无人机集群巡检道路的航线规划与分布式机场选址方法

叶深文; 张钢; 罗志勇

doi:10.12052/gdutxb.220131

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无人机集群巡检道路的航线规划与分布式机场选址方法

综合研究 | 更新时间：2026-05-27

- 无人机集群巡检道路的航线规划与分布式机场选址方法
- Route Planning and Distributed Airport Site Selection Method for UAV Swarm Road Inspection
- 广东工业大学学报 2023年40卷第5期页码：64-72
- 作者机构：
  
  1. 广东工业大学自动化学院,广东,广州,510006
  2. 广州市优飞信息科技有限公司,广东,广州,510630
- 作者简介：
  
  叶深文(1998–) ，男，硕士研究生，主要研究方向为无人机航线规划
  张钢(1979–) ，男，讲师，博士，主要研究方向为优化计算方法，E-mail：FLOATGANG@163.com
- 基金信息：
  
  国家自然科学基金资助项目(61975248);广州市科技计划项目(202007040004)
- DOI：10.12052/gdutxb.220131
  中图分类号： V249
- 纸质出版：2023
- 稿件说明：
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叶深文, 张钢, 罗志勇. 无人机集群巡检道路的航线规划与分布式机场选址方法[J]. 广东工业大学学报, 2023, 40(5): 64-72.

Ye Shen-wen, Zhang Gang, Luo Zhi-yong. Route Planning and Distributed Airport Site Selection Method for UAV Swarm Road Inspection[J]. 2023, 40(5): 64-72.
叶深文, 张钢, 罗志勇. 无人机集群巡检道路的航线规划与分布式机场选址方法[J]. 广东工业大学学报, 2023, 40(5): 64-72. DOI： 10.12052/gdutxb.220131.

Ye Shen-wen, Zhang Gang, Luo Zhi-yong. Route Planning and Distributed Airport Site Selection Method for UAV Swarm Road Inspection[J]. 2023, 40(5): 64-72. DOI： 10.12052/gdutxb.220131.

摘要

无人机集群巡检道路过程中普遍存在无航线规划或规划困难、无人机利用率不均衡、难以确定分布式机场地址等问题。为此，本文首先构建了巡检地图，去除地图中与道路巡检无关的多余信息。其次，将航线规划和机场选址统一在多目标优化的框架中。第三，提出了融合航线优化与机场选址的粒子编码方法、更新规则和解码方法。第四，提出了较为全面的评价指标，用于评价航线规划及机场选址的效果。实验结果表明：(1) 采用本文的方法，优化后的巡检航线重复部分低于总里程的7%，无人机利用均衡率在75%以上。(2) 优化后

分布式机场的重复利用率得到显著提高。可见，本文提出的方法能较好地规划无人机道路巡检任务的航线，均衡无人机的使用，选择较佳位置作为分布式机场的地址。这为构建道路巡检的全自主无人机集群巡检系统奠定了基础。

Abstract

In the process of UAV swarm road inspection

there are many problems such as difficulty in UAV route planning

unbalanced utilization of UAVs

and difficulty in determining the distributed airport site. In response

firstly

an inspection map is built to remove the redundant information irrelevant to road inspection. Secondly

route planning and airport site selection are unified in the framework of multi-objective optimization. Thirdly

the particle coding method

particle update rules and particle decoding method are proposed which combine route optimization and airport site selection. Fourthly

several evaluation indexes are proposed comprehensively to evaluate the effect of route planning and airport site selection. The experimental results show that: (1) By this method

the mileage of repeated part in optimized inspection route is less than 7% of the total mileage

and the UAV utilization balance rate is more than 75%. (2) After optimization

the reuse rate of distributed airports has been significantly improved. It shows that the method proposed in this research can plan the route for UAV road inspection task preferably

balance the utilization of UAV

and select a better location as the distributed airport site. It provides a firm foundation for autonomous UAV swarm road inspection system.

关键词

Keywords

references

BICICI S, ZEYBEK M. An approach for the automated extraction of road surface distress from a UAV-derived point cloud [J]. Automation in Construction, 2021, 122: 1-15.

ZEYBEK M, BICICI S. Road distress measurements using UAV [J]. Turkish Journal of Remote Sensing and GIS, 2020, 1(1): 13-23.

SAAD A M, TAHAR K N. Identification of rut and pothole by using multirotor unmanned aerial vehicle (UAV) [J]. Measurement, 2019, 137: 647-654.

DUHAYYIM M A, OBAYYA M, ALWESABI F N, et al . Energy aware data collection with route planning for 6G enabled UAV communication [J ] . Computers, Materials Continua, 2022, 71(1): 825-842.

LEE M T, CHUANG M L, KUO S T, et al . UAV swarm real-time rerouting by edge computing D* Lite algorithm [J ] . Applied Science, 2022, 12(3): 1056.

WANG K, WU Q Q, HE X T, et al . Optimizing UAV traffic monitoring routes during rush hours considering spatiotemporal variation of monitoring demand [J ] . International Journal of Geographical Informat ion Science, 2022, 36(10): 2086-2111.

ZHOU Y M, SU Y, XIE A H, et al . A newly bio-inspired path planning algorithm for autonomous obstacle avoidance of UAV [J ] . Chinese Journal of Aeronautics, 2021, 34(9): 199-209.

CHO S W, PARK J H, PARK H J, et al . Multi-UAV coverage path planning based on hexagonal grid decomposition in maritime search and rescue [J ] . Mathematics, 2022, 10(1): 83.

PHALAPANYAKOON K, SIRIPONGWUTIKORN P. Route planning of unmanned aerial vehicles under recharging and mission time constraints [J]. International Journal of Mathematical Engineering and Management Sciences, 2021, 6(5): 1439-1459.

XIE J, CHEN J. Multiregional coverage path planning for multiple energy constrained UAVs [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(10): 17366-17381.

ZHANG H, DOU L H, CAI C X, et al . Three-dimensional unmanned aerial vehicle route planning using hybrid differential evolution [J ] . Journal of Advanced Computational Intelligence and Intelligent Informatics, 2020, 24(7): 820-828.

ALVES C J P, SILVA E J, MULLER C, et al . Towards an objective decision-making framework for regional airport site selection [J ] . Journal of Air Transport Management, 2020, 89: 101888.

ERKAN T E, ELSHARIDA W M. Combining AHP and ROC with GIS for airport site selection: a case study in Libya [J]. ISPRS International Journal of Geo-Information, 2020, 9(5): 312.

AYDIN N, SEKER S. WASPAS based multimoora method under IVIF environment for the selection of hub location [J]. Journal of Enterprise Information Management, 2020, 33(5): 1233-1256.

ZHAO B, WANG N, FU Q, et al . Searching a site for a civil airport based on bird ecological conservation: an expert-based selection (Dalian, China) [J ] . Global Ecology and Conservation, 2019, 20: e00729.

HAN B, QU T T, HUANG Z L, et al . Emergency airport site selection using global subdivision grids [J ] . Big Earth Data, 2021, 6(3): 276-293.

LIAO Y, BAO F. Research on airport site selection based on triangular fuzzy number [J]. Applied Mechanics and Materials, 2014, 2973(505-506): 507-511.

WANG Z Z. Robot path planning based on improved particle swarm algorithm [J]. Manufacturing Technology Machine Tool, 2018(2): 150-154.

CHEN J H, QIU M J, GUO J T, et al . Multi-objective reactive power optimization in electric power system with wind farm based on fuzzy entropy weight method and CCPSO algorithm [J ] . Journal of Guangdong University of Technology, 2018, 35(1): 35-40.

MA X, LIU D, HU J X. Particle swarm optimization for solving problem of k -shortest paths via designated points [J ] . Computer Engineering and Applications, 2019, 55(20): 89-94.

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