Skip to main content

Advertisement

SpringerLink
Log in

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

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. http://geographiclib.sourceforge.net/.

  2. http://www.gdal.org/.

References

  • Adolf F, Andert F (2010) Onboard mission management for a VTOL UAV using sequence and supervisory control, chap. 19, InTech, pp 301–316

  • Allen JF (1983) Maintaining knowledge about temporal intervals. Commun ACM 26(11):832–843

    Article  MATH  Google Scholar 

  • Barták R (1999) Constraint programming: In pursuit of the holy grail. In: Week of Doctoral Students, pp 555–564

  • 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 930–937

  • 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 167–176

  • Bessière C (2006) Constraint propagation. Found Artif Intell 2:29–83

    Article  Google Scholar 

  • 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 88–102

  • Bethke B, Valenti M, How JP (2008) UAV task assignment. IEEE Robot Autom Mag 15(1):39–44

    Article  Google Scholar 

  • 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 1087–1090

  • Camacho D, Fernandez F, Rodelgo MA (2006) Roboskeleton: an architecture for coordinating robot soccer agents. Eng Appl Artif Intell 19(2):179–188

    Article  Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. Evol Comput 6(2):182–197

    Article  Google Scholar 

  • 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 Operations

    Article  Google Scholar 

  • 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):332–377

    Article  Google Scholar 

  • 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):183–193

    Article  Google Scholar 

  • 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):26–37

    Article  Google Scholar 

  • 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. IEEE

  • 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. IEEE

  • Gonzalez-Pardo A, Palero F, Camacho D (2014) An empirical study on collective intelligence algorithms for video games problem-solving. Comput Inform 34(1):233–253

    Google Scholar 

  • 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

  • Holland JH (1992) Adaptation in natural and artificial systems. MIT Press, Cambridge

    Google Scholar 

  • Kendoul F (2012) Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems. J Field Robot 29(2):315–378

    Article  Google Scholar 

  • 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, 2004

  • Kvarnström J, Doherty P (2010) Automated planning for collaborative UAV systems. In: Control automation robotics and vision, pp 1078–1085. IEEE

  • 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):535–549

    Article  MATH  Google Scholar 

  • 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. IEEE

  • 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):165–184

    Article  Google Scholar 

  • Mouhoub M (2002) Solving temporal constraints in real time and in a dynamic environment. Tech. Rep. WS-02-17, AAAI

  • Mouhoub M (2004) Reasoning with numeric and symbolic time information. Artif Intell Rev 21(1):25–56

    Article  MATH  Google Scholar 

  • 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 1177–1183

  • 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):773–782

    Article  Google Scholar 

  • 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):1557–1561

    Google Scholar 

  • 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 1205–1208

  • 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 203–212

  • Rasmussen S, Shima T (2006) Branch and bound tree search for assigning cooperating uavs to multiple tasks. In: American control conference. IEEE, pp 6–14

  • 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):37–46

  • 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 1490–1497

  • Rollon E, Larrosa J (2006) Bucket elimination for multiobjective optimization problems. J Heuristics 12(4–5):307–328

    Article  MATH  Google Scholar 

  • 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 249–254

  • Savuran H, Karakaya M (2015) Efficient route planning for an unmanned air vehicle deployed on a moving carrier. Soft Comput 20(7):2905–2920

    Article  Google Scholar 

  • Schwalb E, Vila L (1998) Temporal constraints: a survey. Constraints 3(2–3):129–149

    Article  MathSciNet  MATH  Google Scholar 

  • 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. Citeseer

  • 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

  • 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):101–115

    Article  Google Scholar 

  • 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 204–211

  • 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 16–30

  • 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 3333–3336

  • 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):32–49

    Article  Google Scholar 

  • Zitzler E, Laumanns M, Thiele L (2001) SPEA2: Improving the strength pareto evolutionary algorithm. In: Eurogen, vol. 3242, pp. 95–100

  • Zitzler E, Laumanns M, Bleuler S (2004) A tutorial on evolutionary multiobjective optimization. In: Metaheuristics for multiobjective optimisation. Springer, pp 3–37

  • 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 862–876

Download references

Acknowledgments

The authors would like to acknowledge the support obtained from Airbus Defence & Space, specially from Savier Open Innovation project members: José Insenser, César Castro, Gemma Blasco, and Inés Moreno. This study was funded by Spanish Ministry of Science and Education and Competitivity and European Regional Development Fund FEDER (TIN2014-56494-C4-4-P), Comunidad Autónoma de Madrid (CIBERDINE S2013/ICE-3095), and Airbus Defence & Space under Savier Project (FUAM-076915).

Author information

Authors and Affiliations

  1. Universidad Autonónoma de Madrid, Madrid, Spain

    Cristian Ramirez-Atencia, Gema Bello-Orgaz & David Camacho

  2. Universidad de Alcalá, Madrid, Spain

    María D. R-Moreno

Authors
  1. Cristian Ramirez-Atencia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gema Bello-Orgaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. María D. R-Moreno
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David Camacho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cristian Ramirez-Atencia.

Ethics declarations

Conflict of interest

The Authors: Cristian Ramirez-Atencia, Gema Bello-Orgaz, Maria D. R-Moreno, and David Camacho declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by C. Analide.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ramirez-Atencia, C., Bello-Orgaz, G., R-Moreno, M.D. et al. Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms. Soft Comput 21, 4883–4900 (2017). https://doi.org/10.1007/s00500-016-2376-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-016-2376-7

Keywords

Search

Navigation