{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T05:15:19Z","timestamp":1738300519776,"version":"3.35.0"},"reference-count":32,"publisher":"Cambridge University Press (CUP)","issue":"11","license":[{"start":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T00:00:00Z","timestamp":1730160000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In recent years, dangerous gas leakage events occur frequently. Rapid and accurate location of gas leakage sources by mobile robots is the key to avoid the expansion of disasters. In order to solve the problem of discontinuous gas concentration gradient and sparse gas environment in three-dimensional space, particle filter, and whale swarm optimization algorithm are integrated to locate gas source. Firstly, the <jats:italic>Z<\/jats:italic>-shape search and comb search are used to locate the plume, and then, the particle filter algorithm is combined with the whale optimization method to guide the particle movement, and the random inertial disturbance term is designed to improve the convergence speed and search accuracy of the algorithm. Experimental results in three-dimensional environment show that the proposed information-driven particle filter whale optimization hybrid algorithm effectively guides the robot in localizing gas source within a certain range, significantly enhancing both the efficiency and accuracy of localization compared to other algorithms.<\/jats:p>","DOI":"10.1017\/s0263574724001620","type":"journal-article","created":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T11:14:25Z","timestamp":1730200465000},"page":"3748-3767","source":"Crossref","is-referenced-by-count":0,"title":["A novel hybrid information-based PF-WOA algorithm for gas source localization in 3D space"],"prefix":"10.1017","volume":"42","author":[{"given":"Li","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9843-5391","authenticated-orcid":false,"given":"Ziyu","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Shurui","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2024,10,29]]},"reference":[{"doi-asserted-by":"publisher","key":"S0263574724001620_ref31","DOI":"10.1007\/s10340-020-01204-y"},{"doi-asserted-by":"publisher","key":"S0263574724001620_ref18","DOI":"10.1016\/j.jlp.2023.105003"},{"key":"S0263574724001620_ref23","first-page":"1406","article-title":"An indoor odor source locating method for multi-robotActive olfaction based on improved AEO","volume":"34","author":"Jun","year":"2021","journal-title":"Chinese J. 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