[2] CEKMEZ U, OZSIGINAN, and SAHINGOZ O K. Multi colony ant optimization for UAV path planning with obstacle[C]. International Conference on Unmanned Aircraft System, Piscataway, USA, 2016: 47-52.
|
XU Chunfang, DUAN Haiban, and LIU Fang. Chaotic artificial bee colony approach to uninhabited combat air vehicle (UCAV) path planning[J]. Aerospace Science Technology, 2010, 14(8): 535-541. doi: 10.1016/j.ast.2010-04- 008.
|
[3] ZHANG Daqiao, XIAN Yong, LI Jie, et al. UAV path planning based on chaos ant colony algorithm[C]. International Conference on Computer Science and Mechanical Automation, Hangzhou, China, 2015: 81-85.
|
[4] PEHLIVANOGLU Y V. A new vibrational genetic algorithm enhanced with a voronoi diagram for path planning of autonomous UAV[J]. Aerospace Science & Technology, 2015, 16(1): 47-55. doi: 10.1016/j.ast.2011.02.006.
|
LIU Zhen, SHI Jianguo, and GAO Xiaoguang. Application of voronoi diagram in flight path planning[J]. Acta Aeronauticaet Astronautica Sinica, 2008, 29(5): 15-19. doi: 1000-6893(2008)0S15-05.
|
[6] WU Qi, PAN Guangzhen, and YANG Jiangtao. Route planning of UAV based on voronoi diagram and dynamic and adaptive ant colony algorithm[J]. Computer Measurement and Control, 2016, 22(9): 3037-3041. doi: 1671-4598-(2014) 09-3037-04.
|
[7] KENNEGY J and EBERHART R. Particle swarm optimization[C]. Proceedings of the IEEE International Conference on Neural Networks. Piscataway, USA, 1995: 1942-1948.
|
[8] PENG Zhihong, LI Bo, CHEN Xiaotian, et al. Online route planning for UAV based on model predictive control and particle swarm optimization algorithm[C]. 10th World Congress on Intelligent Control and Automation, Piscataway, USA, 2015: 397-401.
|
[9] LI Shibo, SUN Xiuxia, and XU Yuejie. Particle Swarm optimization for route planning of unmanned air vehicles[C]. Proceedings of the Congress on Information Acquisition, Weihai, China, 2006: 1213-1218.
|
[10] FU Yangguang, DING Mingyue, and ZHOU Chengping. Routing planning for Unmanned Aerial Vehicle (UAV) on the sea using hybrid differential evolution and quantum-behaved particle swarm optimization[J]. IEEE Transactions on Systems, 2016, 43(6): 1451-1465. doi: 10.1109/TSMC.2013. 2248146.
|
HE Pei, QU Xiangju, and WU Zhe. Aircraft referenced flight path planning by using adaptive genetic algorithm[J]. Acta Aeronautica et Astronautica Sinica, 2003, 24(6): 499-502. doi: 1000-6893(2003)06-0499-04.
|
TIAN Jing, CHEN Yan, and SHEN Lincheng, Cooperative search algorithm for multi-UAVs in uncertainty environment [J]. Journal of Electronics & Information Technology, 2007, 29(10): 2325-2328. doi: 1009-5896(2007)10-2325-04.
|
[13] GLABOWSKI M, MUSZNICKI B, NOWAK P, et al. An algorithm for finding shortest path tree using ant colony optimization metaheuristic[J]. Advances in Intelligent Systems and Computing, 2014, 233: 317-326. doi: 10.1007 /978-3-319-016222-1-36.
|
[14] YAO Peng and WANG Honglun. Dynamic adaptive ant lion optimizer applied to route planning for unmanned aerial vehicle[J]. Soft Computing, 2016, 21(18): 5475-5488. doi: 10.1007/s00500- 016-2138-6.
|
ZHANG Shuai and LI Xueren. UAV 3D real-time path planning based on dynamic step[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(12): 2745-2753. doi: 10.13700/j.bh.1001-5965.2015.0821.
|
[16] MIRJALILII S. The ant lion optimizer[J]. Advances in Engineering Software, 2015, 83(C): 80-98. doi: 10.4028/www. scientific.net/AMM.834.187.
|
[17] YAO Pei. UAV path planning based on disturbed fluid and trajectory propagation[J]. Chinese Journal of Aeronautics, 2015, 28(4): 1163-1174. doi: 10.1016/j.neucom.2015.09.039.
|
[18] ALZUGARAY I, TEIXEIRA L, and CHLI M. Short-term UAV path-planning with monocular-inertial SLAM in the loop[C]. IEEE International Conference on Robotics & Automation, Singapore, 2017: 1705-1713.
|