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Real-time Unmanned Aerial Vehicle Cruise Route Optimization for Road Segment Surveillance using Decomposition Algorithm

Published online by Cambridge University Press:  14 September 2020

Xiaofeng LiuOpen the ORCID record for Xiaofeng Liu[Opens in a new window],
Jian Ma,
Dashan Chen and
Li-Ye Zhang
Xiaofeng Liu*
Affiliation:
School of Automotive and Transportation, Tianjin University of Technology and Education, Tianjin 300222, China
Jian Ma
Affiliation:
School of Civil Engineering, Suzhou University of Science and Technology, Suzhou 215011, Jiangsu, China E-mail: 9764634@qq.com
Dashan Chen
Affiliation:
School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China E-mail: logtop@126.com
Li-Ye Zhang
Affiliation:
Institute of High Performance Computing, A*STAR, Singapore 138632, Singapore E-mail: zhangly@ihpc.astar.edu.sg
*
*Corresponding author. E-mail: microbreeze@126.com
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Summary

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Unmanned aerial vehicle (UAV) was introduced for nondeterministic traffic monitoring, and a real-time UAV cruise route planning approach was proposed for road segment surveillance. First, critical road segments are defined so as to identify the visiting and unvisited road segments. Then, a UAV cruise route optimization model is established. Next, a decomposition-based multi-objective evolutionary algorithm (DMEA) is proposed. Furthermore, a case study with two scenarios and algorithm sensitivity analysis are conducted. The analysis result shows that DMEA outperforms other two commonly used algorithms in terms of calculation time and solution quality. Finally, conclusions and recommendations on UAV-based traffic monitoring are presented.

Keywords

Unmanned aerial vehicleTraffic monitoringRoute planningMulti-objective optimizationRoad segment

Information

Type
Article
Information
Robotica , Volume 39 , Issue 6 , June 2021, pp. 1007 - 1022
Copyright
© The Author(s), 2020. Published by Cambridge University Press

References

Watts, A. C., Ambrosia, V. G. and Hinkley, E. A., “Unmanned aircraft systems in remote sensing and scientific research: classification and considerations of use,” Remote Sens. 4(6), 1671–1692 (2012).CrossRefGoogle Scholar
Civil Aviation Administration of China, Air Traffic Control Regulation of Civil Unmanned Aerial Vehicle Systems, Beijing, China (2016).Google Scholar
Xu, Y., Yu, G., Wu, X., Wang, Y. and Ma, Y., “An enhanced viola-jones vehicle detection method from unmanned aerial vehicles imagery,” IEEE Trans. Intell. Transp. Syst. 18(7), 1–12, (2016).Google Scholar
Zhang, L., Peng, Z., Sun, D. J. and Liu, X., “A UAV-Based Automatic Traffic Incident Detection System for Low Volume Roads,” Transportation Research Board of the National Academies (National Research Council, Washington DC, 2013) pp. 542–558.Google Scholar
Dobson, R., Colling, T., Brooks, C., Roussi, C., Watkins, M. and Dean, D., “Collecting decision support system data through remote sensing of unpaved roads,” Transp. Res. Record J. Transp. Res. Board 2433, 108–115 (2014).CrossRefGoogle Scholar
Seo, J., Duque, L. and Wacker, J., “Drone-enabled bridge inspection methodology and application,” Autom. Const. 94, 112–126 (2018).CrossRefGoogle Scholar
Coifman, B., Beymer, D., Mclauchlan, P. and Malik, J., “A real-time computer vision system for vehicle tracking and traffic surveillance,” Transp. Res. Part C Emerg. Tech. 6(4), 271–288 (1998).CrossRefGoogle Scholar
Puri, A., A Survey of Unmanned Aerial Vehicles (UAV) for Traffic Surveillance (Department of Computer Science and Engineering, University of South Florida, 2005) pp. 1–29.Google Scholar
Romanoni, A., Mussone, L., Rizzi, D. and Matteucci, M., “A comparison of two Monte Carlo algorithms for 3D vehicle trajectory reconstruction in roundabouts,” Pattern Recogn. Lett. 51(C), 79–85 (2015).CrossRefGoogle Scholar
Liu, X., Zhao, L., Peng, Z., Gao, T. and Geng, J., “Use of Unmanned Aerial Vehicle and Imaging System for Accident Scene Reconstruction,” Transportation Research Board of the National Academies ( National Research Council, Washington DC, 2017) pp. 432–450.Google Scholar
Peng, Q., Peng, Z. and Chang, Y., “Unmanned Aerial Vehicle Cruise Route Optimization Model for Sparse Road Network,” Transportation Research Board of the National Academies ( National Research Council, Washington DC, 2011) pp. 432–445.Google Scholar
Ryan, J. L., Bailey, T. G., Moore, J. T. and Carlton, W. B., “Reactive Tabu Search in Unmanned Aerial Reconnaissance Simulations,” Simulation Conference Proceedings, Washington, DC, USA, vol. 871 (1998) pp. 873–879.Google Scholar
Zhang, J., Jia, L., Niu, S., Zhang, F., Tong, L. and Zhou, X., “A space-time network-based modeling framework for dynamic unmanned aerial vehicle routing in traffic incident monitoring applications,” Sensors 15(6), 13874–13898 (2015).CrossRefGoogle ScholarPubMed
Liu, X., Peng, Z., Chang, Y. and Zhang, L., “Multi-objective evolutionary approach for UAV cruise route planning to collect traffic information,” J. Central South Univ. 19(12), 3614–3621 (2012).CrossRefGoogle Scholar
Pehlivanoglu, Y. V., “A new vibrational genetic algorithm enhanced with a Voronoi diagram for path planning of autonomous UAV,” Aerospace Sci. Tech. 16(1), 47–55 (2012).CrossRefGoogle Scholar
Chen, Y., Luo, G., Mei, Y., Yu, J. and Su, X., “UAV path planning using artificial potential field method updated by optimal control theory,” Int. J. Syst. Sci. 47(6), 1407–1420 (2016).CrossRefGoogle Scholar
Ragi, S. and Chong, E. K. P., “UAV path planning in a dynamic environment via partially observable Markov decision process,” IEEE Trans. Electr, Aerospace. Syst. 49(4), 2397–2412 (2013).Google Scholar
Wang, J., Zhang, Y., Geng, L., Fuh, J. Y. H. and Teo, S. H., “A heuristic mission planning algorithm for heterogeneous tasks with heterogeneous UAVs,” Unmanned Syst. 3(3), 1–15 (2015).Google Scholar
Ozalp, N., Ayan, U. and Oztop, E., “Cooperative Multi-task Assignment for Heterogonous UAVs,” International Conference on Advanced Robotics, vol. 64(7) (2015) pp. 599–604.Google Scholar
Phung, M. D., Quach, C. H., Dinh, T. H. and Ha, Q., “Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection,” Autom. Const. 81, 25–33 (2017).CrossRefGoogle Scholar
Boveiri, H. R., “An incremental ant colony optimization based approach to task assignment to processors for multiprocessor scheduling,” Front. Inf. Tech. Electr. Eng. 18(4), 498–510 (2017).CrossRefGoogle Scholar
Novoa-Hernandez, P., Corona, C. C. and Pelta, D. A., “Self-adaptation in dynamic environments-a survey and open issues,” Int. J. Bio-Inspired Comput. 8(1), 1–13 (2015).CrossRefGoogle Scholar
Kim, M. H., Baik, H. and Lee, S., “Response threshold model based UAV search planning and task allocation,” J. Intell. Robot. Syst. 75(3), 625–640 (2014).CrossRefGoogle Scholar
Liu, X., Guan, Z., Song, Y. and Chen, D., “An optimization model of UAV route planning for road segment surveillance,” J. Central South Univ. 21(6), 2501–2510 (2014).CrossRefGoogle Scholar
Srinivas, N. and Deb, K., “Multiobjective optimization using nondominated sorting in genetic algorithms,” Evol. Comput. 2(3), 221–248 (1994).CrossRefGoogle Scholar
Knowles, J. D. and Corne, D. W., “The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Pareto Multiobjective Optimization,” Proceedings of the 1999 Congress on Evolutionary Computation, Washington DC (IEEE, 1999) pp. 98–105.Google Scholar
Zitzler, E. and 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
Deb, K., Agrawal, S., Pratap, A. and Meyarivan, T., “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput. 6(2), 182–197 (2002).CrossRefGoogle Scholar
Zhang, Q. and Li, H., “MOEA/D: A multiobjective evolutionary algorithm based on decomposition,” IEEE Trans. Evol. Comput. 11(6), 712–731 (2007).CrossRefGoogle Scholar
Tan, Y., Jiao, Y., Li, H. and Wang, X., “MOEA/D plus uniform design: A new version of MOEA/D for optimization problems with many objectives,” Comput. Operat. Res. 40(6), 1648–1660 (2013).CrossRefGoogle Scholar
Chang, P. C. and 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).CrossRefGoogle Scholar
While, L., Bradstreet, L. and Barone, L., “A fast way of calculating exact hypervolumes,” IEEE Trans. Evol. Comput. 16(1), 86–95 (2012).CrossRefGoogle Scholar
Li, D., Li, K., Liang, J. and Ouyang, A., “A hybrid particle swarm optimization algorithm for load balancing of MDS on heterogeneous computing systems,” Neurocomputing 330, 380–393 (2019).CrossRefGoogle Scholar
Engin, O. and Guclu, A., “A new hybrid ant colony optimization algorithm for solving the no-wait flow shop scheduling problems,” Appl. Soft Comput. 72, 166–176 (2018).CrossRefGoogle Scholar
Ke, L., Zhang, Q. and Battiti, R., “MOEA/D-ACO: A multiobjective evolutionary algorithm using decomposition and ant colony,” IEEE Trans. Cybern. 43(6), 1845–1859 (2013).CrossRefGoogle Scholar
Li, H. and 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
Liu, X., Gao, L., Guan, Z. and Song, Y., “A multi-objective optimization model for planning unmanned aerial vehicle cruise route,” Int. J. Adv. Robot. Syst. 13, 1–8 (2016).CrossRefGoogle Scholar