The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection
Open accessAbstract
Abstract In built infrastructure monitoring, an efficient path planning algorithm is essential for robotic inspection of large surfaces using computer vision. In this work, we first formulate the inspection path planning problem as an extended travelling salesman problem (TSP) in which both the coverage and obstacle avoidance were taken into account. An enhanced discrete particle swarm optimization (DPSO) algorithm is then proposed to solve the TSP, with performance improvement by using deterministic initialization, random mutation, and edge exchange. Finally, we take advantage of parallel computing to implement the DPSO in a GPU-based framework so that the computation time can be significantly reduced while keeping the hardware requirement unchanged. To show the effectiveness of the proposed algorithm, experimental results are included for datasets obtained from UAV inspection of an office building and a bridge.
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Cites
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
Hart, P., Nilsson, N., & Raphael, B. (1968) · IEEE Transactions on Systems Science and Cybernetics · doi:10.1109/tssc.1968.300136 · Cited by 12,738
Ant colony system: a cooperative learning approach to the traveling salesman problem
Dorigo, M., & Gambardella, L. M. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585892 · Cited by 8,081
Cited by
Otto, A., Agatz, N., Campbell, J., Golden, B., & Pesch, E. (2018) · Networks · doi:10.1002/net.21818 · Cited by 985
“The resulting planning problems can be modeled as generalized versions of one of the basic routing problems, such as the TSP [214, 239], the multiple TSP [8, 19, 25, 163, 222, 266, 286, 288], or the VRP [96, 125, 133, 154, 213].”
BackgroundChung, S. H., Sah, B., & Lee, J. (2020) · Computers & Operations Research · doi:10.1016/j.cor.2020.105004 · Cited by 441
A novel hybrid grey wolf optimizer algorithm for unmanned aerial vehicle (UAV) path planning
Qu, C., Gai, W., Zhang, J., & Zhong, M. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105530 · Cited by 282
UAV Path Planning Using Optimization Approaches: A Survey
Ait Saadi, A., Soukane, A., Meraihi, Y., Benmessaoud Gabis, A., Mirjalili, S., & Ramdane-Cherif, A. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09742-7 · Cited by 269
Metadata
- Authors
- Phung, M. D., Quach, C. H., Dinh, T. H., & Ha, Q.
- Venue
- Automation in Construction
- Publisher
- Elsevier BV
- Published
- 2017-09
- Volume
- 81
- Pages
- 25-33
- ISSN
- 0926-5805 (ISSN-L)
- OpenAlex
- W2620253406
- Cited by
- 278
- References
- 2
- FWCI
- 12.79
- Type
- journal article
- Language
- en
- Open access
- green
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- Phung, M. D., Quach, C. H., Dinh, T. H., & Ha, Q. (2017). Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection. Automation in Construction. https://doi.org/10.1016/j.autcon.2017.04.013
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