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An adaptive strategy regulates the trade-off between exploration (admissible informed sampling) and exploitation (local sampling) based on online rewards from previous samples. The paper demonstrates that the algorithm is asymptotically optimal and has a better convergence rate than state-of-the-art path planners (e.g., Informed-RRT<jats:inline-formula><jats:alternatives><jats:tex-math>$$^*$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msup>\n                    <mml:mrow\/>\n                    <mml:mo>\u2217<\/mml:mo>\n                  <\/mml:msup>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) in several simulated and real-world scenarios. An open-source, ROS-compatible implementation of the algorithm is publicly available.<\/jats:p>","DOI":"10.1007\/s10514-024-10157-5","type":"journal-article","created":{"date-parts":[[2024,4,21]],"date-time":"2024-04-21T02:56:11Z","timestamp":1713668171000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Adaptive hybrid local\u2013global sampling for fast informed sampling-based optimal path planning"],"prefix":"10.1007","volume":"48","author":[{"given":"Marco","family":"Faroni","sequence":"first","affiliation":[]},{"given":"Nicola","family":"Pedrocchi","sequence":"additional","affiliation":[]},{"given":"Manuel","family":"Beschi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,20]]},"reference":[{"key":"10157_CR1","doi-asserted-by":"crossref","unstructured":"Choudhury, S., Gammell, J.D., Barfoot, T.D., Srinivasa, S.S., & Scherer, S. (2016). 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