{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T06:23:27Z","timestamp":1784615007359,"version":"3.55.0"},"reference-count":29,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,21]],"date-time":"2019-09-21T00:00:00Z","timestamp":1569024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EPSRC, CASCADE (Complex Autonomous aircraft Systems Configuration, Analysis and Design Exploratory)","award":["EP\/R009953\/1"],"award-info":[{"award-number":["EP\/R009953\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A team from the University of Bristol have developed a method of operating fixed wing Unmanned Aerial Vehicles (UAVs) at long-range and high-altitude over Volc\u00e1n de Fuego in Guatemala for the purposes of volcanic monitoring and ash-sampling. Conventionally, the mission plans must be carefully designed prior to flight, to cope with altitude gains in excess of 3000 m, reaching 9 km from the ground control station and 4500 m above mean sea level. This means the climb route cannot be modified mid-flight. At these scales, atmospheric conditions change over the course of a flight and so a real-time trajectory planner (RTTP) is desirable, calculating a route on-board the aircraft. This paper presents an RTTP based around a genetic algorithm optimisation running on a Raspberry Pi 3 B+, the first of its kind to be flown on-board a UAV. Four flights are presented, each having calculated a new and valid trajectory on-board, from the ground control station to the summit region of Volca\u0144 de Fuego. The RTTP flights are shown to have approximately equivalent efficiency characteristics to conventionally planned missions. This technology is promising for the future of long-range UAV operations and further development is likely to see significant energy and efficiency savings.<\/jats:p>","DOI":"10.3390\/s19194085","type":"journal-article","created":{"date-parts":[[2019,9,23]],"date-time":"2019-09-23T03:26:32Z","timestamp":1569209192000},"page":"4085","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["On-Board Real-Time Trajectory Planning for Fixed Wing Unmanned Aerial Vehicles in Extreme Environments"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7296-6512","authenticated-orcid":false,"given":"Ben","family":"Schellenberg","sequence":"first","affiliation":[{"name":"Department of Aerospace Engineering, University of Bristol, Bristol BS8 1TR, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7767-452X","authenticated-orcid":false,"given":"Tom","family":"Richardson","sequence":"additional","affiliation":[{"name":"Department of Aerospace Engineering, University of Bristol, Bristol BS8 1TR, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9500-5514","authenticated-orcid":false,"given":"Arthur","family":"Richards","sequence":"additional","affiliation":[{"name":"Department of Aerospace Engineering, University of Bristol, Bristol BS8 1TR, UK"},{"name":"Bristol Robotics Laboratory, University of Bristol, Bristol BS16 1QY, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Clarke","sequence":"additional","affiliation":[{"name":"Department of Aerospace Engineering, University of Bristol, Bristol BS8 1TR, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matt","family":"Watson","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, University of Bristol, Bristol BS8 1RJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,21]]},"reference":[{"key":"ref_1","unstructured":"Protti, M., and Barzan, R. (2007). UAV Autonomy\u2014Which level is desirable?\u2014Which level is acceptable ? Alenia Aeronautica Viewpoint. Platform Innovations and System Integration for Unmanned Air, Land and Sea Vehicles (AVT-SCI Joint Symposium), RTO."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Letheren, B., and Montes, G. (2016, January 5\u201312). Design and Flight Testing of a Bio-Inspired Plume Tracking Algorithm for Unmanned Aerial Vehicles. Proceedings of the 2016 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2016.7500614"},{"key":"ref_3","unstructured":"CAA (2019, June 11). Unmanned Aircraft System Operations in UK Airspace\u2014Guidance. Available online: https:\/\/publicapps.caa.co.uk\/modalapplication.aspx?appid=11&mode=detail&id=415."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1109\/6979.898217","article-title":"A review of conflict detection and resolution modeling methods","volume":"1","author":"Kuchar","year":"2000","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"193","DOI":"10.2514\/2.4231","article-title":"Survey of Numerical Methods for Trajectory Optimization","volume":"21","author":"Betts","year":"1998","journal-title":"J. Guid. Control Dyn."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1017\/S0263574714000289","article-title":"Algorithms for collision-free navigation of mobile robots in complex cluttered environments: A survey","volume":"33","author":"Hoy","year":"2015","journal-title":"Robotica"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"267","DOI":"10.7551\/mitpress\/9123.003.0038","article-title":"Incremental sampling-based algorithms for optimal motion planning","volume":"6","author":"Karaman","year":"2011","journal-title":"Robot. Sci. Syst."},{"key":"ref_8","unstructured":"Diankov, R., and Kuffner, J. (November, January 29). Randomized statistical path planning. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, San Diego, CA, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s10700-008-9035-0","article-title":"Multiple UAVs path planning algorithms: A comparative study","volume":"7","author":"Sathyaraj","year":"2008","journal-title":"Fuzzy Optim. Decis. Mak."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1109\/70.880813","article-title":"New potential functions for mobile robot path planning","volume":"16","author":"Ge","year":"2000","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Patle, B.K., Pandey, A., Parhi, D.R.K., and Jagadeesh, A. (2019). A review: On path planning strategies for navigation of mobile robot. Def. Technol.","DOI":"10.1016\/j.dt.2019.04.011"},{"key":"ref_12","unstructured":"Zhen, Z. (2010, January 26\u201328). UAV path planning method based on ant colony optimization. Proceedings of the 2010 Chinese Control and Decision Conference, Xuzhou, China."},{"key":"ref_13","unstructured":"Karaboga, D. (2005). An Idea Based on Honey Bee Swarm for Numerical Optimization, Erciyes University. Technical Report."},{"key":"ref_14","unstructured":"Davis, L., and Steenstrup, M. (1987). Genetic Algorithms and Simulated Annealing: An Overview, Morgan Kaufman Publishers, Inc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/S0957-4174(98)00055-4","article-title":"Mobile robot path planning and tracking using simulated annealing and fuzzy logic control","volume":"15","year":"1998","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1109\/TII.2012.2198665","article-title":"Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning","volume":"9","author":"Roberge","year":"2013","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TSMCB.2002.804370","article-title":"Evolutionary Algorithm Based Offline\/Online Path Planner for UAV Navigation","volume":"33","author":"Nikolos","year":"2003","journal-title":"IEEE Trans. Syst. Man, Cybern. Part B (Cybern.)"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A Fast and Elitist Multiobjective Genetic Algorithm: NGSA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_19","unstructured":"Mittal, S., and Deb, K. (2007, January 25\u201328). Three-Dimentional Offline Path Planning for UAVs Using Multiobjective Evolutionary Algorithms. Proceedings of the 2007 IEEE Congress on Evolutionary Computation (CEC\u20192007), Singapore."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wise, R., Pongpunwattana, A., and Rysdyk, R. (2008, January 18\u201321). Modular Tactical Autonomous Guidance Using Evolution-based Algorithms. Proceedings of the AIAA Guidance, Navigation and Control Conference and Exhibit, Honolulu, HI, USA.","DOI":"10.2514\/6.2008-6302"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1016\/j.asoc.2017.10.025","article-title":"Online path planning for AUV rendezvous in dynamic cluttered undersea environment using evolutionary algorithms","volume":"70","author":"MahmoudZadeh","year":"2018","journal-title":"Appl. Soft Comput. J."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xue, Y., and Sun, J.Q. (2018). Solving the Path Planning Problem in Mobile Robotics with the Multi-Objective Evolutionary Algorithm. Appl. Sci., 8.","DOI":"10.3390\/app8091425"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Salamat, B., and Tonello, A.M. (2017). Stochastic Trajectory Generation Using Particle Swarm Optimization for Quadrotor Unmanned Aerial Vehicles (UAVs). Aerospace, 4.","DOI":"10.3390\/aerospace4020027"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.ast.2017.08.037","article-title":"Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by Adaptive Grasshopper Optimization Algorithm","volume":"70","author":"Wu","year":"2017","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_25","unstructured":"Montes, G., Letheren, B., Villa, T., and Gonzalez, F. (2014, January 2\u20134). Bio-inspired plume tracking algorithm for UAVs. Proceedings of the Australasian Conference on Robotics and Automation, ACRA, Melbourne, Australia."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Holzbecher, E. (2012). Environmental Modeling, Springer. [2nd ed.].","DOI":"10.1007\/978-3-642-22042-5"},{"key":"ref_27","unstructured":"NASA (2018, November 23). NASA JPL Shuttle Radar Topography Mission, Available online: https:\/\/www2.jpl.nasa.gov\/srtm\/."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1002\/rob.21896","article-title":"Remote sensing and identification of volcanic plumes using fixed-wing UAVs over Volc\u00e1n de Fuego, Guatemala","volume":"36","author":"Schellenberg","year":"2019","journal-title":"J. Field Robot."},{"key":"ref_29","unstructured":"(2019, August 05). Google Earth. Available online: https:\/\/earth.google.com\/web\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4085\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:22:46Z","timestamp":1760188966000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4085"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,21]]},"references-count":29,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["s19194085"],"URL":"https:\/\/doi.org\/10.3390\/s19194085","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,21]]}}}