Skip to main content

Formulation and a MOGA Based Approach for Multi-UAV Cooperative Reconnaissance

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 4101)

Abstract

Multi-UAV cooperative reconnaissance is one of the most challenging research area for UAV operations. The objective is to coordinate different kinds of sensor-bearing UAVs conducting reconnaissance on a set of targets within predefined time windows at minimum cost, while satisfying the reconnaissance demands, and without violating the maximum permitted travel time for each UAV. This paper presents a multi-objective optimization mathematical formulation for the problem. Different from previous formulations, the model takes the reconnaissance resolution demands of the targets and time window constraints into account. Then a multi-objective genetic algorithm CR-MOGA is put forward to solve the problem. In CR-MOGA, Pareto optimality based selection is introduced to generate the parent individuals. Novel evolutionary operators are designed according to the specifics of the problem. Finally the simulation results show the efficiency of our algorithm.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (Canada)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ryan, J.L., Bailey, T.G., Moore, J.T., Carlton, W.B.: Reactive Tabu Search in Unmanned Aerial Reconnaissance Simulations. In: Winter Simulation Conference, Washington D.C, pp. 873–879 (1998)

    Google Scholar 

  2. Hutchison, M.G.: A Method for Estimating Range Requirements of Tactical Reconnaissance UAVs. In: AIAA’s 1st Technical Conference and Workshop on Unmanned Aerospace Vehicles, Virginia, pp. 120–124 (2002)

    Google Scholar 

  3. Ousingsawat, J., Campbell, M.E.: Establishing Trajectories for Multi-Vehicle Reconnaissance. In: AIAA Guidance, Navigation, and Control Conference and Exhibit, Providence, Rhode Island, pp. 1–12 (2004)

    Google Scholar 

  4. Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art. Evolutionary Computation 8(2), 125–147 (2000)

    CrossRef  Google Scholar 

  5. Zitzler, E., Laumanns, M., Bleuler, S.: A Tutorial on Evolutionary Multiobjective Optimization. Metaheuristics for Multiobjective Optimisation 535, 3–37 (2004)

    CrossRef  MathSciNet  Google Scholar 

  6. Solomon, M.M.: Algorithms for Vehicle Routing and Scheduling Problems with Time Window Constraints. Operations Research 35(2), 254–265 (1987)

    CrossRef  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tian, J., Shen, L., Zheng, Y. (2006). Formulation and a MOGA Based Approach for Multi-UAV Cooperative Reconnaissance. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2006. Lecture Notes in Computer Science, vol 4101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11863649_13

Download citation

  • DOI: https://doi.org/10.1007/11863649_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44494-7

  • Online ISBN: 978-3-540-44496-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics