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In this research, the proposed algorithm generates the cost-benefit spanning tree (CBST) to boost the IPP performance. The CBST is able to generate different tree structures based on different parameters. The proofs show that the theoretical guarantees depend on the tree structures (e.g., minimal spanning tree and shortest path tree). The simulations and experiments demonstrate that the proposed method outperforms the benchmark approaches.<\/jats:p>","DOI":"10.1017\/s0263574725102956","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T09:42:48Z","timestamp":1764754968000},"page":"4402-4428","source":"Crossref","is-referenced-by-count":0,"title":["Informative path planning for unmanned aerial vehicles using cost-benefit spanning tree"],"prefix":"10.1017","volume":"43","author":[{"given":"Wei-Hsiang","family":"Chiu","sequence":"first","affiliation":[{"name":"National Central University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7818-5821","authenticated-orcid":false,"given":"Kuo-Shih","family":"Tseng","sequence":"additional","affiliation":[{"name":"National Central University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2025,12,3]]},"reference":[{"key":"S0263574725102956_ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2024.3370177"},{"key":"S0263574725102956_ref51","unstructured":"[51] Tseng, K.-S. , Learning in human and robot search: Subgoal, submodularity, and sparsity Ph.D. Thesis (University of Minnesota, 2016)."},{"key":"S0263574725102956_ref52","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3387123"},{"key":"S0263574725102956_ref5","doi-asserted-by":"publisher","DOI":"10.1007\/BF01588971"},{"key":"S0263574725102956_ref2","unstructured":"[2] Singh, A. , Krause, A. and Kaiser, W. 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