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First, a three-dimensional environment model is established, and then the kinematics equation of the robot is given. The constant thrust artificial fluid method based on the isochronous interpolation method is proposed, and a novel algorithm of constant thrust fitting is researched through the impulse compensation. The effect of obstacles on original fluid field is quantified by the perturbation matrix, and the streamlines can be regarded as the planned path. Simulation results demonstrate the effectiveness of this method by comparing with A-star algorithm and ant colony algorithm. It is proved that the planned path can avoid all obstacles smoothly and swiftly and reach the destination eventually.<\/jats:p>","DOI":"10.1155\/2020\/8828039","type":"journal-article","created":{"date-parts":[[2020,10,22]],"date-time":"2020-10-22T03:20:09Z","timestamp":1603336809000},"page":"1-13","source":"Crossref","is-referenced-by-count":0,"title":["Fast Path Planning for On-Water Automatic Rescue Intelligent Robot Based on Constant Thrust Artificial Fluid Method"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-3716-8045","authenticated-orcid":true,"given":"Yongqiang","family":"Qi","sequence":"first","affiliation":[{"name":"School of Mathematics, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Ke","sequence":"additional","affiliation":[{"name":"School of Mathematics, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.3390\/rs9020100"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/maes.2013.6516147"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/AERO.2008.4526559"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1002\/acs.2550"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2014.10.018"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/tcst.2014.2338354"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.3390\/s16111778"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-011-0742-z"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1007\/s11370-013-0138-2"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/tsmc.2013.2248146"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2012.2198665"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2007.10.011"},{"issue":"1","key":"13","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"N. 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