{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T16:08:23Z","timestamp":1782403703799,"version":"3.54.5"},"reference-count":26,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T00:00:00Z","timestamp":1732060800000},"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":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In scenarios such as environmental data collection and traffic monitoring, timely responses to real-time situations are facilitated by persistently accessing nodes with revisiting constraints using unmanned aerial vehicles (UAVs). However, imbalanced task allocation may pose risks to the safety of UAVs and potentially lead to failures in monitoring tasks. For instance, continuous visits to nodes without replenishment may damage UAV batteries, while delays in recharging could result in missing task deadlines, ultimately causing task failures. Therefore, this study investigates the problem of achieving balanced multi-UAV path planning for persistent monitoring tasks, which has not been previously researched according to the authors\u2019 knowledge. The main contribution of this study is the proposal of two novel indicators to assist in balancing task allocation regarding multi-UAV path planning for persistent monitoring. One of the indicators is namely the waiting factor, which reflects the urgency of a task node waiting to be accessed, and the other is the difficulty level which is introduced to measure the difficulty of tasks undertaken by a UAV. By minimizing differences in difficulty level among UAVs, we can ensure equilibrium in task allocation. For a single UAV, the ant colony initialized genetic algorithm (ACIGA) has been proposed to plan its path and obtain its difficulty level. For multiple UAVs, the K-means clustering algorithm has been improved based on difficulty levels to achieve balanced task allocation. Simulation experiments demonstrated that the difficulty level could effectively reflect the difficulty of tasks and that the proposed algorithms could enable UAVs to achieve balanced task allocation.<\/jats:p>","DOI":"10.1017\/s0263574724001899","type":"journal-article","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T09:20:41Z","timestamp":1732094441000},"page":"332-349","source":"Crossref","is-referenced-by-count":5,"title":["Balanced Multi-UAV path planning for persistent monitoring"],"prefix":"10.1017","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4864-4006","authenticated-orcid":false,"given":"Xinru","family":"Zhan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1094-4905","authenticated-orcid":false,"given":"Yang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2024,11,20]]},"reference":[{"key":"S0263574724001899_ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102706"},{"key":"S0263574724001899_ref16","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3056381"},{"key":"S0263574724001899_ref17","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/3516149"},{"key":"S0263574724001899_ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2023.103670"},{"key":"S0263574724001899_ref14","doi-asserted-by":"crossref","unstructured":"[14] Shu, Y. , Chen, Y. , Hu, M. , Wu, H. and Zhao, X. , \u201cUAV path planning based on simultaneous optimization of monitoring frequency and security,\u201d 2022 34th Chinese Control and Decision Conference (CCDC),\u00a0Piscataway, NJ, IEEE (2022) pp. 3808\u20133814.","DOI":"10.1109\/CCDC55256.2022.10033575"},{"key":"S0263574724001899_ref1","doi-asserted-by":"crossref","unstructured":"[1] Messaoudi, K. , Oubbati, O. S. , Rachedi, A. and Bendouma, T. , \u201cUAV-UGV-based system for AOI minimization in IOT networks,\u201d ICC 2023-IEEE International Conference on Communications, Rome, Italy, IEEE (2023), 2023-May, pp. 4743\u20134748.","DOI":"10.1109\/ICC45041.2023.10279435"},{"key":"S0263574724001899_ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2020.3032171"},{"key":"S0263574724001899_ref25","doi-asserted-by":"publisher","DOI":"10.1111\/poms.13252"},{"key":"S0263574724001899_ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2022.3181512"},{"key":"S0263574724001899_ref20","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12183842"},{"key":"S0263574724001899_ref13","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3146938"},{"key":"S0263574724001899_ref23","doi-asserted-by":"crossref","unstructured":"[23] Yu, X. , Jin, S. , Shi, D. , Li, L. , Kang, Y. and Zou, J. , \u201cBalanced multi-region coverage path planning for unmanned aerial vehicles,\u201d IEEE International Conference on Systems, Man, and Cybernetics (SMC), Piscataway, NJ, IEEE (2020) pp. 3499\u20133506.","DOI":"10.1109\/SMC42975.2020.9283426"},{"key":"S0263574724001899_ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2020.3038156"},{"key":"S0263574724001899_ref3","doi-asserted-by":"crossref","unstructured":"[3] Bailon-Ruiz, R. and Lacroix, S. , \u201cWildfire remote sensing with UAVs: A review from the autonomy point of view,\u201d 2020 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE (2020) pp. 412\u2013420.","DOI":"10.1109\/ICUAS48674.2020.9213986"},{"key":"S0263574724001899_ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2022.3210784"},{"key":"S0263574724001899_ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2022.104244"},{"key":"S0263574724001899_ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2024.3502497"},{"key":"S0263574724001899_ref19","doi-asserted-by":"crossref","unstructured":"[19] Lee, S. , \u201cA multi-robot balanced coverage path planning strategy for patrol missions,\u201d 2021 21st International Conference on Control, Automation and Systems (ICCAS), Jeju, Korea, IEEE (2021) pp. 1567\u20131569.","DOI":"10.23919\/ICCAS52745.2021.9649836"},{"key":"S0263574724001899_ref22","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2019.2963697"},{"key":"S0263574724001899_ref26","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574722001680"},{"key":"S0263574724001899_ref2","first-page":"1","article-title":"Power line-guided automatic electric transmission line inspection system","volume":"71","author":"Xu","year":"2022","journal-title":"IEEE Trans. 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