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Path planning based on unmanned aerial vehicle performance with segmented cellular genetic algorithm

Year 2025, Volume: 40 Issue: 1, 135 - 154

Ahmet Gezer Onder Turan Tolga Baklacıoğlu

https://doi.org/10.17341/gazimmfd.1156817

Abstract

Unmanned Aerial Vehicles (UAV) have a wide range of use on industrial, military and commercial areas. Comprehensive and error-free sub-systems are needed to provide planning, management and coordination of UAVs which are designed for variable purposes with different capabilities and sizes. An important part of UAV technological development consists of improvements in the scope of path planning. Different choices can be made in path planning according to operational priorities, it may be preferred to reach the destination as fast as possible or to increase the airtime by compromising speed. Fuel data of cruise, climb and descent phases are used in the path planning algorithm for every speed and altitude that the UAV can fly. Thus, economical and airtime-maximizing paths could be produced on the basis of performance characteristics compatible with the kinematic constraints customized for the UAV. In this thesis, Cellular (cGA) and Segmented Cellular Genetic Algorithm (scGA) are proposed. The novel overprotective algorithm which has a fixed initial population and segmented chromosome structure achieves a high convergence speed to optimal solution and can generate paths which have 5.2 times higher fitness value on average compared with a conventional Genetic Algorithm (GA). It has been observed that scGA improves the initial population in terms of the best solutions 1.9 times and the general population 5.8 times better compared with GA.

Keywords

Path Planning Trajectory Planning Genetic Algorithm Evolutionary Algorithm UAV Aircraft Performance

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Parçalı hücresel genetik algoritma ile insansız hava aracı performansına dayalı yol planlama

Year 2025, Volume: 40 Issue: 1, 135 - 154

Ahmet Gezer Onder Turan Tolga Baklacıoğlu

https://doi.org/10.17341/gazimmfd.1156817

Abstract

İnsansız Hava Araçları (İHA), endüstriyel, askerî ve ticari geniş bir uygulama alanına sahiptir. Değişken amaçlar için tasarlanmış farklı yeteneklere ve boyutlara sahip İHA’ların; planlama, yönetme ve koordinasyonunu sağlayabilmek için hatasız çalışan kapsamlı alt sistemlere ihtiyaç vardır. İHA teknolojik gelişiminin önemli bir parçası, yol planlama alanındaki iyileştirmelerden oluşmaktadır. Yol planlamada operasyonel önceliklere göre farklı tercihler yapılabilir, varış noktasına en hızlı şekilde ulaşılması veya hızdan ödün vererek havada kalma süresinin uzatılması istenebilir. Bir İHA’ya ait uçabildiği her hız ve her irtifa için; seyir, tırmanma ve alçalma fazlarına ait yakıt verileri yol planlama algoritmasında kullanılmıştır. Böylece, bir İHA için özelleştirilmiş kinematik kısıtlara uyumlu performans özellikleri temelinde ekonomik ve havada kalma süresini uzatan yollar üretilebilmiştir. Bu tez çalışmasında, Hücresel (cGA) ve Parçalı Hücresel Genetik Algoritma (scGA) önerilmiştir. Sabit başlangıç popülasyonu ve parçalı kromozom yapısına sahip aşırı korumacı yeni algoritma; optimal çözüme yüksek yakınsama hızı elde etmiş, geleneksel bir genetik algoritmaya (GA) kıyasla ortalama 5,2 kat daha yüksek uygunluk değerine sahip yollar üretebilmiştir. scGA’nın GA’ya kıyasla, başlangıç popülasyonuna göre en iyi çözümü 1,9 kat ve genel popülasyonu 5,8 kat daha iyi geliştirdiği gözlenmiştir.

Keywords

Yol Planlama Yörünge Planlama Genetik Algoritma Evrimsel Algoritma İHA Hava Aracı Performansı

References

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  • Mayer, R., A flight trajectory model for a pc-based airspace analysis tool, AIAA Modeling and Simulation Technologies Conference and Exhibit, 1-10, 2002.
  • Eurocontrol. Base of aircraft data. https://www.eurocontrol.int/model/bada Yayın tarihi 16.01.2009. Erişim tarihi 20.03.2022.
  • Li, D.C., Cheng, N., Cheng, P., Song, J.Y., Design and Development of an Integrated Flight Planning and Rehearsal System Based on GIS and Navigation Database, Applied Mechanics and Materials, Trans Tech Publ, 169-177, 2012.
  • Labonte, G., Simple formulas for the fuel of climbing propeller driven airplanes, Advances in aircraft and spacecraft science, 2(4), 367-389, 2015.
  • Roberge, V., Tarbouchi, M., Labonté, G., Fast genetic algorithm path planner for fixed-wing military UAV using GPU, IEEE Transactions on Aerospace and Electronic Systems, 54(5), 2105-2117, 2018.
  • Langelaan, J.W., Gust energy extraction for mini and micro uninhabited aerial vehicles, Journal of guidance, control, and dynamics, 32(2), 464-473, 2009.
  • Langelaan, J., Long distance/duration trajectory optimization for small UAVs, AIAA Guidance, Navigation and Control Conference and Exhibit, 1-14, 2007.
  • Williams, A., Yakimenko, O., Persistent mobile aerial surveillance platform using intelligent battery health management and drone swapping, 2018 4th International Conference on Control, Automation and Robotics (ICCAR), IEEE, 237-246, 2018.
  • Fujii, K., Higuchi, K., Rekimoto, J., Endless flyer: a continuous flying drone with automatic battery replacement, 2013 IEEE 10th international conference on ubiquitous intelligence and computing and 2013 IEEE 10th international conference on autonomic and trusted computing, IEEE, 216-223, 2013.
  • Qi, Y.C., Zhao, Y.J., Energy-efficient trajectories of unmanned aerial vehicles flying through thermals, Journal of Aerospace Engineering, 18(2), 84-92, 2005.
  • Kniffin, C.A., Dogan, A., Blake, W.B., Formation flight for fuel saving in coronet mission-part a: Sweet spot determination, AIAA Atmospheric Flight Mechanics Conference, 1-14, 2016.
  • Russell, S., Norvig, P., Intelligence artificielle: Avec plus de 500 exercices, Pearson Education, Paris, 2010.
  • Li, S., Sun, X., Xu, Y., Particle swarm optimization for route planning of unmanned aerial vehicles, Information Acquisition, 2006 IEEE International Conference on, IEEE, 1213-1218, 2006.
  • Gao, D., Gong, G., Wang, J., Han, L., Multi-resolution path planning for Miniature Air Vehicles with wind effect, Industrial Informatics (INDIN), 2012 10th IEEE International Conference on, IEEE, 167-171, 2012.
  • Yılmaz, N., Gencer, C., Integration of sensor vision capabilities on UAV flight route optimization: a linear model and a heuristic algorithm proposal, Journal of the Faculty of Engineering and Architecture of Gazi University, 34, 2019.
  • Holland, J.H., Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, MIT press, Massachusetts, 1992.
  • Fu, X., Gao, X., Genetic algorithm with adaptive immigrants for dynamic flight path planning, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, IEEE, 630-634, 2010.
  • Durmaz, E.D., Şahin, R., Çok Amaçlı Tek Sıra Tesis Düzenleme Probleminin Çözümü için Nsga-II ve Hedef Programlama Yaklaşımı, Journal of the Faculty of Engineering and Architecture of Gazi University, 32(3), 2017.
  • Okay, F.Y., Özdemİr, S., Kablosuz Algilayici Ağlarda Kapsama Alaninin Çok Amaçli Evrimsel Algoritmalar İle Artirilmasi, Journal of the Faculty of Engineering and Architecture of Gazi University, 30(2), 2015.
  • Xiao, J., Michalewicz, Z., Zhang, L., Trojanowski, K., Adaptive evolutionary planner/navigator for mobile robots, IEEE transactions on evolutionary computation, 1(1), 18-28, 1997.
  • Yıldırım, M., Akay, R., Fast path planning in multi-obstacle environments for mobile robots, Journal of the Faculty of Engineering and Architecture of Gazi University, 36(3), 2021.
  • Nikolos, I.K., Valavanis, K. P., Tsourveloudis, N. C., Kostaras, A. N., Evolutionary algorithm based offline/online path planner for UAV navigation, IEEE Trans Syst Man Cybern B Cybern, 33(6), 898-912, 2003.
  • Roberge, V., Tarbouchi, M., Labonte, G., Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning, IEEE Transactions on Industrial Informatics, 9(1), 132-141, 2013.
  • Fu, Y.-G., Zhou, C.-P., Hu, H.-P., Research on Differential Evolution Algorithm for Path Planning for Unmanned Aerial Vehicle in Ocean Environment, Acta Armamentarii, 3, 1-9, 2012.
  • Fu, S.-Y., Han, L.-W., Tian, Y., Yang, G.-S., Path planning for unmanned aerial vehicle based on genetic algorithm, Cognitive Informatics & Cognitive Computing (ICCI* CC), 2012 IEEE 11th International Conference on, IEEE, 140-144, 2012.
  • Zheng, R., Feng, Z.-M., Lu, M.-Q., Application of Particle Genetic Algorithm to Path Planning of Unmanned Aerial Vehicle, Computer Simulation, 6, 88-92, 2011.
  • Alba, E., Dorronsoro, B., Cellular genetic algorithms, 42, Springer Science & Business Media, Heidelberg, 2009.
  • Whitley, L.D., Cellular genetic algorithms, Proceedings of the 5th International Conference on Genetic Algorithms, 1-12, 1993.
  • Manderick, B., Spiessens, P., Fine-grained parallel genetic algorithms, Proceedings of the third international conference on Genetic algorithms, Morgan Kaufmann Publishers Inc., 428-433, 1989.
  • Robertson, G.G., Parallel implementation of genetic algorithms in a classifier system, Genetic algorithms and their applications: proceedings of the second International Conference on Genetic Algorithms, Hillsdale, NJ: L. Erlhaum Associates, Massachusetts Institute of Technology, Cambridge, 140-147, 1987.
  • Wright, S., Isolation by distance, Genetics, 28(2), 114-138, 1943.
  • Alba, E., Dorronsoro, B., Solving the vehicle routing problem by using cellular genetic algorithms, Evolutionary Computation in Combinatorial Optimization, Springer, Heidelberg, 2004.
  • Alba, E., Alfonso, H., Dorronsoro, B., Advanced models of cellular genetic algorithms evaluated on SAT, Proceedings of the 2005 conference on Genetic and evolutionary computation, ACM, 1123-1130, 2005.
  • Folino, G., Pizzuti, C., Spezzano, G., Combining cellular genetic algorithms and local search for solving satisfiability problems, Tools with Artificial Intelligence, 1998. Proceedings. Tenth IEEE International Conference on, IEEE, 192-198, 1998.
  • Murata, T., Gen, M., Cellular genetic algorithm for multi-objective optimization, Proc. of the 4th Asian Fuzzy System Symposium, 538-542, 2002.
  • Mantere, T., Image ordering by cellular genetic algorithms with TSP and ICA, Evolutionary Computation, 2009. CEC'09. IEEE Congress on, IEEE, 822-829, 2009.
  • Gezer, A., Turan, Ö., Baklacıoğlu, T., UAV path planning using segmented cellular evolutionary algorithm, International Journal of Sustainable Aviation, 2(3), 222-234, 2016.
  • Gezer, A., Turan, Ö., Baklacıoğlu, T., Hücresel Genetik Algoritma ile İnsansız Hava Araçlarında Yol Planlama, Proceeding 3rd International Conference on Applied Engineering and Natural Sciences, Konya, Türkiye, 1346-1350, 2022.
  • Gezer, A., Turan, Ö., Baklacıoğlu, T., İnsansız Hava Aracı Performansına Dayalı Yol Planlama, Proceeding 3rd International Conference on Applied Engineering and Natural Sciences, Konya, Türkiye, 2055-2061, 2022.
  • Boulos, M.N.K., Web GIS in practice III: creating a simple interactive map of England's strategic Health Authorities using Google Maps API, Google Earth KML, and MSN Virtual Earth Map Control, 4(22), 1-8, 2005.
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There are 86 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Ahmet Gezer 0000-0002-8764-3011

Onder Turan 0000-0003-0303-4313

Tolga Baklacıoğlu 0000-0002-9600-2697

Early Pub Date May 17, 2024
Publication Date
Submission Date August 5, 2022
Acceptance Date February 3, 2024
Published in Issue Year 2025 Volume: 40 Issue: 1

Cite

APA Gezer, A., Turan, O., & Baklacıoğlu, T. (2024). Parçalı hücresel genetik algoritma ile insansız hava aracı performansına dayalı yol planlama. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 40(1), 135-154. https://doi.org/10.17341/gazimmfd.1156817
AMA Gezer A, Turan O, Baklacıoğlu T. Parçalı hücresel genetik algoritma ile insansız hava aracı performansına dayalı yol planlama. GUMMFD. May 2024;40(1):135-154. doi:10.17341/gazimmfd.1156817
Chicago Gezer, Ahmet, Onder Turan, and Tolga Baklacıoğlu. “Parçalı hücresel Genetik Algoritma Ile insansız Hava Aracı performansına Dayalı Yol Planlama”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40, no. 1 (May 2024): 135-54. https://doi.org/10.17341/gazimmfd.1156817.
EndNote Gezer A, Turan O, Baklacıoğlu T (May 1, 2024) Parçalı hücresel genetik algoritma ile insansız hava aracı performansına dayalı yol planlama. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40 1 135–154.
IEEE A. Gezer, O. Turan, and T. Baklacıoğlu, “Parçalı hücresel genetik algoritma ile insansız hava aracı performansına dayalı yol planlama”, GUMMFD, vol. 40, no. 1, pp. 135–154, 2024, doi: 10.17341/gazimmfd.1156817.
ISNAD Gezer, Ahmet et al. “Parçalı hücresel Genetik Algoritma Ile insansız Hava Aracı performansına Dayalı Yol Planlama”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40/1 (May 2024), 135-154. https://doi.org/10.17341/gazimmfd.1156817.
JAMA Gezer A, Turan O, Baklacıoğlu T. Parçalı hücresel genetik algoritma ile insansız hava aracı performansına dayalı yol planlama. GUMMFD. 2024;40:135–154.
MLA Gezer, Ahmet et al. “Parçalı hücresel Genetik Algoritma Ile insansız Hava Aracı performansına Dayalı Yol Planlama”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 40, no. 1, 2024, pp. 135-54, doi:10.17341/gazimmfd.1156817.
Vancouver Gezer A, Turan O, Baklacıoğlu T. Parçalı hücresel genetik algoritma ile insansız hava aracı performansına dayalı yol planlama. GUMMFD. 2024;40(1):135-54.