{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T01:45:03Z","timestamp":1782438303550,"version":"3.54.5"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T00:00:00Z","timestamp":1649894400000},"content-version":"vor","delay-in-days":13,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Aiming at the problem that the progressively optimized Rapidly-exploring Random Trees Star (RRT*) algorithm generates a large number of redundant nodes, which causes slow convergence and low search efficiency in high-dimensional and complex environments. In this paper we present Target-biased Informed Trees (TBIT*), an improved RRT* path planning algorithm based on target-biased sampling strategy and heuristic optimization strategy. The algorithm adopts a combined target bias strategy in the search phase of finding the initial path to guide the random tree to grow rapidly toward the target direction, thereby reducing the generation of redundant nodes and improving the search efficiency of the algorithm; after the initial path is searched, heuristic sampling is used to optimize the initial path instead of optimizing the random tree, which can benefit from reducing useless calculations, and improve the convergence capability of the algorithm. The experimental results show that the algorithm proposed in this article changes the randomness of the algorithm to a certain extent, and the search efficiency and convergence capability in complex environments have been significantly improved, indicating that the improved algorithm is feasible and efficient.<\/jats:p>","DOI":"10.1093\/jcde\/qwac025","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T12:11:45Z","timestamp":1646914305000},"page":"755-771","source":"Crossref","is-referenced-by-count":15,"title":["Target-biased informed trees: sampling-based method for optimal motion planning in complex environments"],"prefix":"10.1093","volume":"9","author":[{"given":"Xianpeng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinglu","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunxu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213000, Jiangsu Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,4,14]]},"reference":[{"issue":"2","key":"2022041810215522400_bib1","doi-asserted-by":"crossref","first-page":"299","DOI":"10.7210\/jrsj.11.299","article-title":"Path planning and collision avoidance of a robot manipulator using genetic algorithm","volume":"11","author":"Baba","year":"1993","journal-title":"Journal of the Robotics Society of Japan"},{"issue":"3","key":"2022041810215522400_bib2","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1109\/70.88137","article-title":"The vector field histogram-fast obstacle avoidance for mobile robots","volume":"7","author":"Borenstein","year":"1991","journal-title":"IEEE Transactions on Robotics and Automation"},{"key":"2022041810215522400_bib3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1360\/112012-392","article-title":"Research on fork vehicle AGV path planning based on cubic B-spline curve","author":"Donghai","year":"2022","journal-title":"Computer Measurement and Control"},{"key":"2022041810215522400_bib4","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s10846-014-0172-0","article-title":"Continuous path smoothing for car-like robots using B-Spline curves","volume":"80","author":"Elbanhawi","year":"2015","journal-title":"Journal of Intelligent and Robotic Systems"},{"issue":"5","key":"2022041810215522400_bib5","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1177\/0278364919890396","article-title":"Batch informed trees (BIT*): Informed asymptotically optimal anytime search","volume":"39","author":"Gammell","year":"2020","journal-title":"The International Journal of Robotics Research"},{"issue":"S1","key":"2022041810215522400_bib6","first-page":"89","article-title":"Study on smoothing algorithm of shrub pruning trajectory based on improved cubic B-spline","volume":"52","author":"Gu","year":"2021","journal-title":"Transactions of the Chinese Society of Agricultural Machinery"},{"issue":"7","key":"2022041810215522400_bib7","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1177\/0278364915577958","article-title":"Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions","volume":"34","author":"Janson","year":"2015","journal-title":"The International Journal of Robotics Research"},{"key":"2022041810215522400_bib8","volume-title":"Optimal bidirectional rapidly-exploring random trees, Series\/report no. 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