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

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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.

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References

  1. Federal Aviation Administration. Unmanned aircraft systems [EB/OL]. https://www.faa.gov/uas/. Accessed: Dec 22 2016 (2016)

  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. 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)

    Article  Google Scholar 

  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)

  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)

  6. Zink, J., Lovelace, B.: Unmanned aerial vehicle bridge inspection demonstration project. Minnesota Department of Transportation, Minnesota (2015)

    Google Scholar 

  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)

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  15. Chen, H., Hsueh, C., Chang, M.: The real-time time-dependent vehicle routing problem. Transport. Res. Part E 42, 383–408 (2006)

    Article  Google Scholar 

  16. Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  20. Zhang, Q., Li, H.: MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  27. Bader, J., Zitzler, E.: Hype: an algorithm for fast hypervolume-based many-objective optimization. Evol. Comput. 19(1), 45–76 (2011)

    Article  Google Scholar 

  28. While, L., Bradstreet, L., Barone, L.: A fast way of calculating exact hypervolumes. IEEE Trans. Evol. Comput. 16(1), 86–95 (2012)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  32. Yang, X., Karamanoglu, M., He, X.: Flower pollination algorithm: a novel approach for multiobjective optimization. Eng. Optim. 46(9), 1222–1237 (2014)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Transportation and Automotive, Tianjin University of Technology and Education, Tianjin, 300222, China

    Xiaofeng Liu

  2. Department of Urban and Regional Planning, University of Florida, Gainesville, 32611, USA

    Zhong-Ren Peng

  3. Institute of High Performance Computing, A*STAR, Singapore, 138632, Singapore

    Li-Ye Zhang

Authors
  1. Xiaofeng Liu
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  2. Zhong-Ren Peng
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  3. Li-Ye Zhang
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Corresponding author

Correspondence to Xiaofeng Liu.

Additional information

This work was supported in part by the National Natural Science Foundation of China award 51408417, 61503284, and the Science and Technology Project of Tianjin award 17KPXMSF00010, 16PTGCCX00150,17ZXRGGX00070, and 16JCZDJC38200.

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Liu, X., Peng, ZR. & Zhang, LY. Real-time UAV Rerouting for Traffic Monitoring with Decomposition Based Multi-objective Optimization. J Intell Robot Syst 94, 491–501 (2019). https://doi.org/10.1007/s10846-018-0806-8

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  • DOI: https://doi.org/10.1007/s10846-018-0806-8

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