面向城市飞行安全的无人机离散型多路径规划方法

胡莘婷; 吴宇

doi:10.7527/S1000-6893.2020.24383

航空学报 >

2021 , Vol. 42 >Issue 6: 324383 - 324383

DOI: https://doi.org/10.7527/S1000-6893.2020.24383

电子电气工程与控制

面向城市飞行安全的无人机离散型多路径规划方法

胡莘婷 ,
吴宇

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1. 中国民航大学空中交通管理学院, 天津 300300;
2. 重庆大学航空航天学院, 重庆 400044

收稿日期: 2020-06-09

修回日期: 2020-07-27

网络出版日期: 2020-08-21

基金资助

重庆市自然科学基金（cstc2020jcyj-msxmX0602）；中央高校基本科研业务费（2020 CDJ-LHZZ-066）

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Risk-based discrete multi-path planning method for UAVs in urban environments

HU Xinting ,
WU Yu

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1. School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China;
2. College of Aerospace Engineering, Chongqing University, Chongqing 400044, China

Received date: 2020-06-09

Revised date: 2020-07-27

Online published: 2020-08-21

Supported by

Chongqing Research Program of Basic Research and Frontier Technology (cstc2020jcyj-msxmX0602); Fundamental Research Funds for the Central Universities (2020 CDJ-LHZZ-066)

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摘要

为了提高无人机（UAV）在城市环境中运行的安全性，且能生成多条备选路径，提出一种离散型城市环境下基于无人机飞行安全的多路径规划方法。根据定义的城市环境模型、无人机的飞行规则和安全性原则，建立无人机飞行安全性分析模型和离散型多路径规划问题的数学模型。为提高算法的收敛速度和解的优质性，以及使算法能够同时输出多条路径，针对蚁群（ACO）算法的运行机制，设计聚类算子，提出改进聚类蚁群（CIACO）算法。实验结果表明，所提方法能够快速的收敛输出多条风险值较低的飞行路径。

关键词： 无人机(UAV); 城市环境; 安全性分析; 多路径规划; 蚁群(ACO)算法

本文引用格式

胡莘婷 , 吴宇 . 面向城市飞行安全的无人机离散型多路径规划方法[J]. 航空学报, 2021 , 42(6) : 324383 -324383 . DOI: 10.7527/S1000-6893.2020.24383

Abstract

A risk-based Unmanned Aerial Vehicle (UAV) multi-path planning method is proposed to improve the safety of UAV operations and generate multiple alternative flight paths in discrete urban environments. According to the proposed model of urban environments, flight rules, and principles of safety, a safety analysis model and a multi-path planning model are established respectively. A Clustering Improved Ant Colony Optimization (CIACO) algorithm is employed to enhance algorithm performance on convergence rate and solution quality. In this algorithm, the multi-path planning problem is solved by designing a cluster mechanism and improving original Ant Colony Optimization (ACO) algorithm. Simulation studies show that the proposed method can generate multiple flight paths with low risks at fast convergence rates.

Key words： Unmanned Aerial Vehicle (UAV); urban environments; safety analysis; multi-path planning; Ant Colony Optimization (ACO) algorithm

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