| Autor: |
CAO Zhengling, CHENG Liangliang, SUN Bin, LI Haoyan, WANG Huawei, YIN Zhimin |
| Jazyk: |
čínština |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Zhejiang dianli, Vol 42, Iss 8, Pp 84-91 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1007-1881 |
| DOI: |
10.19585/j.zjdl.202308011 |
| Popis: |
For the problem of autonomous inspection path planning of UAVs （unmanned aerial vehicles） on overhead lines， considering the time cost of access target of UAVs， the geometric mean of the line patrol time interval and the defect level is used as the benefit of visiting nodes， which is equivalently extended to the OP （orientation problem） optimization model， in order to achieve the goal of obtaining greater benefits within a certain time limit. Then， an AGA （adaptive genetic algorithm） based on LA （learning automata） mechanism to improve PSO （particle swarm optimization） is proposed to solve the optimal solution of the proposed model in the benchmark instance and verify the validity of the proposed model. Finally， taking the autonomous inspection task of UAVs on overhead lines as the application background， the stability of the proposed model and algorithm is validated， which provides a reference for the formulation of path planning strategy of autonomous inspection for UAVs. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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