{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T13:30:40Z","timestamp":1780839040904,"version":"3.54.1"},"reference-count":49,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T00:00:00Z","timestamp":1664928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Unmanned Aerial Vehicles (UAVs) or drones presently are enhanced with miniature sensors that can provide information relative to their environment. As such, they can detect changes in temperature, orientation, altitude, geographical location, electromagnetic fluctuations, lighting conditions, and more. Combining this information properly can help produce advanced environmental awareness; thus, the drone can navigate its environment autonomously. Wireless communications can also aid in the creation of drone swarms that, combined with the proper algorithm, can be coordinated towards area coverage for various missions, such as search and rescue. Coverage Path Planning (CPP) is the field that studies how drones, independently or in swarms, can cover an area of interest efficiently. In the current work, a CPP algorithm is proposed for a swarm of drones to detect points of interest and collect information from them. The algorithm\u2019s effectiveness is evaluated under simulation results. A set of characteristics is defined to describe the coverage radius of each drone, the speed of the swarm, and the coverage path followed by it. The results show that, for larger swarm sizes, the missions require less time while more points of interest can be detected within the area. Two coverage paths are examined here\u2014parallel lines and spiral coverage. The results depict that the parallel lines coverage is more time-efficient since the spiral increases the required time by an average of 5% in all cases for the same number of detected points of interest.<\/jats:p>","DOI":"10.3390\/s22197551","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T05:12:21Z","timestamp":1665378741000},"page":"7551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Coverage Path Planning and Point-of-Interest Detection Using Autonomous Drone Swarms"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4386-0353","authenticated-orcid":false,"given":"Konstantinos","family":"Bezas","sequence":"first","affiliation":[{"name":"Department of Informatics and Telecommunications, Campus of Arta, University of Ioannina, 47100 Arta, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9010-3422","authenticated-orcid":false,"given":"Georgios","family":"Tsoumanis","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, Campus of Arta, University of Ioannina, 47100 Arta, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Constantinos T.","family":"Angelis","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, Campus of Arta, University of Ioannina, 47100 Arta, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7279-9710","authenticated-orcid":false,"given":"Konstantinos","family":"Oikonomou","sequence":"additional","affiliation":[{"name":"Department of Informatics, Ionian University, 49100 Corfu, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,5]]},"reference":[{"key":"ref_1","unstructured":"Hambling, D. (2015). Swarm Troopers: How Small Drones Will Conquer the World, Publishing Services Provided by Archangel Ink."},{"key":"ref_2","unstructured":"Yamazaki, F., and Liu, W. (2016, January 22\u201324). Remote sensing technologies for post-earthquake damage assessment: A case study on the 2016 Kumamoto earthquake. Proceedings of the 6th Asia Conference on Earthquake Engineering, Cebu City, Philippines."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kinaneva, D., Hristov, G., Raychev, J., and Zahariev, P. (2019, January 20\u201324). Early forest fire detection using drones and artificial intelligence. Proceedings of the 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.","DOI":"10.23919\/MIPRO.2019.8756696"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Pham, H.X., La, H.M., Feil-Seifer, D., and Deans, M. (2017, January 24\u201328). A distributed control framework for a team of unmanned aerial vehicles for dynamic wildfire tracking. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206579"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hong, I., Kuby, M., and Murray, A. (2017). A deviation flow refueling location model for continuous space: A commercial drone delivery system for urban areas. Advances in Geocomputation, Springer.","DOI":"10.1007\/978-3-319-22786-3_12"},{"key":"ref_6","first-page":"40","article-title":"Drones: Designed for product delivery","volume":"26","author":"Bamburry","year":"2015","journal-title":"Des. Manag. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s10846-012-9778-2","article-title":"UAV path planning for structure inspection in windy environments","volume":"69","author":"Guerrero","year":"2013","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chang, W., Yang, G., Yu, J., Liang, Z., Cheng, L., and Zhou, C. (2017, January 24\u201328). Development of a power line inspection robot with hybrid operation modes. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8202263"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1139\/juvs-2018-0009","article-title":"UAV swarm communication and control architectures: A review","volume":"7","author":"Campion","year":"2018","journal-title":"J. Unmanned Veh. Syst."},{"key":"ref_10","unstructured":"Lomonaco, V., Trotta, A., Ziosi, M., Avila, J.D.D.Y., and D\u00edaz-Rodr\u00edguez, N. (2018). Intelligent drone swarm for search and rescue operations at sea. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107932","DOI":"10.1016\/j.oceaneng.2020.107932","article-title":"Efficient path planning of AUVs for container ship oil spill detection in coastal areas","volume":"217","author":"Kumar","year":"2020","journal-title":"Ocean. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.swevo.2019.01.005","article-title":"Distributed operation of collaborating unmanned aerial vehicles for time-sensitive oil spill mapping","volume":"46","author":"Odonkor","year":"2019","journal-title":"Swarm Evol. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Luo, S., Singh, Y., Yang, H., Bae, J.H., Dietz, J.E., Diao, X., and Min, B.C. (2019, January 24\u201328). Image processing and model-based spill coverage path planning for unmanned surface vehicles. Proceedings of the OCEANS 2019 MTS\/IEEE SEATTLE, Vancouver, BC, Canada.","DOI":"10.23919\/OCEANS40490.2019.8962662"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.jocs.2019.04.009","article-title":"Self-organising swarms of firefighting drones: Harnessing the power of collective intelligence in decentralised multi-robot systems","volume":"34","author":"Innocente","year":"2019","journal-title":"J. Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ausonio, E., Bagnerini, P., and Ghio, M. (2021). Drone Swarms in Fire Suppression Activities: A Conceptual Framework. Drones, 5.","DOI":"10.3390\/drones5010017"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1139\/cjfr-2014-0347","article-title":"A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques","volume":"45","author":"Yuan","year":"2015","journal-title":"Can. J. For. Res."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, X.m., and Li, Y. (2009, January 7\u20138). A survey of autonomous control for UAV. Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence, Shanghai, China.","DOI":"10.1109\/AICI.2009.147"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cicek, C.T., Gultekin, H., Tavli, B., and Yanikomeroglu, H. (2019, January 5\u20137). UAV base station location optimization for next generation wireless networks: Overview and future research directions. Proceedings of the 2019 1st International Conference on Unmanned Vehicle Systems-Oman (UVS), Muscat, Oman.","DOI":"10.1109\/UVS.2019.8658363"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Katsigiannis, P., Misopolinos, L., Liakopoulos, V., Alexandridis, T.K., and Zalidis, G. (2016, January 21\u201324). An autonomous multi-sensor UAV system for reduced-input precision agriculture applications. Proceedings of the 2016 24th Mediterranean Conference on Control and Automation (MED), Athens, Greece.","DOI":"10.1109\/MED.2016.7535938"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhao, B., Chen, X., Zhao, X., Jiang, J., and Wei, J. (2018). Real-time UAV autonomous localization based on smartphone sensors. Sensors, 18.","DOI":"10.3390\/s18124161"},{"key":"ref_21","unstructured":"Doherty, P., and Rudol, P. (2007, January 2\u20136). A UAV search and rescue scenario with human body detection and geolocalization. Proceedings of the Australasian Joint Conference on Artificial Intelligence, Sydney, Australia."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gaszczak, A., Breckon, T.P., and Han, J. (2011, January 23\u201327). Real-time people and vehicle detection from UAV imagery. Proceedings of the Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques, San Francisco, CA, USA.","DOI":"10.1117\/12.876663"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.jocs.2018.09.014","article-title":"Design and simulation of the emergent behavior of small drones swarming for distributed target localization","volume":"29","author":"Alfeo","year":"2018","journal-title":"J. Comput. Sci."},{"key":"ref_24","unstructured":"Jin, N., Ma, R., Lv, Y., Lou, X., and Wei, Q. (2010, January 25\u201327). A novel design of water environment monitoring system based on wsn. Proceedings of the 2010 International Conference on Computer Design and Applications, Qinhuangdao, China."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/MC.2004.1266294","article-title":"Energy-efficient area monitoring for sensor networks","volume":"37","author":"Carle","year":"2004","journal-title":"Computer"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ahmed, A., Ali, J., Raza, A., and Abbas, G. (2006, January 14\u201317). Wired vs wireless deployment support for wireless sensor networks. Proceedings of the TENCON 2006\u20132006 IEEE Region 10 Conference, Hong Kong, China.","DOI":"10.1109\/TENCON.2006.343679"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ab Aziz, N.A.B., Mohemmed, A.W., and Alias, M.Y. (2009, January 26\u201329). A wireless sensor network coverage optimization algorithm based on particle swarm optimization and Voronoi diagram. Proceedings of the 2009 International Conference on Networking, Sensing and Control, Okayama, Japan.","DOI":"10.1109\/ICNSC.2009.4919346"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1109\/TII.2015.2411231","article-title":"Lifetime and energy hole evolution analysis in data-gathering wireless sensor networks","volume":"12","author":"Ren","year":"2015","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yuan, C., Liu, Z., and Zhang, Y. (2017, January 13\u201316). Fire detection using infrared images for UAV-based forest fire surveillance. Proceedings of the 2017 International Conference on Unmanned Aircraft Systems (ICUAS), Miami, FL, USA.","DOI":"10.1109\/ICUAS.2017.7991306"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Popescu, D., Dragana, C., Stoican, F., Ichim, L., and Stamatescu, G. (2018). A collaborative UAV-WSN network for monitoring large areas. Sensors, 18.","DOI":"10.3390\/s18124202"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"C\u00e2mara, D. (2014, January 16\u201319). Cavalry to the rescue: Drones fleet to help rescuers operations over disasters scenarios. Proceedings of the 2014 IEEE Conference on Antenna Measurements &Applications (CAMA), Antibes Juan-les-Pins, France.","DOI":"10.1109\/CAMA.2014.7003421"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1109\/TRO.2008.2007935","article-title":"Decentralized perimeter surveillance using a team of UAVs","volume":"24","author":"Kingston","year":"2008","journal-title":"IEEE Trans. Robot."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yang, F., Wang, P., Zhang, Y., Zheng, L., and Lu, J. (2017, January 27\u201329). Survey of swarm intelligence optimization algorithms. Proceedings of the 2017 IEEE International Conference on Unmanned Systems (ICUS), Beijing, China.","DOI":"10.1109\/ICUS.2017.8278405"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"100369","DOI":"10.1016\/j.cosrev.2021.100369","article-title":"A comprehensive survey on the Multiple Traveling Salesman Problem: Applications, approaches and taxonomy","volume":"40","author":"Cheikhrouhou","year":"2021","journal-title":"Comput. Sci. Rev."},{"key":"ref_35","unstructured":"Khader, A.T., Al-betar, M.A., and Mohammed, A.A. (2013). Artificial Bee Colony Algorithm, Its Variants and Applications: A Survey, Citeseer."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Teodorovi\u0107, D. (2009). Bee colony optimization (BCO). Innovations in Swarm Intelligence, Springer.","DOI":"10.1007\/978-3-642-04225-6_3"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s10462-009-9127-4","article-title":"A survey: Algorithms simulating bee swarm intelligence","volume":"31","author":"Karaboga","year":"2009","journal-title":"Artif. Intell. Rev."},{"key":"ref_38","unstructured":"Gambardella, M., Martinoli, M.B.A., and St\u00fctzle, R.P.T. (2006, January 20\u201321). Ant colony optimization and swarm intelligence. Proceedings of the 5th International Workshop, Sydney, Australia."},{"key":"ref_39","first-page":"975","article-title":"Comparative analysis of ant colony and particle swarm optimization techniques","volume":"5","author":"Selvi","year":"2010","journal-title":"Int. J. Comput. Appl."},{"key":"ref_40","first-page":"1","article-title":"Application Survey on Swarm Intelligence in the Traveling Salesman Problem","volume":"8","author":"Yang","year":"2016","journal-title":"Tech. Autom. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.asoc.2013.10.017","article-title":"An ant colony algorithm for the multi-compartment vehicle routing problem","volume":"15","author":"Reed","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1002\/net.21818","article-title":"Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey","volume":"72","author":"Otto","year":"2018","journal-title":"Networks"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cabreira, T.M., Brisolara, L.B., and Ferreira, P.R. (2019). Survey on coverage path planning with unmanned aerial vehicles. Drones, 3.","DOI":"10.3390\/drones3010004"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Nam, L., Huang, L., Li, X.J., and Xu, J. (2016, January 22\u201324). An approach for coverage path planning for UAVs. Proceedings of the 2016 IEEE 14th international workshop on advanced motion control (AMC), Auckland, New Zealand.","DOI":"10.1109\/AMC.2016.7496385"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1016\/j.mechatronics.2010.10.009","article-title":"Coverage path planning for UAVs based on enhanced exact cellular decomposition method","volume":"21","author":"Li","year":"2011","journal-title":"Mechatronics"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"McGuire, K., De Wagter, C., Tuyls, K., Kappen, H., and de Croon, G.C. (2019). Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment. Sci. Robot., 4.","DOI":"10.1126\/scirobotics.aaw9710"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Xia, Y., Chen, C., Shi, J., Liu, Y., and Li, G. (2022, September 05). Two-Layer Path Planning for Multi-Area Coverage by a Cooperated Ground Vehicle and Drone System. Available online: https:\/\/www.researchgate.net\/publication\/346526962_Two-Layer_Path_Planning_for_Multi-Area_Coverage_by_a_Cooperated_Ground_Vehicle_and_Drone_System.","DOI":"10.1016\/j.eswa.2023.119604"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"102220","DOI":"10.1016\/j.adhoc.2020.102220","article-title":"Impact of drone route geometry on information collection in wireless sensor networks","volume":"106","author":"Skiadopoulos","year":"2020","journal-title":"Ad Hoc Netw."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"108125","DOI":"10.1016\/j.cie.2022.108125","article-title":"Coverage path planning for spraying drones","volume":"168","year":"2022","journal-title":"Comput. Ind. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7551\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:46:58Z","timestamp":1760143618000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7551"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,5]]},"references-count":49,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22197551"],"URL":"https:\/\/doi.org\/10.3390\/s22197551","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,5]]}}}