
تعداد نشریات | 33 |
تعداد شمارهها | 770 |
تعداد مقالات | 7,474 |
تعداد مشاهده مقاله | 12,463,616 |
تعداد دریافت فایل اصل مقاله | 8,475,983 |
Safety inspection path of unmanned aerial vehicle | ||
Iranian Journal of Fuzzy Systems | ||
دوره 21، شماره 5، آذر و دی 2024، صفحه 11-30 اصل مقاله (881.26 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22111/ijfs.2024.47905.8431 | ||
نویسندگان | ||
Zi-hong Huang1؛ Yong Liu* 2؛ Chang-cheng Ji1 | ||
1School of Business Jiangnan University Jiangsu Wuxi China | ||
2School of Business, Jiangnan University Jiangsu Wuxi China | ||
چکیده | ||
Unmanned aerial vehicle (UAV) safety inspection is a developing technology that offers the benefits of high efficiency, low cost, and freedom from dangerous areas and unique situations. An urgent fundamental issue in the deployment of UAV in factories is how to successfully strike a balance between the effectiveness and cost of UAV safety inspection. In view of this, we build a route planning model for UAV inspection. And then, by using the path planning of UAV safety inspection as the research object, based on the two important evaluation indicators of cost and efficiency, we exploit fuzzy time window and adaptive genetic algorithms to design the solution algorithm. Finally, a case verifies the applicability and logic of the proposed model. The results show that the proposed path optimization model with fuzzy time window can reasonably pass all inspection points under balanced conditions, and the hybrid genetic algorithm has good optimization ability. | ||
کلیدواژهها | ||
Unmanned aerial vehicle؛ path planning؛ fuzzy time window؛ genetic algorithm | ||
مراجع | ||
[1] A. Al-Kaff, A. Madridano, S. Campos, et al, Emergency support unmanned aerial vehicle for forest fire surveillance, Electronics, 9(2) (2020), 260. https://doi.org/10.3390/electronics9020260 [2] M. N. Alpdemir, Tactical UAV path optimization under radar threat using deep reinforcement learning, Neural Computing and Applications, 34(7) (2022), 5649-5664. https://doi.org/10.1007/s00521-021-06702-3 [3] S. Aslan, T. Erkin, A multi-population immune plasma algorithm for path planning of unmanned combat aerial vehicle, Advanced Engineering Informatics, 55 (2023), 101829. https://doi.org/10.1016/j.aei.2022.101829 [4] Q. Chen, H. Y. Cui, Visual inspection model of UAV cluster based on improved pigeon flock hierarchy, Journal of System Simulation, 34(6) (2022), 1275-1285. https://doi.org/10.16182/j.issn1004731x.joss.21-1121 [5] A. Chowdhury, D. De, RGSO-UAV: Reverse glowworm swarm optimization inspired UAV path-planning in a 3D dynamic environment, Ad Hoc Networks, 140 (2023), 103068. https://doi.org/10.1016/j.adhoc.2022.103068 [6] X. Cui, Y. Wang, S. Yang, et al, UAV path planning method for data collection of fixed-point equipment in complex forest environment, Frontiers in Neurorobotics, 16 (2022), 1105177. https://doi.org/10.3389/fnbot.2022. 1105177 [7] H. B. Demir, E. P. ¨ Ozmen, S. Esnaf, Time-windowed vehicle routing problem: Tabu search algorithm approach, Advances in Distributed Computing and Artificial Intelligence Journal, 11(2) (2022), 179-189. https://doi.org/ 10.14201/adcaij.27533 [8] G. A. Fern´andez, E. Lalla-Ruiz, S. M. G´omez, et al, The cumulative vehicle routing problem with time windows: Models and algorithm, Annals of Operations Research, (2023), 1-29. https://doi.org/10.1007/s10479-022-05102-7 [9] F. Ge, K. Li, Y. Han, et al, Path planning of UAV for oilfield inspections in a three-dimensional dynamic environment with moving obstacles based on an improved pigeon-inspired optimization algorithm, Applied Intelligence, 50 (2020), 2800-2817. https://doi.org/10.1007/s10489-020-01650-2 [10] B. Han, T. Qu, X. Tong, et al, Grid-optimized UAV indoor path planning algorithms in a complex environment, International Journal of Applied Earth Observation and Geoinformation, 111 (2022), 102857. https://doi.org/ 10.1016/j.jag.2022.102857 [11] M. Hoogeboom, W. Dullaert, D. Lai, et al, Efficient neighborhood evaluations for the vehicle routing problem with multiple time windows, Transportation Science, 54(2) (2020), 400-416. https://doi.org/10.1287/trsc.2019. 0912 [12] X. Li, Z. J. Li, H. Z. Wang, et al, Unmanned aerial vehicle for transmission line inspection: Status, standardization, and perspectives, Frontiers in Energy Research, 9 (2021), 713634. https://doi.org/10.3389/fenrg.2021.713634 [13] Y. Li, M. Liu, Path planning of electric VTOL UAV considering minimum energy consumption in urban areas, Sustainability, 14(20) (2022), 13421. https://doi.org/10.3390/su142013421 [14] J. Li, Y. Xiong, J. She, et al, A path planning method for sweep coverage with multiple UAVs, IEEE Internet of Things Journal, 7(9) (2020), 8967-8978. https://doi.org/10.1109/JIOT.2020.2999083 [15] J. C. Liao, L. B. Cao, W. Li, et al, UnetDVH-Linear: Linear feature segmentation by dilated convolution with vertical and horizontal kernels, Sensors, 20(20) (2020), 5759. https://doi.org/10.3390/s20205759 [16] Y. Lu, D. Macias, Z. S. Dean, et al, A UAV-mounted whole cell biosensor system for environmental monitoring applications, IEEE Transactions on Nanobioscience, 14(8) (2015), 811-817. https://doi.org/10.1109/TNB.2015. 2478481 [17] H. ¨ Oztop, D. Kizilay, Z. A. C¸ il, Mathematical models for the periodic vehicle routing problem with time windows and time spread constraints, An International Journal of Optimization and Control: Theories and Applications, 11(1) (2021), 10-23. https://doi.org/10.11121/ijocta.01.2021.00899 [18] M. D. Phung, Q. P. Ha, Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization, Applied Soft Computing, 107 (2021), 107376. https://doi.org/10.1016/j.asoc.2021.107376 [19] Y. Shen, Y. Zhu, H. Kang, et al, UAV path planning based on multi-stage constraint optimization, Drones, 5(4) (2021), 144. https://doi.org/10.3390/drones5040144 [20] Y. Sui, P. F. Ning, P. J. Niu, et al, Review on mounted UAV for transmission line inspection, Power System Technology, 45(9) (2021), 3636-3648. https://doi.org/10.13335/j.1000-3673.pst.2020.1178 [21] X. Wang, J. S. Pan, Q. Yang, et al, Modified mayfly algorithm for UAV path planning, Drones, 6(5) (2022), 134. https://doi.org/10.3390/drones6050134 [22] Y. Xu, J. Li, F. Zhang, A UAV-based forest fire patrol path planning strategy, Forests, 13(11) (2022), 1952. https://doi.org/10.3390/f13111952 [23] C. Xu, M. Xu, C. Yin, Optimized multi-UAV cooperative path planning under the complex confrontation environment, Computer Communications, 162 (2020), 196-203. https://doi.org/10.1016/j.comcom.2020.04.050 [24] J. Yang, X. Huang, Intelligent planning modeling and optimization of UAV cluster based on multi-objective optimization algorithm, Electronics, 11(24) (2022), 4238. https://doi.org/10.3390/electronics11244238
[25] E. Yanmaz, Joint or decoupled optimization: Multi-UAV path planning for search and rescue, Ad Hoc Networks, 138 (2023), 103018. https://doi.org/10.1016/j.adhoc.2022.103018 [26] X. Yu, C. Li, J. F. Zhou, A constrained differential evolution algorithm to solve UAV path planning in disaster scenarios, Knowledge-Based Systems, 204 (2020), 106209. https://doi.org/10.1016/j.knosys.2020.106209 [27] G. Yue, Y. T. Pan, Intelligent inspection of marine disasters based on UAV intelligent vision, Journal of Coastal Research, 93 (2019), 410-416. https://doi.org/10.2112/SI93-054.1 [28] J. Zhang, H. L. Huang, Occlusion-aware UAV path planning for reconnaissance and surveillance, Drones, 5(3) (2021), 98. https://doi.org/10.3390/drones5030098 [29] W. Zhang, H. Li, W. Yang, et al, Hybrid multiobjective evolutionary algorithm considering combination timing for multi-type vehicle routing problem with time windows, Computers and Industrial Engineering, 171 (2022), 108435. https://doi.org/10.1016/j.cie.2022.108435 [30] X. Zhang, S. Xia, X. Li, et al, Multi-objective particle swarm optimization with multi-mode collaboration based on reinforcement learning for path planning of unmanned air vehicles, Knowledge-Based Systems, 250 (2022), 109075. https://doi.org/10.1016/j.knosys.2022.109075 [31] X. Zhang, S. Xia, T. Zhang, et al, Hybrid FWPS cooperation algorithm based unmanned aerial vehicle constrained path planning, Aerospace Science and Technology, 118(1) (2021), 107004. https://doi.org/10.1016/j.ast.2021. 107004 [32] L. Zhao, L. Yan, X. Hu, et al, Efficient and high path quality autonomous exploration and trajectory planning of UAV in an unknown environment, ISPRS International Journal of Geo-Information, 10(10) (2021), 631. https: //doi.org/10.3390/ijgi10100631 | ||
آمار تعداد مشاهده مقاله: 110 تعداد دریافت فایل اصل مقاله: 168 |