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Journal of Intelligent & Robotic Systems

, Volume 94, Issue 2, pp 491–501 | Cite as

Real-time UAV Rerouting for Traffic Monitoring with Decomposition Based Multi-objective Optimization

  • Xiaofeng LiuEmail author
  • Zhong-Ren Peng
  • Li-Ye Zhang
Article
  • 113 Downloads

Abstract

This paper introduces unmanned aerial vehicle (UAV) to monitor traffic situation, and considers the UAV real-time rerouting problem. Firstly, critical target is introduced at the time of UAV route re-planning, which is used to identify the existing visited targets and the remaining unvisited targets. Meanwhile, a real-time UAV rerouting model is proposed with the consideration of time window and multi-objective optimization. Then, a target insertion method is used to generate feasible UAV routes, and a decomposition based multi-objective evolutionary algorithm is proposed. Next, a case study and algorithm sensitivity analysis are implemented, and the results show that compared with the initial optimal solutions, the optimized optimal solutions are improved significantly. In addition, the proposed algorithm is compared with the non-dominated sorting genetic algorithm II (NSGA-II), the case study shows that the proposed algorithm outperforms NSGA-II in terms of computational time, the percentage of finding optimal UAV routes and solution quality. It suggests that the proposed algorithm is promising in planning UAV cruise routes.

Keywords

Traffic monitoring Unmanned aerial vehicle Real-time rerouting Multi-objective optimization 

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References

  1. 1.
    Federal Aviation Administration. Unmanned aircraft systems [EB/OL]. https://www.faa.gov/uas/. Accessed: Dec 22 2016 (2016)
  2. 2.
    Civil Aviation Administration of China. Air traffic control regulation of civil unmanned aerial vehicle systems [EB/OL]. http://www.caac.gov.cn/XXGK/XXGK/GFXWJ/201610/t2016100840016.html. Accessed: Dec 22 2016 (2016)
  3. 3.
    Xu, Y., Yu, G., Wu, X., Wang, Y., Ma, Y.: An enhanced viola-jones vehicle detection method from unmanned aerial vehicles imagery. IEEE Trans. Intell. Trans. Syst. 18(7), 1–12 (2017)CrossRefGoogle Scholar
  4. 4.
    Zhang, L., Peng, Z., Sun, D.J., Liu, X.: A Uav-Based Automatic Traffic Incident Detection System for Low Volume Roads. In: Transportation research board of the national academies, pp 542–558. National Research Council, Washington (2013)Google Scholar
  5. 5.
    Dobson, R., Colling, T., Brooks, C., Roussi, C., Watkins, M., Dean, D.: Collecting decision support system data through remote sensing of unpaved roads. Transp Res Rec.: J. Transp. Res. Board (2433), 108–115 (2014)Google Scholar
  6. 6.
    Zink, J., Lovelace, B.: Unmanned aerial vehicle bridge inspection demonstration project. Minnesota Department of Transportation, Minnesota (2015)Google Scholar
  7. 7.
    Hutchison, M.G.: A method for estimating range requirements of tactical reconnaissance UAVs. In: Proceedings of AIAA’S 1St technical conference and workshop on unmanned aerospace vehicles, pp 1–12. AIAA, Virginia (2002)Google Scholar
  8. 8.
    Yan, Q., Peng, Z., Chang, Y.: Unmanned aerial vehicle cruise route optimization model for sparse road network. In: Transportation research board of the national academies, pp 432–445. National Research Council, Washington (2011)Google Scholar
  9. 9.
    Pitre, R.R., Delbalzo, R.: Uav route planning for joint search and track missions–an information-value approach. IEEE Trans. Aerosp. Electron. Syst. 48(3), 2551–2565 (2012)CrossRefGoogle Scholar
  10. 10.
    Peng, Z., Wu, J., Chen, J.: Three-dimensional multi-constraint route planning of unmanned aerial vehicle low-altitude penetration based on coevolutionary multi-agent genetic algorithm. J. Cent. South Univ. Technol. 18 (5), 1502–1508 (2011)CrossRefGoogle Scholar
  11. 11.
    Liu, X., Gao, L, Guan, Z., Song, Y.: A multi-objective optimization model for planning unmanned aerial vehicle cruise route. Intern. J. Adv. Robot. Syst. 13, 1–8 (2016)CrossRefGoogle Scholar
  12. 12.
    Chen, Y., Luo, G., Mei, Y., Yu, J., Su, X.: UAV Path planning using artificial potential field method updated by optimal control theory. Intern. J. Syst. Sci. 47(6), 1407–1420 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Ragi, S., Chong, E.K.P.: UAV Path planning in a dynamic environment via partially observable Markov decision process. IEEE Trans. Aerosp. Electron. Syst. 49(4), 2397–2412 (2013)CrossRefGoogle Scholar
  14. 14.
    Pehlivanoglu, Y.V.: A new vibrational genetic algorithm enhanced with a Voronoi diagram for path planning of autonomous UAV. Aerospace Sci. Technol. 16(1), 47–55 (2012)CrossRefGoogle Scholar
  15. 15.
    Chen, H., Hsueh, C., Chang, M.: The real-time time-dependent vehicle routing problem. Transport. Res. Part E 42, 383–408 (2006)CrossRefGoogle Scholar
  16. 16.
    Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)CrossRefGoogle Scholar
  17. 17.
    Zitzler, E., Thiele, L.: Multi-objective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 4(3), 257–271 (1999)CrossRefGoogle Scholar
  18. 18.
    Knowles, J.D., Corne, D.W.: The Pareto archived evolution strategy: a new baseline algorithm for pareto multiobjective optimization. In: Proceedings of the 1999 congress on evolutionary computation, pp 98–105. IEEE, Washington (1999)Google Scholar
  19. 19.
    Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  20. 20.
    Zhang, Q., Li, H.: MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)CrossRefGoogle Scholar
  21. 21.
    Zhang, Q., Liu, W., Tsang, E., Virginas, B.: Expensive multiobjective optimization by MOEA/d with Gaussian process model. IEEE Trans. Evol. Comput. 14(3), 456–474 (2010)CrossRefGoogle Scholar
  22. 22.
    Tan, Y., Jiao, Y., Li, H., Wang, X.: MOEA/D plus uniform design: a new version of MOEA/d for optimization problems with many objectives. Comput. Oper. Res. 40(6), 1648–1660 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Ma, X., Liu, F., Qing, Y., Gong, M., Yin, M., Li, L., Jiao, L., Wu, J.: MOEA/D with opposition-based learning for multiobjective optimization problem. Neurocomputing 146(146), 48–64 (2014)Google Scholar
  24. 24.
    Wang, Z., Zhang, Q., Zhou, A., Gong, M., Jiao, L.: Adaptive replacement strategies for MOEA/d. IEEE Trans. Cybern. 46(2), 474–486 (2016)CrossRefGoogle Scholar
  25. 25.
    Nebro, A.J., Durillo, J.J., Luna, F., Dorronsoro, B., Alba, E.: MOCEll: a cellular genetic algorithm for multiobjective optimization. Intern. J. Intell. Syst. 24(7), 726–746 (2009)CrossRefzbMATHGoogle Scholar
  26. 26.
    Chang, P.C., Chen, S.H.: The development of a sub-population genetic algorithm II (SPGA II) for multi-objective combinatorial problems. Appl. Soft Comput. 9(1), 173–181 (2009)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Bader, J., Zitzler, E.: Hype: an algorithm for fast hypervolume-based many-objective optimization. Evol. Comput. 19(1), 45–76 (2011)CrossRefGoogle Scholar
  28. 28.
    While, L., Bradstreet, L., Barone, L.: A fast way of calculating exact hypervolumes. IEEE Trans. Evol. Comput. 16(1), 86–95 (2012)CrossRefGoogle Scholar
  29. 29.
    Tiwari, S., Fadel, G., Deb, K.: AMGA2: Improving the performance of the archive-based micro-genetic algorithm for multi-objective optimization. Eng. Optim. 43(4), 377–401 (2011)CrossRefGoogle Scholar
  30. 30.
    Cagnina, L.C., Esquivel, S.C., Coello, C.A.C.: Solving constrained optimization problems with a hybrid particle swarm optimization algorithm. Eng. Optim. 43(8), 843–866 (2011)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Ke, L., Zhang, Q., Battiti, R.: MOEA/D-ACO: a multiobjective evolutionary algorithm using decomposition and ant colony. IEEE Trans. Cybern. 43(6), 1845–1859 (2013)CrossRefGoogle Scholar
  32. 32.
    Yang, X., Karamanoglu, M., He, X.: Flower pollination algorithm: a novel approach for multiobjective optimization. Eng. Optim. 46(9), 1222–1237 (2014)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Li, H., Zhang, Q.: Multiobjective optimization problems with complicated pareto sets, MOEA/d and NSGA-II. IEEE Trans. Evol. Comput. 13(2), 284–302 (2009)CrossRefGoogle Scholar
  34. 34.
    Liu, X., Peng, Z., Chang, Y., Zhang, L.: Multi-objective evolutionary approach for UAV cruise route planning to collect traffic information. J. Cent. South Univ. 19(12), 3614–3621 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Transportation and AutomotiveTianjin University of Technology and EducationTianjinChina
  2. 2.Department of Urban and Regional PlanningUniversity of FloridaGainesvilleUSA
  3. 3.Institute of High Performance ComputingA*STARSingaporeSingapore

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