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Firstly, to address the problem of the high degree of randomness in the process of random tree expansion, the expansion direction of the random tree growing at the starting point is constrained by the improved artificial potential field method; thus, the random tree grows towards the target point. Secondly, the random tree sampling point grown at the target point is biased to the random number sampling point grown at the starting point. Finally, the path planned by the improved bidirectional RRT* algorithm is optimized by extracting key points. Simulation experiments show that compared with the traditional A*, the traditional RRT, and the traditional bidirectional RRT*, the improved bidirectional RRT* algorithm has a shorter path length, higher path-planning efficiency, and fewer inflection points. The optimized path is segmented using the dynamic window method according to the key points. The path planned by the fusion algorithm in a complex environment is smoother and allows for excellent avoidance of temporary obstacles.<\/jats:p>","DOI":"10.3390\/s23021041","type":"journal-article","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T01:41:20Z","timestamp":1673919680000},"page":"1041","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Improved Bidirectional RRT* Algorithm for Robot Path Planning"],"prefix":"10.3390","volume":"23","author":[{"given":"Peng","family":"Xin","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaomin","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoli","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2264-6238","authenticated-orcid":false,"given":"Yanhui","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhibo","family":"Zhai","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiqing","family":"Ma","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s11633-019-1204-9","article-title":"A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles","volume":"17","author":"Panda","year":"2020","journal-title":"Int. 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