{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T05:42:37Z","timestamp":1770270157955,"version":"3.49.0"},"reference-count":22,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,15]],"date-time":"2023-10-15T00:00:00Z","timestamp":1697328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key projects of Guangzhou Science and technology plan, Guangdong Province, China","award":["202007040004"],"award-info":[{"award-number":["202007040004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Current methods that use Unmanned Aerial Vehicle (UAV) swarms to inspect roads still have many limitations in practical applications, such as the lack of or difficulty in the route planning, the unbalanced utilization rate of the UAV swarm and the difficulty of the site selection for the distributed droneports. To solve the limitations, firstly, we construct the inspection map and remove the redundant information irrelevant to the road inspection. Secondly, we formulate both the route planning problem and the droneport site selection problem in a unified multi-objective optimization model. Thirdly, we redesign the encoding strategy, the updating rules and the decoding strategy of the particle swarm optimization method to effectively solve both the route planning problem and the droneport site selection problem. Finally, we introduce the comprehensive evaluation indicators to verify the effectiveness of the route planning and the droneport site selection. The experimental results show that (1) with the proposed method, the overlapped part of the optimized inspection routes is less than 7% of the total mileage, and the balanced utilization rate of the UAVs is above 75%; (2) the reuse rate of the distributed droneports is significantly improved after optimization; and (3) the proposed method outperforms the ant colony optimization (ACO) method in all evaluation indicators. To this end, the proposed method can effectively plan the inspection routes, balance the utilization of the UAVs and select the sites for the distributed droneports, which has great significance for a fully autonomous UAV swarm inspection system for road inspection.<\/jats:p>","DOI":"10.3390\/s23208479","type":"journal-article","created":{"date-parts":[[2023,10,15]],"date-time":"2023-10-15T10:47:32Z","timestamp":1697366852000},"page":"8479","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Route Planning Method for UAV Swarm Inspection of Roads Fusing Distributed Droneport Site Selection"],"prefix":"10.3390","volume":"23","author":[{"given":"Yingchun","family":"Zhong","sequence":"first","affiliation":[{"name":"Automation School, Guangdong University of Technology, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenwen","family":"Ye","sequence":"additional","affiliation":[{"name":"Automation School, Guangdong University of Technology, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yizhou","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sichuan Normal University, Chengdu 610101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8661-5695","authenticated-orcid":false,"given":"Jingwen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sichuan Normal University, Chengdu 610101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103475","DOI":"10.1016\/j.autcon.2020.103475","article-title":"An approach for the automated extraction of road surface distress from a UAV-derived point cloud","volume":"122","author":"Zeybek","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_2","unstructured":"Attoh-Okine, N., and Adarkwa, O. (2013). Pavement Condition Surveys\u2013Overview of Current Practices, Delaware Center for Transportation, University of Delaware."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.measurement.2019.01.093","article-title":"Identification of rut and pothole by using multirotor unmanned aerial vehicle (UAV)","volume":"137","author":"Saad","year":"2019","journal-title":"Measurement"},{"key":"ref_4","first-page":"825","article-title":"Energy aware data collection with route planning for 6G enabled UAV communication","volume":"71","author":"Obayya","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lee, M.-T., Chuang, M.-L., Kuo, S.-T., and Chen, Y.-R. (2022). UAV swarm real-time rerouting by edge computing D* lite algorithm. Appl. Sci., 12.","DOI":"10.3390\/app12031056"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2086","DOI":"10.1080\/13658816.2022.2045605","article-title":"Optimizing UAV traffic monitoring routes during rush hours considering spatiotemporal variation of monitoring demand","volume":"36","author":"Wang","year":"2022","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.cja.2020.12.018","article-title":"A newly bio-inspired path planning algorithm for autonomous obstacle avoidance of UAV","volume":"34","author":"Zhou","year":"2021","journal-title":"Chin. J. Aeronaut."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.procs.2022.10.122","article-title":"Payload Assisted Unmanned Aerial Vehicle Structural Health Monitoring (UAVSHM) for Active Damage Detection","volume":"210","author":"Malik","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cho, S.-W., Park, J.-H., Park, H.-J., and Kim, S. (2021). Multi-uav coverage path planning based on hexagonal grid decomposition in maritime search and rescue. Mathematics, 10.","DOI":"10.3390\/math10010083"},{"key":"ref_10","first-page":"1439","article-title":"Route Planning of Unmanned Aerial Vehicles under Recharging and Mission Time Constraints","volume":"6","author":"Phalapanyakoon","year":"2021","journal-title":"Int. J. Math. Eng. Manag. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"17366","DOI":"10.1109\/TITS.2022.3160402","article-title":"Multiregional coverage path planning for multiple energy constrained UAVs","volume":"23","author":"Xie","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"820","DOI":"10.20965\/jaciii.2020.p0820","article-title":"Three-Dimensional Unmanned Aerial Vehicle Route Planning Using Hybrid Differential Evolution","volume":"24","author":"Zhang","year":"2020","journal-title":"J. Adv. Comput. Intell. Intell. Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101888","DOI":"10.1016\/j.jairtraman.2020.101888","article-title":"Towards an objective decision-making framework for regional droneport site selection","volume":"89","author":"Alves","year":"2020","journal-title":"J. Air Transp. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Erkan, T.E., and Elsharida, W.M. (2020). Combining AHP and ROC with GIS for droneport site selection: A case study in Libya. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9050312"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1108\/JEIM-09-2019-0277","article-title":"WASPAS based MULTIMOORA method under IVIF environment for the selection of hub location","volume":"33","author":"Aydin","year":"2020","journal-title":"J. Enterp. Inf. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e00729","DOI":"10.1016\/j.gecco.2019.e00729","article-title":"Searching a site for a civil droneport based on bird ecological conservation: An expert-based selection (Dalian, China)","volume":"20","author":"Zhao","year":"2019","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1080\/20964471.2021.1996866","article-title":"Emergency droneport site selection using global subdivision grids","volume":"6","author":"Han","year":"2022","journal-title":"Big Earth Data"},{"key":"ref_18","first-page":"507","article-title":"Research on droneport site selection based on triangular fuzzy number","volume":"505","author":"Liao","year":"2014","journal-title":"Appl. Mech. Mater."},{"key":"ref_19","first-page":"615","article-title":"Research on partial discharge detecting repetitive-path problem in robot inspection","volume":"30","author":"Li","year":"2020","journal-title":"Chin. High Technol. Lett."},{"key":"ref_20","first-page":"89","article-title":"Particle Swarm Optimization for Solving Problem of k-Shortest Paths via Designated Points","volume":"55","author":"Ma","year":"2019","journal-title":"Comput. Eng. Appl."},{"key":"ref_21","first-page":"1552","article-title":"Performance analysis and parameter selection of PSO algorithms","volume":"42","author":"Wang","year":"2016","journal-title":"Acta Autom. Sin."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.procs.2021.03.013","article-title":"Delivery drone route planning over a battery swapping network","volume":"184","author":"Cokyasar","year":"2021","journal-title":"Procedia Comput. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/20\/8479\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:07:12Z","timestamp":1760130432000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/20\/8479"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,15]]},"references-count":22,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["s23208479"],"URL":"https:\/\/doi.org\/10.3390\/s23208479","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,15]]}}}