{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T22:03:11Z","timestamp":1761948191551,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2016,10,10]],"date-time":"2016-10-10T00:00:00Z","timestamp":1476057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Route planning is a key technology for an unmanned aerial vehicle (UAV) to fly reliably and safely in the presence of a threat environment. Existing route planning methods are mainly based on the simulation scene, whereas approaches based on the virtual globe platform have rarely been reported. In this paper, a new planning space for the virtual globe and the planner is proposed and a common threat model is constructed for threats including a no-fly zone, hazardous weather, radar coverage area, missile killing zone and dynamic threats. Additionally, an improved ant colony optimization (ACO) algorithm is developed to enhance route planning efficiency and terrain masking ability. Our route planning methods are optimized on the virtual globe platform for practicability. A route planning system and six types of planners were developed and implemented on the virtual globe platform. Finally, our evaluation results demonstrate that our optimum planner has better performance in terms of fuel consumption, terrain masking, and risk avoidance. Experiments also demonstrate that the method and system described in this paper can be used to perform global route planning and mission operations.<\/jats:p>","DOI":"10.3390\/ijgi5100184","type":"journal-article","created":{"date-parts":[[2016,10,10]],"date-time":"2016-10-10T10:35:19Z","timestamp":1476095719000},"page":"184","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Unmanned Aerial Vehicle Route Planning in the Presence of a Threat Environment Based on a Virtual Globe Platform"],"prefix":"10.3390","volume":"5","author":[{"given":"Ming","family":"Zhang","sequence":"first","affiliation":[{"name":"Shenzhen Graduate School, Peking University, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Su","sequence":"additional","affiliation":[{"name":"Shenzhen Graduate School, Peking University, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingyuan","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuesheng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Shenzhen Graduate School, Peking University, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,10,10]]},"reference":[{"key":"ref_1","unstructured":"Kimon, P.V., and George, J.V. (2015). Handbook of Unmanned Aerial Vehicles, Springer."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Neto, A.A., Macharet, D.G., and Campos, M.F.M. (2010, January 18\u201322). Feasible RRT-based path planning using seventh order B\u00e9zier curves. Proceedings of the 2010 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan.","DOI":"10.1109\/IROS.2010.5649145"},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1109\/TRO.2007.898976","article-title":"Vector field path following for miniature air vehicles","volume":"23","author":"Nelson","year":"2007","journal-title":"IEEE Trans. Robot."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1109\/TAES.2013.6494384","article-title":"UAV path planning with Tangent-plus-Lyapunov vector field guidance and obstacle avoidance","volume":"49","author":"Chen","year":"2013","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_6","unstructured":"Lee, J., Pippin, C., and Balch, T. (2008, January 22\u201326). Cost based planning with RRT in outdoor environments. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS 2008), Nice, France."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/JAS.2015.7081657","article-title":"UAV online path planning algorithm in a low altitude dangerous environment","volume":"2","author":"Wen","year":"2015","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wen, N., Zhao, L., Su, X., and Ma, P. (2015). Online UAV path planning in uncertain and hostile environments. Int. J. Mach. Learn. Cyber., 1\u201319.","DOI":"10.1007\/s13042-015-0339-4"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/TAES.2013.6404117","article-title":"Path planning for UAVs for maximum information collection","volume":"49","author":"Ergezer","year":"2013","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/TRO.2005.844684","article-title":"Evolutionary route planner for unmanned air vehicles","volume":"21","author":"Zheng","year":"2005","journal-title":"IEEE Trans. Robot."},{"key":"ref_11","unstructured":"Mittal, S., and Deb, K. (2007, January 25\u201328). Three-dimensional offline path planning for UAVs using multiobjective evolutionary algorithms. Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2007), Singapore."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1109\/TRO.2015.2459812","article-title":"Path planning for single unmanned aerial vehicle by separately evolving waypoints","volume":"31","author":"Yang","year":"2015","journal-title":"IEEE Trans. Robot."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1109\/TSMC.2013.2248146","article-title":"Route planning for Unmanned Aerial Vehicle (UAV) on the sea using hybrid differential evolution and quantum-behaved particle swarm optimization","volume":"43","author":"Fu","year":"2013","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ling, X., and Hao, Y. (2015, January 4\u20136). Effective 3-D path planning for UAV in presence of threat netting. Proceedings of the 2015 Fifth International Conference on Communication Systems and Network Technologies (CSNT), Gwalior, India.","DOI":"10.1109\/CSNT.2015.217"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1016\/j.simpat.2009.10.006","article-title":"Three-dimension path planning for UCAV using hybrid meta-heuristic ACO-DE algorithm","volume":"18","author":"Duan","year":"2010","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_16","first-page":"285","article-title":"Dynamic graph-search algorithm for global path planning in presence of hazardous weather","volume":"69","author":"Garcia","year":"2013","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.3390\/app5041457","article-title":"Quantum wind driven optimization for unmanned combat air vehicle path planning","volume":"5","author":"Zhou","year":"2015","journal-title":"Appl. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yao, P., and Wang, H. (2016). Dynamic Adaptive Ant Lion Optimizer applied to route planning for unmanned aerial vehicle. Soft Comput.","DOI":"10.1007\/s00500-016-2138-6"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3966","DOI":"10.1080\/01431161.2011.636081","article-title":"Google Earth as a virtual globe tool for Earth science applications at the global scale: progress and perspectives","volume":"33","author":"Le","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","unstructured":"Zheng, G., and Zheng, Y. (2011, January 24\u201327). Radar netting technology & its development. Proceedings of the 2011 IEEE CIE International Conference on Radar, Chengdu, China."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bell, D.G., Kuehnel, F., Maxwell, C., Kim, R., Kasraie, K., Gaskins, T., Hogan, P., and Coughlan, J. (2007, January 3\u201310). NASA world wind: Opensource GIS for mission operations. Proceedings of the 2007 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2007.352954"},{"key":"ref_22","unstructured":"United States Standard for Performance Based Navigation (PBN) Instrument Procedure Design Document Information, Available online: http:\/\/www.faa.gov\/regulations_policies\/."},{"key":"ref_23","first-page":"1","article-title":"Killing zone boundary and defense efficiency of surface-air missile","volume":"1","author":"Mingan","year":"1992","journal-title":"Tactical Missile Technol."},{"key":"ref_24","unstructured":"Haifeng, L. (2006). A Study on Spatial Modeling Method of Threats in Mission Rehearsal of Tactical Aviation. [Master\u2019s Thesis, National University of Defense Technology]. (In Chinese)."},{"key":"ref_25","unstructured":"Skolnik, M.I. (2002). Introduction to Radar Systems, McGraw-Hill Book Company. [3rd ed.]."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/S0893-9659(98)00149-9","article-title":"A box spline subdivision pyramid algorithm","volume":"12","author":"Hebert","year":"1999","journal-title":"Appl. Math. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1137\/S0036141097330294","article-title":"Nonlinear pyramid transforms based on median-interpolation","volume":"31","author":"Donoho","year":"2000","journal-title":"SIAM J. Math. Anal."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1109\/78.277863","article-title":"The recursive pyramid algorithm for the discrete wavelet transform","volume":"42","author":"Vishwanath","year":"1994","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/3477.484436","article-title":"Ant system: Optimization by a colony of cooperating agents","volume":"26","author":"Dorigo","year":"1996","journal-title":"IEEE Trans. Syst. Man Cybern. Cybern."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/4235.585892","article-title":"Ant colony system: A cooperative learning approach to the traveling salesman problem","volume":"1","author":"Dorigo","year":"1997","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_31","unstructured":"Adubi, S.A., and Misra, S. (October, January 29). A comparative study on the ant colony optimization algorithms. Proceedings of the 11th International Conference on Electronics, Computer and Computation (ICECCO), Abuja, Nigeria."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s11721-015-0116-8","article-title":"An ant colony-based semi-supervised approach for learning classification rules","volume":"9","author":"Albinati","year":"2015","journal-title":"Swarm Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1109\/TSC.2014.2382555","article-title":"Using ant colony system to consolidate VMs for green cloud computing","volume":"8","author":"Farahnakian","year":"2015","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_34","first-page":"1","article-title":"A modified ant colony optimization algorithm for network coding resource minimization","volume":"1","author":"Wang","year":"2015","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1080\/18756891.2012.670520","article-title":"Autonomously implemented versatile path planning for mobile robots based on cellular automata and ant colony","volume":"5","author":"Adel","year":"2012","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1559\/152304099782424901","article-title":"Topologically consistent line simplification with the douglas-peucker algorithm","volume":"26","author":"Saalfeld","year":"1999","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"St\u00fctzle, T., L\u00f3pez-Ib\u00e1nez, M., Pellegrini, P., Maur, M., De Oca, M.M., Birattari, M., and Dorigo, M. (2011). Parameter Adaptation in Ant Colony Optimization. Autonomous Search, Springer.","DOI":"10.1007\/978-3-642-21434-9_8"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/5\/10\/184\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:32:36Z","timestamp":1760211156000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/5\/10\/184"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,10,10]]},"references-count":37,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2016,10]]}},"alternative-id":["ijgi5100184"],"URL":"https:\/\/doi.org\/10.3390\/ijgi5100184","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2016,10,10]]}}}