Real-time UAV Rerouting for Traffic Monitoring with Decomposition Based Multi-objective Optimization
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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.
KeywordsTraffic monitoring Unmanned aerial vehicle Real-time rerouting Multi-objective optimization
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- 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)
- 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.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.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)Google Scholar
- 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
- 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
- 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