Soft Computing

, Volume 21, Issue 17, pp 4883–4900 | Cite as

Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms

  • Cristian Ramirez-Atencia
  • Gema Bello-Orgaz
  • María D. R-Moreno
  • David Camacho
Focus

Abstract

Due to recent booming of unmanned air vehicles (UAVs) technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from ground control stations (GCSs) where human operators use rudimentary systems. This paper presents a new multi-objective genetic algorithm for solving complex mission planning problems involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a constraint satisfaction problem to check whether solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets, optimizing different variables of the mission, such as the makespan, the fuel consumption, and distance. Experimental results show that the new algorithm is able to obtain good solutions; however, as the problem becomes more complex, the optimal solutions also become harder to find.

Keywords

Unmanned air vehicles Mission planning Multi-objective optimization Genetic algorithms Constraint satisfaction problems 

References

  1. Adolf F, Andert F (2010) Onboard mission management for a VTOL UAV using sequence and supervisory control, chap. 19, InTech, pp 301–316Google Scholar
  2. Allen JF (1983) Maintaining knowledge about temporal intervals. Commun ACM 26(11):832–843CrossRefMATHGoogle Scholar
  3. Barták R (1999) Constraint programming: In pursuit of the holy grail. In: Week of Doctoral Students, pp 555–564Google Scholar
  4. Bello-Orgaz G, Camacho D (2014) Evolutionary clustering algorithm for community detection using graph-based information. In: Evolutionary Computation (CEC), 2014 IEEE congress on, pp 930–937Google Scholar
  5. Bello-Orgaz G, Ramirez-Atencia C, Fradera-Gil J, Camacho D (2015) Gampp: genetic algorithm for uav mission planning problems. In: Intelligent distributed computing IX. Springer International Publishing, pp 167–176Google Scholar
  6. Bessière C (2006) Constraint propagation. Found Artif Intell 2:29–83CrossRefGoogle Scholar
  7. Bessière C, Meseguer P, Freuder E, Larrosa J (1999) On forward checking for non-binary constraint satisfaction. In: Jaffar J (ed) International conference on principles and practice of constraint programming, lecture notes in computer science, vol 1713. Springer, Berlin, pp 88–102Google Scholar
  8. Bethke B, Valenti M, How JP (2008) UAV task assignment. IEEE Robot Autom Mag 15(1):39–44CrossRefGoogle Scholar
  9. Bin X, Min W, Yanming L, Yu F (2010) Improved genetic algorithm research for route optimization of logistic distribution. In: Proceedings of the 2010 international conference on computational and information sciences, ICCIS ’10, IEEE Computer Society, Washington, pp 1087–1090Google Scholar
  10. Camacho D, Fernandez F, Rodelgo MA (2006) Roboskeleton: an architecture for coordinating robot soccer agents. Eng Appl Artif Intell 19(2):179–188CrossRefGoogle Scholar
  11. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. Evol Comput 6(2):182–197CrossRefGoogle Scholar
  12. Diaz D, Cesta A, Oddi A, Rasconi R, R-Moreno MD (2013) Efficient energy management for autonomous control in rover missions. IEEE Comput Intell Mag 8(4):12–24 Special Issue on Computational Intelligence for Space Systems and OperationsCrossRefGoogle Scholar
  13. Doherty P, Kvarnström J, Heintz F (2009) A temporal logic-based planning and execution monitoring framework for unmanned aircraft systems. Auton Agent Multi-agent Syst 19(3):332–377CrossRefGoogle Scholar
  14. Fabiani P, Fuertes V, Piquereau A, Mampey R, Teichteil-Konigsbuch F (2007) Autonomous flight and navigation of VTOL UAVs: from autonomy demonstrations to out-of-sight flights. Aerosp Sci Technol 11(2–3):183–193CrossRefGoogle Scholar
  15. Fonseca C, Fleming P (1998) Multiobjective optimization and multiple constraint handling with evolutionary algorithms. A unified formulation. IEEE Trans Syst Man Cybern- A: Syst Hum 28(1):26–37CrossRefGoogle Scholar
  16. Geng L, Zhang Y, Wang J, Fuh J, Teo S (2013) Cooperative task planning for multiple autonomous uavs with graph representation and genetic algorithm. In: Control and automation (ICCA), 10th IEEE international conference on, pp 394–399. IEEEGoogle Scholar
  17. Gonzalez-Pardo A, Camacho D (2013) A new CSP graph-based representation for ant colony optimization. In: IEEE conference on evolutionary computation (CEC 2013), vol 1, pp 689–696. IEEEGoogle Scholar
  18. Gonzalez-Pardo A, Palero F, Camacho D (2014) An empirical study on collective intelligence algorithms for video games problem-solving. Comput Inform 34(1):233–253Google Scholar
  19. Hao X, Liu J (2015) A multiagent evolutionary algorithm with direct and indirect combined representation for constraint satisfaction problems. Soft Comput 1–13. doi:10.1007/s00500-015-1815-1
  20. Holland JH (1992) Adaptation in natural and artificial systems. MIT Press, CambridgeGoogle Scholar
  21. Kendoul F (2012) Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems. J Field Robot 29(2):315–378CrossRefGoogle Scholar
  22. Kuter U, Sirin E, Parsia B, Nau D, Hendlerb J (2005) Information gathering during planning for web service composition. Web semantics: science, services and agents on the World Wide Web 3(2):183–205. Selected papers from the international semantic web conference, 2004 ISWC, 2004 3rd international semantic web conference, 2004Google Scholar
  23. Kvarnström J, Doherty P (2010) Automated planning for collaborative UAV systems. In: Control automation robotics and vision, pp 1078–1085. IEEEGoogle Scholar
  24. Lee Yi, Kim YG (2008) Comparison of fuzzy implication operators by means of fuzzy relational products used for intelligent local path-planning of auvs. Soft Comput 13(6):535–549CrossRefMATHGoogle Scholar
  25. Menendez HD, Barrero DF, Camacho D (2014) A co-evolutionary multi-objective approach for a k-adaptive graph-based clustering algorithm. In: Evolutionary computation (CEC), 2014 IEEE congress on, pp 2724–2731. IEEEGoogle Scholar
  26. Merino L, Caballero F, Martínez-de Dios JR, Ferruz J, Ollero A (2006) A cooperative perception system for multiple uavs: application to automatic detection of forest fires. J Field Robot 23(3–4):165–184CrossRefGoogle Scholar
  27. Mouhoub M (2002) Solving temporal constraints in real time and in a dynamic environment. Tech. Rep. WS-02-17, AAAIGoogle Scholar
  28. Mouhoub M (2004) Reasoning with numeric and symbolic time information. Artif Intell Rev 21(1):25–56CrossRefMATHGoogle Scholar
  29. Nash A, Daniel K, Koenig S, Felner A (2007) Theta*: any-angle path planning on grids. In: Proceedings of the National Conference on Artificial Intelligence, vol 22. AAAI Press, MIT Press, Menlo Park, Cambridge, London, pp 1177–1183Google Scholar
  30. Pascarella D, Venticinque S, Aversa R, Mattei M, Blasi L (2015) Parallel and distributed computing for uavs trajectory planning. J Ambient Intell Humaniz Comput 6(6):773–782CrossRefGoogle Scholar
  31. Pereira E, Bencatel R, Correia J, Félix L, Gonçalves G, Morgado J, Sousa J (2009) Unmanned air vehicles for coastal and environmental research. J Coast Res II(56):1557–1561Google Scholar
  32. Ramirez-Atencia C, Bello-Orgaz G, R-Moreno MD, Camacho D (2015a) A hybrid MOGA-CSP for multi-UAV mission planning. In: Proceedings of the companion publication of the 2015 on genetic and evolutionary computation conference. ACM, pp 1205–1208Google Scholar
  33. Ramirez-Atencia C, Bello-Orgaz G, R-Moreno MD, Camacho D (2015b) Performance evaluation of multi-UAV cooperative mission planning models. In: Computational collective intelligence. Springer International Publishing, pp 203–212Google Scholar
  34. Rasmussen S, Shima T (2006) Branch and bound tree search for assigning cooperating uavs to multiple tasks. In: American control conference. IEEE, pp 6–14Google Scholar
  35. Rodríguez-Fernández V, Menéndez HD, Camacho D (2015a) Automatic profile generation for uav operators using a simulation-based training environment. Prog Artif Intell 5(1):37–46Google Scholar
  36. Rodriguez-Fernandez V, Ramirez-Atencia C, Camacho D (2015b) A multi-uav mission planning videogame-based framework for player analysis. In: Evolutionary computation (CEC), 2015 IEEE congress on. IEEE, pp 1490–1497Google Scholar
  37. Rollon E, Larrosa J (2006) Bucket elimination for multiobjective optimization problems. J Heuristics 12(4–5):307–328CrossRefMATHGoogle Scholar
  38. Rollon E, Larrosa J (2007) Multi-objective Russian doll search. In: Proceedings of the national conference on artificial intelligence, vol 22. AAAI Press, MIT Press, Menlo Park, Cambridge, London, pp 249–254Google Scholar
  39. Savuran H, Karakaya M (2015) Efficient route planning for an unmanned air vehicle deployed on a moving carrier. Soft Comput 20(7):2905–2920CrossRefGoogle Scholar
  40. Schwalb E, Vila L (1998) Temporal constraints: a survey. Constraints 3(2–3):129–149MathSciNetCrossRefMATHGoogle Scholar
  41. Soliday SW, et al. (1999) A genetic algorithm model for mission planning and dynamic resource allocation of airborne sensors. In: Proceedings, 1999 IRIS National Symposium on Sensor and Data Fusion. CiteseerGoogle Scholar
  42. Tang L, Zhu C, Zhang W, Liu Z (2011) Robust mission planning based on nested genetic algorithm. In: Advanced computational intelligence (IWACI), 2011 fourth international workshop on, pp 45–49. IEEE. doi:10.1109/IWACI.2011.6159972
  43. Vachtsevanos G, Tang L, Drozeski G, Gutierrez L (2005) From mission planning to flight control of unmanned aerial vehicles: strategies and implementation tools. Ann Rev Control 29(1):101–115CrossRefGoogle Scholar
  44. Van Veldhuizen D, Lamont GB, et al. (2000) On measuring multiobjective evolutionary algorithm performance. In: Evolutionary computation, 2000. Proceedings of the 2000 congress on, vol 1. IEEE, pp 204–211Google Scholar
  45. Wagner T, Trautmann H, Martí L (2011) A taxonomy of online stopping criteria for multi-objective evolutionary algorithms. In: Evolutionary multi-criterion optimization. Springer, pp 16–30Google Scholar
  46. Wu J, Zhou G (2006) High-resolution planimetric mapping from uav video for quick-response to natural disaster. In: Geoscience and remote sensing symposium, 2006. IGARSS 2006. IEEE international conference on. IEEE, pp 3333–3336Google Scholar
  47. Zhou A, Qu BY, Li H, Zhao SZ, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evolut Comput 1(1):32–49CrossRefGoogle Scholar
  48. Zitzler E, Laumanns M, Thiele L (2001) SPEA2: Improving the strength pareto evolutionary algorithm. In: Eurogen, vol. 3242, pp. 95–100Google Scholar
  49. Zitzler E, Laumanns M, Bleuler S (2004) A tutorial on evolutionary multiobjective optimization. In: Metaheuristics for multiobjective optimisation. Springer, pp 3–37Google Scholar
  50. Zitzler E, Brockhoff D, Thiele L (2007) The hypervolume indicator revisited: On the design of pareto-compliant indicators via weighted integration. In: Evolutionary multi-criterion optimization. Springer, pp 862–876Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Cristian Ramirez-Atencia
    • 1
  • Gema Bello-Orgaz
    • 1
  • María D. R-Moreno
    • 2
  • David Camacho
    • 1
  1. 1.Universidad Autonónoma de MadridMadridSpain
  2. 2.Universidad de AlcaláMadridSpain

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