, a filtering function for key path nodes and an enhanced jump point optimization method help to remove redundant nodes in the path, reduce turning angles, greatly shorten the path length, and smooth the path using cubic B-spline curves. Furthermore, in local path planning, the combination of key path nodes and the dynamic window approach (DWA) algorithm is utilized to achieve obstacle avoidance in dynamic environments and adjust the heading angle of the section enables seamless locomotion of the robot. Finally, the simulation experiments and physical experiments on the robot were conducted to validate that the proposed improved algorithm significantly improves the speed of path planning while also reducing the length of the planned path and improve the reliability of the algorithm when compared with other algorithms.<\/jats:p>","DOI":"10.1007\/s44196-024-00571-z","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T07:03:07Z","timestamp":1719903787000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["A Multiple Environment Available Path Planning Based on an Improved A* Algorithm"],"prefix":"10.1007","volume":"17","author":[{"given":"Yu","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6747-7864","authenticated-orcid":false,"given":"Qingni","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangyao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,2]]},"reference":[{"key":"571_CR1","doi-asserted-by":"publisher","first-page":"8847863","DOI":"10.1155\/2021\/8847863","volume":"2021","author":"Y Guo","year":"2021","unstructured":"Guo, Y., et al.: Research progress of path planning methods for autonomous underwater vehicle. 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