{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T14:50:25Z","timestamp":1785336625049,"version":"3.55.0"},"reference-count":19,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Directorate of Technical Airworthiness and Engineering Support 6 (DTAES-6)","award":["TC292"],"award-info":[{"award-number":["TC292"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a method based on particle swarm optimization (PSO) for optimizing the power settings of unmanned aerial vehicle (UAVs) along a given trajectory in order to minimize fuel consumption and maximize autonomy during surveillance missions. UAVs are widely used in surveillance missions and their autonomy is a key characteristic that contributes to their success. Providing a way to reduce fuel consumption and increase autonomy provides a significant advantage during the mission. The method proposed in this paper included path smoothing techniques in 3D for fixed-wing UAVs based on circular arcs that overfly the waypoints, an essential feature in a surveillance mission. It used the equations of motions and the decomposition of Newton\u2019s equation to compute the fuel consumption based on a given power setting. The proposed method used PSO to compute optimized power settings while respecting the absolute physical constraints, such as the load factor, the lift coefficient, the maximum speed and the maximum amount of fuel onboard. Finally, the method was parallelized on a multicore processor to accelerate the computation and provide fast optimization of the power settings in case the trajectory was changed in flight by the operator. Our results showed that the proposed PSO was able to reduce fuel consumption by up to 25% in the trajectories tested and the parallel implementation provided a speedup of 21.67\u00d7 compared to a sequential implementation on the CPU.<\/jats:p>","DOI":"10.3390\/s24020408","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T07:50:48Z","timestamp":1704873048000},"page":"408","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Minimizing Fuel Consumption for Surveillance Unmanned Aerial Vehicles Using Parallel Particle Swarm Optimization"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6147-4910","authenticated-orcid":false,"given":"Vincent","family":"Roberge","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Royal Military College of Canada, Kingston, ON K7K 7B4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gilles","family":"Labont\u00e9","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Royal Military College of Canada, Kingston, ON K7K 7B4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Tarbouchi","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Royal Military College of Canada, Kingston, ON K7K 7B4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, X., and Chen, X. (2014, January 2\u20133). A Support Vector Method for Modeling Civil Aircraft Fuel Consumption with ROC Optimization. Proceedings of the 2014 Enterprise Systems Conference, Shanghai, China.","DOI":"10.1109\/ES.2014.13"},{"key":"ref_2","unstructured":"Wang, X., and Chen, J. (2020, January 6\u20138). Aircraft Fuel Consumption Prediction Method Based on Just-In-Time Learning and Enhanced Fifitness Adaptive-Differential Evolution-Relevance Vector Machine. Proceedings of the 2020 Chinese Automation Congress (CAC), Shanghai, China."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Meng, N., Wang, M., and Sun, Y. (2019\u20132, January 29). Research on Flight Fuel Prediction based on Historical Data Mining. Proceedings of the IEEE INFOCOM 2019\u2014IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Paris, France. Available online: https:\/\/ieeexplore.ieee.org\/document\/9093769.","DOI":"10.1109\/INFOCOMWKSHPS47286.2019.9093769"},{"key":"ref_4","unstructured":"Liu, J., and Ma, T. (2015, January 8\u201312). A method of aircraft fuel consumption performance evaluation based on RELAX signal separation. Proceedings of the 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Shenyang, China."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"L\u2019Afflitto, A., and Sultan, C. (2010, January 15\u201317). On the fuel and energy consumption optimization problem in aircraft path planning. Proceedings of the 49th IEEE Conference on Decision and Control (CDC), Atlanta, GA, USA.","DOI":"10.1109\/CDC.2010.5717598"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, X.-C., Chen, Y.-C., and Wang, Y.-R. (2022, January 20\u201322). Fuel Consumption Analysis of Distributed Propulsion. Proceedings of the 2022 13th International Conference on Mechanical and Aerospace Engineering (ICMAE), Bratislava, Slovakia.","DOI":"10.1109\/ICMAE56000.2022.9852885"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1108\/00022661211194951","article-title":"Formulas for the fuel of climbing propeller driven airplanes","volume":"84","year":"2012","journal-title":"Aircr. Eng. Aerosp. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ye, B., Wang, Z., Tian, Y., and Wan, L. (2017, January 11\u201314). Aircraft-specific trajectory optimization of continuous descent approach for fuel savings. Proceedings of the 2017 IEEE\/SICE International Symposium on System Integration (SII), Taipei, Taiwan.","DOI":"10.1109\/SII.2017.8279312"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2406","DOI":"10.1109\/TAES.2019.2949384","article-title":"Trajectory Optimization for High-Altitude Long-Endurance UAV Maritime Radar Surveillance","volume":"56","author":"Brown","year":"2020","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1109\/TRO.2005.852260","article-title":"Maneuver-based motion planning for nonlinear systems with symmetries","volume":"21","author":"Frazzoli","year":"2005","journal-title":"IEEE Trans. Robot."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"77499","DOI":"10.1109\/ACCESS.2019.2922203","article-title":"PSO-Based Dynamic UAV Positioning Algorithm for Sensing Information Acquisition in Wireless Sensor Networks","volume":"7","author":"Na","year":"2019","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mahdi, W.H., and Taspiner, N. (2022\u20131, January 30). Overview for Parallel Particle Swarm Optimization Algorithms (PPSO). Proceedings of the 2022 14th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Ploiesti, Romania.","DOI":"10.1109\/ECAI54874.2022.9847459"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Abdullah, E.A., Ahmed Saleh, I., and Al Saif, O.I. (2018, January 9\u201311). Performance Evaluation of Parallel Particle Swarm Optimization for Multicore Environment. Proceedings of the 2018 International Conference on Advanced Science and Engineering (ICOASE), Duhok, Iraq.","DOI":"10.1109\/ICOASE.2018.8548816"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Santos, M., Nogueira, B., Pinheiro, R.G.S., Guimar\u00e3es, A., Lima, A., and Andrade, E. (2021, January 17\u201320). A comparative study of GPU metaheuristics for data clustering. Proceedings of the 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Melbourne, Australia.","DOI":"10.1109\/SMC52423.2021.9658803"},{"key":"ref_15","unstructured":"Anderson, J.D. (2000). Introduction to Flight, McGraw-Hill. [4th ed.]. McGraw-Hill Series in Aeronautical and Aerospace Engineering."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Stengel, R.F. (2004). Flight Dynamics, Princeton University Press.","DOI":"10.1515\/9781400866816"},{"key":"ref_17","unstructured":"Kamm, R.W. (2023, September 25). Mixed Up about Fuel Mixtures. Available online: https:\/\/www.aviationpros.com\/home\/article\/10387634\/mixed-up-about-fuel-mixtures."},{"key":"ref_18","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia."},{"key":"ref_19","first-page":"53","article-title":"How airplanes fly at power-off and full-power on rectilinear trajectories","volume":"7","year":"2020","journal-title":"Adv. Aircr. Spacecr. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/408\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:43:20Z","timestamp":1760103800000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/408"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,9]]},"references-count":19,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24020408"],"URL":"https:\/\/doi.org\/10.3390\/s24020408","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,9]]}}}