Safety inspection path of unmanned aerial vehicle

Document Type : Research Paper

Authors

1 School of Business Jiangnan University Jiangsu Wuxi China

2 School of Business, Jiangnan University Jiangsu Wuxi China

Abstract

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.

Keywords

Main Subjects

References

[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
Iranian Journal of Fuzzy Systems
Volume 21, Issue 5
September and October 2024
Pages 11-30

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