广东工业大学学报 ›› 2023, Vol. 40 ›› Issue (05): 64-72.doi: 10.12052/gdutxb.220131

• 综合研究 • 上一篇    

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1, 张钢1, 罗志勇2   

  1. 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
  • 收稿日期:2022-08-26 发布日期:2023-09-26
  • 通信作者: 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
  • 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
  • 基金资助:
    国家自然科学基金资助项目(61975248)；广州市科技计划项目(202007040004)

Route Planning and Distributed Airport Site Selection Method for UAV Swarm Road Inspection

Ye Shen-wen1, Zhang Gang1, Luo Zhi-yong2   

  1. 1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
    2. Guangzhou Ufly Information Technology Co., Ltd., Guangzhou 510630, China
  • Received:2022-08-26 Published:2023-09-26

HTML

PDF (PC)

490

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。

关键词: 无人机自主巡检, 分布式机场, 选址方法, 航线规划, 粒子群算法

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.

Key words: autonomous UAV inspection, distributed airports, site selection method, route planning, particle swarm optimization algorithm

中图分类号: 

  • V249
[1] 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.
[2] ZEYBEK M, BICICI S. Road distress measurements using UAV [J]. Turkish Journal of Remote Sensing and GIS, 2020, 1(1): 13-23.
[3] SAAD A M, TAHAR K N. Identification of rut and pothole by using multirotor unmanned aerial vehicle (UAV) [J]. Measurement, 2019, 137: 647-654.
[4] 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.
[5] 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.
[6] 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 Information Science, 2022, 36(10): 2086-2111.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 王志中. 基于改进粒子群算法的机器人路径规划[J]. 制造技术与机床, 2018(2): 150-154.
WANG Z Z. Robot path planning based on improved particle swarm algorithm [J]. Manufacturing Technology & Machine Tool, 2018(2): 150-154.
[19] 陈璟华, 邱明晋, 郭经韬, 等. 模糊熵权法和CCPSO算法的含风电场电力系统多目标无功优化[J]. 广东工业大学学报, 2018, 35(1): 35-40.
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.
[20] 马炫, 刘栋, 胡家鑫. 求解必经点k条最优路径问题的粒子群优化算法[J]. 计算机工程与应用, 2019, 55(20): 89-94.
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.
[1] 胡晓敏, 许万森, 段宇晖, 李敏. 基于智能优化的协作治疗调度算法[J]. 广东工业大学学报, 2023, 40(05): 21-33.
[2] 陈思豆, 黄卓铨, 杨兴雨. 考虑限制性卖空的多期模糊投资组合优化模型[J]. 广东工业大学学报, 2021, 38(02): 39-47.
[3] 吴丹琦, 赖俊升, 杨俊华, 李学聪, 赖来利, 熊锋俊. 基于局部粒子群算法的家庭用电负荷优化控制策略[J]. 广东工业大学学报, 2019, 36(06): 66-73.
[4] 唐俊杰, 陈璟华, 邱明晋. 基于动态模糊混沌粒子群算法的含电动汽车微电网多目标优化调度研究[J]. 广东工业大学学报, 2018, 35(03): 100-106.
[5] 姚蕾. 一种粒子群-Mamdani模糊神经网络的参数优化新算法[J]. 广东工业大学学报, 2014, 31(1): 36-39.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2017 《广东工业大学学报》编辑部
地址：广州市东风东路729号广东工业大学 邮编：510090
电话：020-37626653,020-37626139,020-37626396
邮箱：xbzrb@gdut.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发