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Finally, the performance of FF-RRT* is validated in four simulation environments and compared with the other algorithms. The FF-RRT* shortens 32% of the convergence time in complex maze environment and 25% of the convergence time in simple maze environment compared to F-RRT*. And in a complex maze with a concave cavity obstacle, the average convergence time of Fast-RRT* in this environment is 134% more than the complex maze environment compared to 12% with F-RRT* and 34% with FF-RRT*. The simulation results show that FF-RRT* possesses superior performance compared to the other algorithms, and also fits with a much more complex environment.<\/jats:p>","DOI":"10.1007\/s40747-023-01111-6","type":"journal-article","created":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T08:03:36Z","timestamp":1687853016000},"page":"7249-7267","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["FF-RRT*: a sampling-improved path planning algorithm for mobile robots against concave cavity obstacle"],"prefix":"10.1007","volume":"9","author":[{"given":"Jiping","family":"Cong","sequence":"first","affiliation":[]},{"given":"Jianbo","family":"Hu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5340-5592","authenticated-orcid":false,"given":"Yingyang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zihou","family":"He","sequence":"additional","affiliation":[]},{"given":"Linxiao","family":"Han","sequence":"additional","affiliation":[]},{"given":"Maoyu","family":"Su","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,27]]},"reference":[{"key":"1111_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2019.10.014","author":"S Aggarwal","year":"2019","unstructured":"Aggarwal S, Kumar N (2019) Path planning techniques for unmanned aerial vehicles: a review, solutions, and challenges. 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