Progress in Artificial Intelligence

, Volume 6, Issue 3, pp 263–274 | Cite as

Handling swarm of UAVs based on evolutionary multi-objective optimization

  • Cristian Ramirez-AtenciaEmail author
  • Maria D. R-Moreno
  • David Camacho
Regular Paper


The fast technological improvements in unmanned aerial vehicles (UAVs) has created new scenarios where a swarm of UAVs could operate in a distributed way. This swarm of vehicles needs to be controlled from a set of ground control stations, and new reliable mission planning systems, which should be able to handle the large amount of variables and constraints. This paper presents a new approach where this complex problem has been modelled as a constraint satisfaction problem (CSP), and is solved using a multi-objective genetic algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission, such as the fuel consumption or the makespan among others. The designed fitness function, used by the algorithm, takes into consideration, as a weighted penalty function, the number of constraints fulfilled for each solution. Therefore, the MOGA algorithm is able to manage the number of constraints fulfilled by the selected plan, so it is possible to maximize in the elitism phase of the MOGA the quality of the solutions found. This approach allows to alleviate the computational effort carried out by the CSP solver, finding new solutions from the Pareto front, and therefore reducing the execution time to obtain a solution. In order to test the performance of this new approach 16 different mission scenarios have been designed. The experimental results show that the approach outperforms the convergence of the algorithm in terms of number of generations and runtime.


Unmanned aerial vehicles Mission planning Constraint satisfaction problems Multi-objective genetic algorithm 



This work has been supported by the next research projects: Airbus Defence and Space (FUAM-076914 and FUAM-076915), UAH 2016/00351/001, EphemeCH (TIN2014-56494-C4-4-P) Spanish Ministry of Economy and Competitivity, CIBERDINE S2013/ICE-3095, both under the European Regional Development Fund FEDER, and RiskTrack project co-funded by the European Union’s Justice Program (2014–2020). The authors would like to acknowledge the support obtained from Airbus Defence and Space, specially from Savier Open Innovation project members: José Insenser, Gemma Blasco and César Castro.


  1. 1.
    Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)CrossRefzbMATHGoogle Scholar
  2. 2.
    Barták, R., Salido, M.A.: Constraint satisfaction for planning and scheduling problems. Constraints 16(3), 223–227 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Bethke, B., Valenti, M., How, J.P.: UAV task assignment. IEEE Robot. Autom. Mag. 15, 39–44 (2008)CrossRefGoogle Scholar
  4. 4.
    Daniel, K., Nash, A., Koenig, S., Felner, A.: Theta*: any-angle path planning on grids. J. Artif. Intell. Res. 39, 533–579 (2010)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  6. 6.
    Fabiani, P., Fuertes, V., Piquereau, A., Mampey, R., Teichteil-Königsbuch, F.: Autonomous flight and navigation of VTOL UAVs: from autonomy demonstrations to out-of-sight flights. Aerosp. Sci. Technol. 11(2–3), 183–193 (2007)CrossRefGoogle Scholar
  7. 7.
    Guerriero, F., Surace, R., Loscri, V., Natalizio, E.: A multi-objective approach for unmanned aerial vehicle routing problem with soft time windows constraints. Appl. Math. Model. 38(3), 839–852 (2014)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hao, X., Liu, J.: A multiagent evolutionary algorithm with direct and indirect combined representation for constraint satisfaction problems. Soft. Comput. 21(3), 781–793 (2017)CrossRefGoogle Scholar
  9. 9.
    Kvarnström, J., Doherty, P.: Automated planning for collaborative UAV systems. In: 11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010, December, pp. 1078–1085 (2010)Google Scholar
  10. 10.
    Leary, S., Deittert, M., Bookless, J.: Constrained UAV mission planning: a comparison of approaches. In: IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 2002–2009. IEEE (2011)Google Scholar
  11. 11.
    Mittal, S., Deb, K.: Three-Dimentional Offline Path Planning for UAVs Using Multiobjective Evolutionary Algorithms. In: 2007 IEEE Congress on Evolutionary Computation (CEC’2007), pp. 3195–3202. IEEE (2007)Google Scholar
  12. 12.
    Pascarella, D., Venticinque, S., Aversa, R., Mattei, M., Blasi, L.: Parallel and distributed computing for UAVs trajectory planning. J. Ambient. Intell. Humaniz. Comput. 6(6), 773–782 (2015)CrossRefGoogle Scholar
  13. 13.
    Perez-Carabaza, S., Besada-Portas, E., Lopez-Orozco, J.A., de la Cruz, J.M.: A real World multi-UAV evolutionary planner for minimum time target detection. In: 2016 on Genetic and Evolutionary Computation Conference—GECCO ’16, pp. 981–988. ACM (2016)Google Scholar
  14. 14.
    Pohl, A.J., Lamont, G.B.: Multi-Objective UAV Mission Planning Using Evolutionary Computation. In: Mason, S.J., Hill, R.R., Mönch, L., Rose, O., Jefferson, T., Fowler, J.W. (eds.) 2008 Winter Simulation Conference, Pohl, pp. 1268–1279. IEEE (2008)Google Scholar
  15. 15.
    Ramirez-Atencia, C., Bello-Orgaz, G., R-Moreno, M.D., Camacho, D.: A hybrid MOGA-CSP for multi-UAV mission planning. In: Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1205–1208. ACM (2015)Google Scholar
  16. 16.
    Ramirez-Atencia, C., Bello-Orgaz, G., R-Moreno, M.D., Camacho, D.: Performance Evaluation of Multi-UAV Cooperative Mission Planning Models. In: Núñez, D.C.M., Nguyen, N., Trawiński, B. (eds.) Computational Collective Intelligence. Lecture Notes in Computer Science, vol. 9330, pp. 203–212. Springer, Cham (2015)Google Scholar
  17. 17.
    Ramirez-Atencia, C., Bello-Orgaz, G., R-Moreno, M.D., Camacho, D.: A weighted penalty fitness for a hybrid MOGA-CSP to solve mission planning problems. In: XI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2016), pp. 305–314 (2016)Google Scholar
  18. 18.
    Ramirez-Atencia, C., Bello-Orgaz, G., R-Moreno, M.D., Camacho, D.: Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms. Soft Computing pp. 1–18 (2016)Google Scholar
  19. 19.
    Rodríguez-Fernández, V., Menéndez, H.D., Camacho, D.: Automatic profile generation for UAV operators using a simulation-based training environment. Prog. Artif. Intell. 5(1), 37–46 (2016)CrossRefGoogle Scholar
  20. 20.
    Rosenberg, B., Richards, M., Langton, J.T., Tenenbaum, S., Stouch, D.W.: Applications of multi-objective evolutionary algorithms to air operations mission planning. In: 10th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO 2008), pp. 1879–1886. ACM, Atlanta, GA, USA (2008)Google Scholar
  21. 21.
    Ruiz, J.J., Martinez-De-Dios, J.R., Cobano, J.A., Ollero, A.: A multi-payload simulator for cooperative UAS missions. 2016 International Conference on Unmanned Aircraft Systems, ICUAS 2016 pp. 1192–1199 (2016)Google Scholar
  22. 22.
    Shang, K., Karungaru, S., Feng, Z., Ke, L., Terada, K.: A GA-ACO hybrid algorithm for the multi-UAV mission planning problem. In: 14th International Symposium on Communications and Information Technologies, ISCIT 2014, pp. 243–248. IEEE (2014)Google Scholar
  23. 23.
    Stouch, D.W., Zeidman, E., Richards, M., McGraw, K.D., Callahan, W.: Coevolving Collection Plans for UAS Constellations. In: 13th Annual Genetic and Evolutionary Computation Conference (GECCO’11), pp. 1691–1698 (2011)Google Scholar
  24. 24.
    Wang, Z., Liu, Q., Tao, H., Li, J.: Multiple task planning based on TS algorithm for multiple heterogeneous unmanned aerial vehicles. In: 2014 IEEE Chinese Guidance, Navigation and Control Conference, CGNCC 2014, pp. 630–635. IEEE (2014)Google Scholar
  25. 25.
    Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)CrossRefGoogle Scholar
  26. 26.
    Zille, H., Ishibuchi, H., Mostaghim, S., Nojima, Y.: Weighted optimization framework for large-scale multi-objective optimization. In: Genetic and Evolutionary Computation—GECCO, pp. 83–84. ACM, Denver, Colorado, USA (2016)Google Scholar
  27. 27.
    Zitzler, E., Brockhoff, D., Thiele, L.: The Hypervolume Indicator Revisited: On the Design of Pareto-compliant Indicators Via Weighted Integration. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) Evolutionary Multi-Criterion Optimization. EMO 2007. Lecture Notes in Computer Science, vol. 4403, pp. 862–876. Springer, Berlin (2007)Google Scholar
  28. 28.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. In: Giannakoglou, K.C., Tsahalis, D.T., Periaux, J., Fogarty, T. (eds.) Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems (EUROGEN 2001), pp. 95–100. International Center for Numerical Methods in Engineering (CIMNE) (2002)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Universidad Autónoma de MadridMadridSpain
  2. 2.Universidad de AlcaláMadridSpain

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