{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T01:48:55Z","timestamp":1726451335451},"reference-count":47,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,16]],"date-time":"2020-01-16T00:00:00Z","timestamp":1579132800000},"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":"Fleet mission planning for Unmanned Aerial Vehicles (UAVs) is the process of creating flight plans for a specific set of objectives and typically over a time period. Due to the increasing focus on the usage of large UAVs, a key challenge is to conduct mission planning addressing changing weather conditions, collision avoidance, and energy constraints specific to these types of UAVs. This paper presents a declarative approach for solving the complex mission planning resistant to weather uncertainty. The approach has been tested on several examples, analyzing how customer satisfaction is influenced by different values of the mission parameters, such as the fleet size, travel distance, wind direction, and wind speed. Computational experiments show the results that allow assessing alternative strategies of UAV mission planning.<\/jats:p>","DOI":"10.3390\/s20020515","type":"journal-article","created":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T12:39:02Z","timestamp":1579264742000},"page":"515","source":"Crossref","is-referenced-by-count":64,"title":["UAV Mission Planning Resistant to Weather Uncertainty"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-5443-8839","authenticated-orcid":false,"given":"Amila","family":"Thibbotuwawa","sequence":"first","affiliation":[{"name":"Department of Materials and Production, Aalborg University, 9220 Aalborg, Denmark"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5181-2872","authenticated-orcid":false,"given":"Grzegorz","family":"Bocewicz","sequence":"additional","affiliation":[{"name":"Faculty of Electronics and Computer Science, Koszalin University of Technology, 75-453 Koszalin, Poland"}]},{"given":"Grzegorz","family":"Radzki","sequence":"additional","affiliation":[{"name":"Faculty of Electronics and Computer Science, Koszalin University of Technology, 75-453 Koszalin, Poland"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4882-7942","authenticated-orcid":false,"given":"Peter","family":"Nielsen","sequence":"additional","affiliation":[{"name":"Department of Materials and Production, Aalborg University, 9220 Aalborg, Denmark"}]},{"given":"Zbigniew","family":"Banaszak","sequence":"additional","affiliation":[{"name":"Faculty of Electronics and Computer Science, Koszalin University of Technology, 75-453 Koszalin, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bocewicz, G., Nielsen, P., Banaszak, Z., and Thibbotuwawa, A. (2018). Deployment of Battery Swapping Stations for Unmanned Aerial Vehicles Subject to Cyclic Production Flow Constraints. Communications in Computer and Information Science, Springer.","DOI":"10.1007\/978-3-319-99972-2_6"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dorling, K., Heinrichs, J., Messier, G.G., and Magierowski, S. (2016). Vehicle Routing Problems for Drone Delivery. IEEE Trans. Syst. Man Cybern. Syst., 70\u201385.","DOI":"10.1109\/TSMC.2016.2582745"},{"key":"ref_3","unstructured":"Thibbotuwawa, A. (2020). Unmanned Aerial Vehicle Fleet Mission Planning Subject to Changing Weather Conditions. [Ph.D. Thesis, Og Naturvidenskabelige Fakultet, Aalborg Universitet]. in print."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Thibbotuwawa, A., Bocewicz, G., Zbigniew, B., and Nielsen, P. (2019). A Solution Approach for UAV Fleet Mission Planning in Changing Weather Conditions. Appl. Sci., 9.","DOI":"10.3390\/app9193972"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sung, I., and Nielsen, P. (2019). Zoning a Service Area of Unmanned Aerial Vehicles for Package Delivery Services. J. Intell. Robot. Syst.","DOI":"10.1007\/s10846-019-01045-7"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nielsen, L.D., Sung, I., and Nielsen, P. (2019). Convex decomposition for a coverage path planning for autonomous vehicles: Interior extension of edges. Sensors, 19.","DOI":"10.3390\/s19194165"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cor.2019.01.001","article-title":"A vehicle routing problem arising in unmanned aerial monitoring","volume":"105","author":"Zhen","year":"2019","journal-title":"Comput. Oper. Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Herrera-Viedma, E., Vale, Z., Nielsen, P., Martin Del Rey, A. (2020). UAV Mission Planning Subject to Weather Forecast Constraints. Advances in Intelligent Systems and Computing, Springer.","DOI":"10.1007\/978-3-030-23946-6"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1057\/palgrave.jors.2601867","article-title":"Devising a quick-running heuristic for an unmanned aerial vehicle (UAV) routing system","volume":"56","author":"Kinney","year":"2005","journal-title":"J. Oper. Res. Soc."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Thibbotuwawa, A., Nielsen, P., Zbigniew, B., and Bocewicz, G. (2020). UAVs Fleet Mission Planning Subject to Weather Fore-Cast and Energy Consumption Constraints, Springer International Publishing.","DOI":"10.1007\/978-3-030-13273-6_11"},{"key":"ref_11","unstructured":"Tseng, C.M., Chau, C.K., Elbassioni, K., and Khonji, M. (2017). Autonomous Recharging and Flight Mission Planning for Battery-operated Autonomous Drones. arXiv."},{"key":"ref_12","first-page":"125","article-title":"Cyclic routing of unmanned aerial vehicles","volume":"Volume 9676","author":"Drucker","year":"2016","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5","DOI":"10.35784\/acs-2019-17","article-title":"UAVs flight routes optimization in changing weather conditions\u2014Constraint programming approach","volume":"15","author":"Radzki","year":"2019","journal-title":"Appl. Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1016\/j.ifacol.2019.11.231","article-title":"Planning deliveries with UAV routing under weather forecast and energy consumption constraints","volume":"52","author":"Thibbotuwawa","year":"2019","journal-title":"IFAC-PapersOnLine"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Thibbotuwawa, A., Nielsen, P., Zbigniew, B., and Bocewicz, G. (2019). Energy Consumption in Unmanned Aerial Vehicles: A Review of Energy Consumption Models and Their Relation to the UAV Routing. Advances in Intelligent Systems and Computing, Springer.","DOI":"10.1007\/978-3-319-99996-8_16"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1016\/j.apm.2013.07.002","article-title":"A multi-objective approach for unmanned aerial vehicle routing problem with soft time windows constraints","volume":"38","author":"Guerriero","year":"2014","journal-title":"Appl. Math. Model."},{"key":"ref_17","unstructured":"Sundar, K., Venkatachalam, S., and Rathinam, S. (2016). An Exact Algorithm for a Fuel-Constrained Autonomous Vehicle Path Planning Problem. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s10846-009-9383-1","article-title":"A survey of motion planning algorithms from the perspective of autonomous UAV guidance","volume":"57","author":"Goerzen","year":"2010","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1007\/s11590-016-1035-3","article-title":"The Vehicle Routing Problem with Drones: Several worst-case results","volume":"11","author":"Wang","year":"2017","journal-title":"Optim. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"LaValle, S.M. (2006). Planning Algorithms, Cambridge University Press. Available online: http:\/\/planning.cs.uiuc.edu.","DOI":"10.1017\/CBO9780511546877"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.cor.2017.04.011","article-title":"A multi-objective green UAV routing problem","volume":"88","author":"Coelho","year":"2017","journal-title":"Comput. Oper. Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Valavanis, K., and Vachtsevanos, G. (2015). UAV Routing and Coordination in Stochastic, Dynamic Environments. Handbook of Unmanned Aerial Vehicles, Springer.","DOI":"10.1007\/978-90-481-9707-1"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Adbelhafiz, M., Mostafa, A., and Girard, A. (2010, January 2\u20135). Vehicle Routing Problem Instances: Application to Multi-UAV Mission Planning. Proceedings of the AIAA Guidance, Navigation, and Control Conference, Toronto, ON, Canada.","DOI":"10.2514\/6.2010-8435"},{"key":"ref_24","unstructured":"Esposito, F., Ra\u015b, Z.W., Malerba, D., and Semeraro, G. (2006). Genetic Algorithm Based Approach for Multi-UAV Cooperative Reconnaissance Mission Planning Problem BT\u2014Foundations of Intelligent Systems, Springer."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Borzemski, L., Grzech, A., \u015awikatek, J., and Wilimowska, Z. (2016). Concept of Indoor 3D-Route UAV Scheduling System. Information Systems Architecture and Technology, Proceedings of 36th International Conference on Information Systems Architecture and Technology\u2014ISAT 2015\u2014Part I, Springer.","DOI":"10.1007\/978-3-319-28555-9"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Thibbotuwawa, A., Nielsen, P., Zbigniew, B., and Bocewicz, G. (2019). Factors Affecting Energy Consumption of Unmanned Aerial Vehicles: An Analysis of How Energy Consumption Changes in Relation to UAV Routing. Advances in Intelligent Systems and Computing, Springer.","DOI":"10.1007\/978-3-319-99996-8_21"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1016\/j.promfg.2015.07.290","article-title":"Safety and Security Management with Unmanned Aerial Vehicle in Oil and Gas Industry","volume":"3","author":"Cho","year":"2015","journal-title":"Procedia Manuf."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bocewicz, G., Nielsen, P., Banaszak, Z., and Thibbotuwawa, A. (2019). Routing and Scheduling of Unmanned Aerial Vehicles Subject to Cyclic Production Flow Constraints. Advances in Intelligent Systems and Computing, Springer.","DOI":"10.1007\/978-3-319-99608-0_9"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.cie.2018.05.013","article-title":"Persistent UAV delivery logistics: MILP formulation and efficient heuristic","volume":"120","author":"Song","year":"2018","journal-title":"Comput. Ind. Eng."},{"key":"ref_30","unstructured":"Alami, R., Chatila, R., and Asama, H. (2007). Multiple UAV Cooperative Searching Operation Using Polygon Area Decomposition and Efficient Coverage Algorithms BT\u2014Distributed Autonomous Robotic Systems 6, Springer."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"235","DOI":"10.5772\/56286","article-title":"Employing multiple unmanned aerial vehicles for co-operative path planning","volume":"10","author":"Habib","year":"2013","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1109\/TASE.2013.2279544","article-title":"Algorithms for routing an unmanned aerial vehicle in the presence of refueling depots","volume":"11","author":"Sundar","year":"2014","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rubio, J.C., and Kragelund, S. (2003, January 2\u201316). The trans-pacific crossing: Long range adaptive path planning for UAVs through variable wind fields. Proceedings of the Digital Avionics Systems Conference, Indianapolis, IN, USA.","DOI":"10.1109\/DASC.2003.1245898"},{"key":"ref_34","unstructured":"Nguyen, T., and Tsz-Chiu, A. (2017, January 8\u201312). Extending the Range of Delivery Drones by Exploratory Learning of Energy Models. Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, S\u00e3o Paulo, Brazil."},{"key":"ref_35","unstructured":"Xu, K.X.K., Hong, X.H.X., and Gerla, M.G.M. (2001, January 28\u201331). Landmark routing in large wireless battlefield networks using UAVs. Proceedings of the MILCOM 2001 Communications for Network-Centric Operations: Creating the Information Force (Cat No01CH37277), McLean, VA, USA."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"260","DOI":"10.3182\/20130902-5-DE-2040.00101","article-title":"Cooperative guidance of UAVs for area exploration with final target allocation","volume":"46","author":"Gorecki","year":"2013","journal-title":"IFAC Proc. Vol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2501","DOI":"10.1007\/s11771-014-2205-z","article-title":"An optimization model of UAV route planning for road segment surveillance","volume":"21","author":"Liu","year":"2014","journal-title":"J. Cent. South Univ."},{"key":"ref_38","unstructured":"Geyer, C., Dey, D., and Singh, S. (2009). Prototype Sense-and-Avoid Sstemy for UAVs, Robotics Institute, Carnegie Mellon University. Tech. Report, CMU-RI-TR-09-09."},{"key":"ref_39","unstructured":"Geyer, C., Singh, S., and Chamberlain, L. (2008). Avoiding Collisions between Aircraft: State of the Art and Requirements for UAVs Operating in Civilian Airspace, Robotics Institute, Carnegie Mellon University. Tech. Report, CMU-RI-TR-08-03."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"15983","DOI":"10.1016\/j.ifacol.2017.08.1908","article-title":"Distributed Path Planning for Controlling a Fleet of UAVs: Application to a Team of Quadrotors","volume":"50","author":"Belkadi","year":"2017","journal-title":"IFAC-PapersOnLine"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhan, W., Wang, W., Chen, N., and Wang, C. (2014). Efficient UAV path planning with multiconstraints in a 3D large battlefield environment. Math. Probl. Eng.","DOI":"10.1155\/2014\/597092"},{"key":"ref_42","unstructured":"AIRBUS (2020, January 13). Airbus\u2019 Skyways Drone Trials World\u2019s First Shore-to-Ship Deliveries. Available online: https:\/\/www.airbus.com\/newsroom\/press-releases\/en\/2019\/03\/airbus-skyways-drone-trials-worlds-first-shoretoship-deliveries.html."},{"key":"ref_43","unstructured":"(2020, January 13). UAV R&D. Available online: https:\/\/www.kari.re.kr\/eng\/sub03_01_01.do."},{"key":"ref_44","unstructured":"F\u00fcgenschuh, A., and M\u00fcllenstedt, D. (2015). Flight Planning for Unmanned Aerial Vehicles. Professur f\u00fcr Angewandte Mathematik, Helmut-Schmidt-Universit\u00e4t. Angewandte Mathematik und Optimierung Schriftenreihe\/Applied Mathematics and Optimization Series, Universit\u00e4t der Bundeswehr Hamburg."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rucco, A., Aguiar, A.P., Pereira, F.L., and Sousa, J.B.D. (2016). A Predictive Path-Following Approach for Fixed-Wing Unmanned Aerial Vehicles in Presence of Wind Disturbances. Advances in Intelligent Systems and Computing, Springer.","DOI":"10.1007\/978-3-319-27146-0_48"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Luo, H., Liang, Z., Zhu, M., Hu, X., and Wang, G. (2018). Integrated optimization of un-manned aerial vehicle task allocation and path planning under steady wind. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0194690"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kim, S.J., Lim, G.J., and Cho, J. (2018). Drone Flight Scheduling Under Uncertainty on Bat-tery Duration and Air Temperature. Comput. Ind. Eng.","DOI":"10.1016\/j.cie.2018.02.005"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/2\/515\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T16:37:51Z","timestamp":1719160671000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/2\/515"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,16]]},"references-count":47,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20020515"],"URL":"https:\/\/doi.org\/10.3390\/s20020515","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,16]]}}}