Abstract
Motivated by the wide use of unmanned aerial vehicles (UAV) in search-and-rescue operations, we consider a problem of planning the search sequence and search modes of UAV, the aim of which is to maximize the probability of finding the target in a complex environment with probabilistic belief of target location. We design five meta-heuristic algorithm for solving the complex problem, but find that none of them can always obtain satisfactory solutions on a variety of instances. To overcome this obstacle, we integrate these meta-heuristics into a hyper-heuristic framework, which adaptively manage the low-level heuristics (LLH) by using feedback of their real-time performance in problem solving, and thus can find the most suitable LLH or their combination that can outperform any single LLH on each given instance. Experiments show that the overall performance of the hyper-heuristic is significantly better than any individual heuristic on the test instances.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bourgault, F., Göktogan, A., Furukawa, T., Durrant-Whyte, H.F.: Coordinated search for a lost target in a bayesian world. Adv. Robot. 18(10), 979–1000 (2004)
Burke, E., Hart, E., Kendall, G., Newall, J., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern research technolology. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 457–474. Kluwer, Dordrecht (2003)
Doherty, P., Rudol, P.: A UAV search and rescue scenario with human body detection and geolocalization. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 1–13. Springer, Heidelberg (2007). doi:10.1007/978-3-540-76928-6_1
Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Computer 22(6), 46–57 (1989)
Gemeinder, M., Gerke, M.: GA-based path planning for mobile robot systems employing an active search algorithm. Appl. Soft Comput. 3(2), 149–158 (2003)
Goodrich, M.A., Morse, B.S., Gerhardt, D., Cooper, J.L., Quigley, M., Adams, J.A., Humphrey, C.: Supporting wilderness search and rescue using a camera-equipped mini UAV. J. Field Robot. 25(1–2), 89–110 (2008)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge (1975)
Jin, Y., Liao, Y., Minai, A.A., Polycarpou, M.M.: Balancing search and target response in cooperative unmanned aerial vehicle (UAV) teams. IEEE Trans. Syst. Man Cybern. Part B 36(3), 571–587 (2005)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Li, C., Duan, H.: Target detection approach for UAVs via improved pigeon-inspired optimization and edge potential function. Aeros. Sci. Techn. 39, 352–360 (2014)
Liang, J.J., Qin, A.K., Suganthan, P., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)
Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: IEEE Congress on Evolutionary Computation, pp. 69–73 (1998)
Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)
Symington, A., Waharte, S., Julier, S., Trigoni, N.: Probabilistic target detection by camera-equipped UAVs. In: 2010 IEEE International Conference on Robotics and Automation, pp. 4076–4081 (2010)
Syswerda, G.: Schedule optimization using genetic algorithms. In: Davis, L. (ed.) Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)
Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010. LNCS, vol. 6145, pp. 355–364. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13495-1_44
Waharte, S., Symington, A., Trigoni, N.: Probabilistic search with agile UAVs. In: 2010 IEEE International Conference on Robotics and Automation, pp. 2840–2845 (2010)
Waharte, S., Trigoni, N.: Supporting search and rescue operations with UAVs. In: 2010 International Conference on Emerging Security Technologies, pp. 142–147 (2010)
Zheng, Y.J., Chen, S.Y.: Cooperative particle swarm optimization for multiobjective transportation planning. Appl. Intell. 39, 202–216 (2013)
Zheng, Y.J., Ling, H.F., Xue, J.Y., Chen, S.Y.: Population classification in fire evacuation: a multiobjective particle swarm optimization approach. IEEE Trans. Evol. Comput. 18(1), 70–81 (2014)
Zheng, Y.J.: Water wave optimization: a new nature-inspired metaheuristic. Comput. Oper. Res. 55(1), 1–11 (2015)
Zheng, Y.J., Chen, S.Y., Ling, H.F.: Evolutionary optimization for disaster relief operations: a survey. Appl. Soft Comput. 27, 553–566 (2015)
Acknowledgements
This work is supported by National Natural Science Foundation (Grant No. 61473263) and Zhejiang Provincial Natural Science Foundation (Grant No. LY14F030011) of China.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Wang, Y., Zhang, MX., Zheng, YJ. (2017). A Hyper-Heuristic Method for UAV Search Planning. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_48
Download citation
DOI: https://doi.org/10.1007/978-3-319-61833-3_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61832-6
Online ISBN: 978-3-319-61833-3
eBook Packages: Computer ScienceComputer Science (R0)