Journal of Guangdong University of Technology ›› 2023, Vol. 40 ›› Issue (05): 64-72.doi: 10.12052/gdutxb.220131

• Comprehensive Studies • Previous Articles    

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

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

CLC Number: 

  • 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] Tang Jun-jie, Chen Jing-hua, Qiu Ming-jin. Multi-objective Dispatch of Microgrid Based on Dynamic Fuzzy Chaotic Particle Swarm Algorithm [J]. Journal of Guangdong University of Technology, 2018, 35(03): 100-106.
[2] Chen Jing-hua, Qiu Ming-jin, Tang Jun-jie, Tian Ming-zheng, Tan Geng-rui. A Hybrid Algorithm Based on Improved Differential Evolution and Particle Swarm Optimization for Power System Optimal Power Flow Calculation [J]. Journal of Guangdong University of Technology, 2017, 34(05): 22-28.
[3] Guo Jingtao, Chen Jinghua, Zhou Jun, Xu Weilong. Reactive Power Optimization Based on Combined Chaotic #br# Dynamic Particle Swarm Optimization Algorithm#br#  [J]. Journal of Guangdong University of Technology, 2014, 31(2): 85-89.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!