Abstract
The use of unmanned aerial vehicles (UAVs) is becoming more commonplace in search-and-rescue tasks, but UAV search planning can be very complex due to limited response time, large search area, and multiple candidate search modes. In this paper, we present a UAV search planning problem where the search area is divided into a set of subareas and each subarea has a prior probability that the target is present in it. The problem aims to determine the search sequence of the subareas and the search mode for each subarea to maximize the probability of finding the target. We propose an adaptive memetic algorithm that combines a genetic algorithm with a set of local search procedures and dynamically determines which procedure to apply based on the past performance of the procedures measured in fitness improvement and diversity improvement during problem-solving. Computational experiments show that the proposed algorithm exhibits competitive performance compared to a set of state-of-the-art global search heuristics, non-adaptive memetic algorithms, and adaptive memetic algorithms on a wide set of problem instances.
摘要
无人机在搜救任务中的应用日益广泛, 然而由于响应时间有限、 搜索区域广、 搜索模式多样, 无人机搜索规划也更加复杂. 本文提出一类无人机搜索规划问题, 其搜索区域被划分为一组子区域, 且每个子区域中目标存在的先验概率已知. 解决该问题需要确定这些子区域的搜索顺序以及每个子区域的搜索模式, 使得最终搜索成功的概率最大化. 提出一种自适应文化基因算法, 它结合了遗传算法和一组邻域搜索策略, 基于问题求解过程中的适应度提升和多样性提升指标, 动态选择邻域搜索策略. 在多个问题实例上的计算实验表明, 与先进的全局搜索启发式算法以及非自适应文化基因算法相比, 所提算法展现了出色性能.
Similar content being viewed by others
References
Al-Helal H, Sprinkle J, 2010. UAV search: maximizing target acquisition. Proc 17th IEEE Int Conf and Workshops on Engineering of Computer Based Systems, p.9–18. https://doi.org/10.1109/ECBS.2010.9
Altshuler Y, Yanovsky V, Wagner IA, et al., 2008. Efficient cooperative search of smart targets using UAV swarms. Robotica, 26(4):551–557. https://doi.org/10.1017/S0263574708004141
Bertuccelli LF, How JP, 2006. UAV search for dynamic targets with uncertain motion models. Proc 45th IEEE Conf on Decision and Control, p.5941–5946. https://doi.org/10.1109/CDC.2006.377010
Bourgault F, Furukawa T, Durrant-Whyte HF, 2006. Optimal search for a lost target in a Bayesian world. In: Yuta S, Asama H, Prassler E, et al. (Eds.), Field and Service Robotics: Recent Advances in Research and Applications. Springer, Berlin, Heidelberg, p.209–222. https://doi.org/10.1007/10991459_21
Burcin Ozsoydan F, Sağir M, 2021. Iterated greedy algorithms enhanced by hyper-heuristic based learning for hybrid flexible flowshop scheduling problem with sequence dependent setup times: a case study at a manufacturing plant. Comput Oper Res, 125:105044. https://doi.org/10.1016/j.cor.2020.105044
Cabassi F, Locatelli M, 2016. Computational investigation of simple memetic approaches for continuous global optimization. Comput Oper Res, 72:50–70. https://doi.org/10.1016/j.cor.2016.01.015
Chak CK, Feng G, 1995. Accelerated genetic algorithms: combined with local search techniques for fast and accurate global search. Proc IEEE Int Conf on Evolutionary Computation, p.378–383. https://doi.org/10.1109/ICEC.1995.489177
Chandler P, Rasmussen S, Pachter M, 2000. UAV cooperative path planning. Proc AIAA Guidance, Navigation, and Control Conf and Exhibit, p.1–11. https://doi.org/10.2514/6.2000-4370
Dong ZN, Chen ZJ, Zhou R, et al., 2011. A hybrid approach of virtual force and A* search algorithm for UAV path re-planning. Proc 6th IEEE Conf on Industrial Electronics and Applications, p.1140–1145. https://doi.org/10.1109/ICIEA.2011.5975758
Du YC, Zhang MX, Ling HF, et al., 2019. Evolutionary planning of multi-UAV search for missing tourists. IEEE Access, 7:73480–73492. https://doi.org/10.1109/ACCESS.2019.2920623
Duan HB, Li P, 2014. Bio-inspired Computation in Unmanned Aerial Vehicles. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41196-0
Eiben AE, Smit SK, 2011. Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm Evol Comput, 1(1):19–31. https://doi.org/10.1016/j.swevo.2011.02.001
Goodrich MA, Morse BS, Gerhardt D, et al., 2008. Supporting wilderness search and rescue using a camera-equipped mini UAV. J Field Robot, 25(1–2):89–110. https://doi.org/10.1002/rob.20226
Hansen SR, McLain TW, Goodrich MA, 2007. Probabilistic searching using a small unmanned aerial vehicle. Proc AIAA Infotech@Aerospace Conf and Exhibit, p.1–16. https://doi.org/10.2514/6.2007-2740
Hu CH, Xia Y, Zhang JG, 2019. Adaptive operator quantum-behaved pigeon-inspired optimization algorithm with application to UAV path planning. Algorithms, 12(1):3. https://doi.org/10.3390/a12010003
Hutter F, Hoos HH, Leyton-Brown K, et al., 2009. ParamILS: an automatic algorithm configuration framework. J Artif Intell Res, 36:267–306. https://doi.org/10.1613/jair.2861
Jin Y, Liao Y, Minai AA, et al., 2006. Balancing search and target response in cooperative unmanned aerial vehicle (UAV) teams. IEEE Trans Syst Man Cybern Part B Cybern, 36(3):571–587. https://doi.org/10.1109/TSMCB.2005.861881
Kamrani F, Ayani R, 2009. UAV Path Planning in Search Operations. INTECH Open Access Publisher, Rijeka, Croatia.
Krasnogor N, Smith J, 2005. A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Trans Evol Comput, 9(5):474–488. https://doi.org/10.1109/TEVC.2005.850260
Lai XJ, Hao JK, 2016. A tabu search based memetic algorithm for the max-mean dispersion problem. Comput Oper Res, 72:118–127. https://doi.org/10.1016/j.cor.2016.02.016
Li W, Yang BW, Song GH, et al., 2021. Dynamic value iteration networks for the planning of rapidly changing UAV swarms. Front Inform Technol Electron Eng, 22(5): 687–696. https://doi.org/10.1631/FITEE.1900712
Lin J, 2019. Backtracking search based hyper-heuristic for the flexible job-shop scheduling problem with fuzzy processing time. Eng Appl Artif Intell, 77:186–196. https://doi.org/10.1016/j.engappai.2018.10.008
Lin L, Goodrich MA, 2009. UAV intelligent path planning for wilderness search and rescue. Proc IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.709–714. https://doi.org/10.1109/IROS.2009.5354455
López-Ibáñez M, Dubois-Lacoste J, Pérez Cáceres L, et al., 2016. The irace package: iterated racing for automatic algorithm configuration. Oper Res Persp, 3:43–58. https://doi.org/10.1016/j.orp.2016.09.002
López-Ortiz A, Maftuleac D, 2015. Optimal strategies for search and rescue operations with robot swarms. https://arxiv.org/abs/1410.1077v1
Moscato P, Cotta C, 2003. A gentle introduction to memetic algorithms. In: Glover F, Kochenberger GA (Eds.), Handbook of Metaheuristics. Springer, Boston, USA, p.105–144. https://doi.org/10.1007/0-306-48056-5_5
Murphy RR, Tadokoro S, Nardi D, et al., 2008. Search and rescue robotics. In: Siciliano B, Khatib O (Eds.), Springer Handbook of Robotics. Springer, Berlin, Heidelberg, p.1151–1173. https://doi.org/10.1007/978-3-540-30301-5_51
Nikolos IK, Zografos ES, Brintaki AN, 2007. UAV path planning using evolutionary algorithms. In: Chahl JS, Jain LC, Mizutani A, et al. (Eds.), Innovations in Intelligent Machines 1. Springer, Berlin, Heidelberg, p.77–111. https://doi.org/10.1007/978-3-540-72696-8_4
Ong YS, Lim MH, Zhu N, et al., 2006. Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern Part B Cybern, 36(1):141–152. https://doi.org/10.1109/TSMCB.2005.856143
Özcan E, Drake JH, Altintas C, et al., 2016. A self-adaptive Multimeme Memetic Algorithm co-evolving utility scores to control genetic operators and their parameter settings. Appl Soft Comput, 49:81–93. https://doi.org/10.1016/j.asoc.2016.07.032
Phung MD, Ha QP, 2021. Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization. Appl Soft Comput, 107:107376. https://doi.org/10.1016/j.asoc.2021.107376
Qi YT, Hou ZT, Li H, et al., 2015. A decomposition based memetic algorithm for multi-objective vehicle routing problem with time windows. Comput Oper Res, 62:61–77. https://doi.org/10.1016/j.cor.2015.04.009
Qin AK, Huang VL, Suganthan PN, 2009. Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput, 13(2):398–417. https://doi.org/10.1109/TEVC.2008.927706
Ragi S, Chong EKP, 2013. UAV path planning in a dynamic environment via partially observable Markov decision process. IEEE Trans Aerosp Electron Syst, 49(4):2397–2412. https://doi.org/10.1109/TAES.2013.6621824
Ruan WY, Duan HB, 2020. Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization. Front Inform Technol Electron Eng, 21(5):740–748. https://doi.org/10.1631/FITEE.2000066
Ryan A, Hedrick JK, 2005. A mode-switching path planner for UAV-assisted search and rescue. Proc 44th IEEE Conf on Decision and Control, p.1471–1476. https://doi.org/10.1109/CDC.2005.1582366
Sheng WG, Chen SY, Sheng MM, et al., 2016. Adaptive multi-subpopulation competition and multiniche crowding-based memetic algorithm for automatic data clustering. IEEE Trans Evol Comput, 20(6):838–858. https://doi.org/10.1109/TEVC.2016.2524555
Syswerda G, 1991. Schedule optimization using genetic algorithms. In: Davis LD (Ed.), Handbook of Genetic Algorithms. van Nostrand Reinhold, New York, USA.
Tisdale J, Kim Z, Hedrick JK, 2009. Autonomous UAV path planning and estimation. IEEE Robot Autom Mag, 16(2):35–42. https://doi.org/10.1109/MRA.2009.932529
Tomlin JA, 1971. Technical note—an improved branch-and-bound method for integer programming. Oper Res, 19(4):1070–1075. https://doi.org/10.1287/opre.19.4.1070
Turky A, Sabar NR, Dunstall S, et al., 2020. Hyper-heuristic local search for combinatorial optimisation problems. Knowl-Based Syst, 205:106264. https://doi.org/10.1016/j.knosys.2020.106264
van Willigen WH, Schut MC, Eiben AE, et al., 2011. Online adaptation of path formation in UAV search-and-identify missions. Proc Int Conf on Adaptive and Natural Computing Algorithms, p.186–195. https://doi.org/10.1007/978-3-642-20267-4_20
Volgenant T, Jonker R, 1982. A branch and bound algorithm for the symmetric traveling salesman problem based on the 1-tree relaxation. Eur J Oper Res, 9(1):83–89. https://doi.org/10.1016/0377-2217(82)90015-7
Waharte S, Trigoni N, 2010. Supporting search and rescue operations with UAVs. Proc Int Conf on Emerging Security Technologies, p.142–147. https://doi.org/10.1109/EST.2010.31
Waharte S, Symington A, Trigoni N, 2010. Probabilistic search with agile UAVs. Proc IEEE Int Conf on Robotics and Automation, p.2840–2845. https://doi.org/10.1109/ROBOT.2010.5509962
Wang XP, Tang LX, 2017. A machine-learning based memetic algorithm for the multi-objective permutation flowshop scheduling problem. Comput Oper Res, 79:60–77. https://doi.org/10.1016/j.cor.2016.10.003
Wang Y, Zhang MX, Zheng YJ, 2017. A hyper-heuristic method for UAV search planning. Proc Int Conf on Swarm Intelligence, p.454–464 https://doi.org/10.1007/978-3-319-61833-3_48
Yu XB, Li CL, Yen GG, 2021. A knee-guided differential evolution algorithm for unmanned aerial vehicle path planning in disaster management. Appl Soft Comput, 98:106857. https://doi.org/10.1016/j.asoc.2020.106857
Zhang B, Duan HB, 2014. Predator-prey pigeon-inspired optimization for UAV three-dimensional path planning. Proc Int Conf on Swarm Intelligence, p.96–105. https://doi.org/10.1007/978-3-319-11897-0_12
Zhang H, Xin B, Dou LH, et al., 2020. A review of cooperative path planning of an unmanned aerial vehicle group. Front Inform Technol Electron Eng, 21(12):1671–1694. https://doi.org/10.1631/FITEE.2000228
Zhang YZ, Mei Y, Tang K, et al., 2017. Memetic algorithm with route decomposing for periodic capacitated arc routing problem. Appl Soft Comput, 52:1130–1142. https://doi.org/10.1016/j.asoc.2016.09.017
Zheng YJ, 2015. Water wave optimization: a new nature-inspired metaheuristic. Comput Oper Res, 55:1–11. https://doi.org/10.1016/j.cor.2014.10.008
Zheng YJ, Ling HF, Xue JY, 2014. Ecogeography-based optimization: enhancing biogeography-based optimization with ecogeographic barriers and differentiations. Comput Oper Res, 50:115–127. https://doi.org/10.1016/j.cor.2014.04.013
Zheng YJ, Zhang MX, Ling HF, et al., 2015. Emergency railway transportation planning using a hyper-heuristic approach. IEEE Trans Intell Transp Syst, 16(1):321–329. https://doi.org/10.1109/TITS.2014.2331239
Zheng YJ, Du YC, Ling HF, et al., 2020. Evolutionary collaborative human-UAV search for escaped criminals. IEEE Trans Evol Comput, 24(2):217–231. https://doi.org/10.1109/TEVC.2019.2925175
Zheng YJ, Du YC, Su ZL, et al., 2021. Evolutionary human-UAV cooperation for transmission network restoration. IEEE Trans Ind Inform, 17(3):1648–1657. https://doi.org/10.1109/TII.2020.3003903
Zhou R, Feng Y, Di B, et al., 2020. Multi-UAV cooperative target tracking with bounded noise for connectivity preservation. Front Inform Technol Electron Eng, 21(10):1494–1503. https://doi.org/10.1631/FITEE.1900617
Author information
Authors and Affiliations
Contributions
Libin HONG and Yujun ZHENG designed the research. Yue WANG processed the data. Yue WANG and Yichen DU developed the algorithm. Yichen DU drafted the paper. Xin CHEN and Yujun ZHENG revised and finalized the paper.
Corresponding author
Ethics declarations
Libin HONG, Yue WANG, Yichen DU, Xin CHEN, and Yujun ZHENG declare that they have no conflict of interest.
Additional information
Project supported by the National Natural Science Foundation of China (Nos. 61872123 and 61473263) and the Zhejiang Provincial Natural Science Foundation, China (No. LR20F030002)
Rights and permissions
About this article
Cite this article
Hong, L., Wang, Y., Du, Y. et al. UAV search-and-rescue planning using an adaptive memetic algorithm. Front Inform Technol Electron Eng 22, 1477–1491 (2021). https://doi.org/10.1631/FITEE.2000632
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.2000632