Research on 3D layered visibility graph route network model and multi-objective path planning for UAVs in complex urban environments

作     者：Hu, Xiao-Bing Yang, Chang-Shu Zhou, Jun Zhang, Ying-Fei Ma, Yi-Ming 

作者机构：Civil Aviat Univ China Coll Safety Sci & Engn Lab Complex Syst Safety & Intelligent Decis Tianjin 300300 Peoples R China Civil Aviat Univ China Sino European Inst Aviat Engn Tianjin 300300 Peoples R China Beijing Union Univ Collaborat Innocat Ctr eTourism Beijing 100101 Peoples R China Univ Warwick Sch Engn Coventry CV4 7AL England Shanghai Aircraft Design & Res Inst Shanghai 201210 Peoples R China Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China 

出 版 物：《AEROSPACE SCIENCE AND TECHNOLOGY》 (Aerosp Sci Technol)

年 卷 期：2025年第159卷

核心收录：

学科分类：08[工学] 0825[工学-航空宇航科学与技术] 

基　　金：Fundamental Research Funds for the Central Universities of Ministry of Education of China (Civil Aviation University of China) 

主　　题：Urban air mobility (UAM) Unmanned aerial vehicles (UAVs) Route network design 3D visibility graph Multi-category multi-objective path planning Ripple spreading algorithm 

摘      要：Over the past decade, unmanned aerial vehicles (UAVs) have seen increased use in urban transportation, including applications such as package delivery and emergency rescue. However, the complex nature of urban environments poses significant challenges to the safe and efficient operation of UAVs. Path planning offers an effective solution for improving both flight efficiency and safety. This paper develops a novel flight path optimization method that accounts for the configuration of urban environments, as well as critical operational constraints, such as obstacle avoidance and flight altitude limitations. The urban environment is modeled as a 3D layered visibility graph network by deforming urban buildings to cuboid obstacles and representing the obstacles with discrete sampling points. To mitigate potential collision risks between UAVs, maneuvering protection zones (MPZs) are integrated into the network. The objective of this study is to simultaneously minimize path length and maximize the path confidence level, with the latter being a comprehensive assessment of safety, reliability, and public acceptance. To solve the proposed multi-category multi-objective path optimization problem, an improved ripple-spreading algorithm (RSA) is developed, which is theoretically proven to provide the exact and complete Pareto-optimal solution set. Simulation tests, performed on both artificially generated problems and the urban area of Shenzhen, China, demonstrate that the inclusion of MPZs reduces the computing time by 70% while increasing the average shortest path length by only 1%. In addition, compared to other existing algorithms, RSA demonstrates high efficiency in identifying the complete Pareto frontier in large-scale networks.