Three-dimensional multi-constraint route planning of unmanned aerial vehicle low-altitude penetration based on coevolutionary multi-agent genetic algorithm

Zhi Hong Peng*, Jin Ping Wu, Jie Chen

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

29 引用 (Scopus)
Plum Print visual indicator of research metrics
  • Citations
    • Citation Indexes: 29
  • Captures
    • Readers: 10
see details

摘要

To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned aerial vehicle (UAV) low-altitude penetration, a novel route planning method was proposed. First and foremost, a coevolutionary multi-agent genetic algorithm (CE-MAGA) was formed by introducing coevolutionary mechanism to multi-agent genetic algorithm (MAGA), an efficient global optimization algorithm. A dynamic route representation form was also adopted to improve the flight route accuracy. Moreover, an efficient constraint handling method was used to simplify the treatment of multi-constraint and reduce the time-cost of planning computation. Simulation and corresponding analysis show that the planning results of CE-MAGA have better performance on terrain following, terrain avoidance, threat avoidance (TF/TA 2) and lower route costs than other existing algorithms. In addition, feasible flight routes can be acquired within 2 s, and the convergence rate of the whole evolutionary process is very fast.

源语言英语
页(从-至)1502-1508
页数7
期刊Journal of Central South University of Technology (English Edition)
18
5
DOI
出版状态已出版 - 10月 2011

指纹

探究 'Three-dimensional multi-constraint route planning of unmanned aerial vehicle low-altitude penetration based on coevolutionary multi-agent genetic algorithm' 的科研主题。它们共同构成独一无二的指纹。

引用此

Peng, Z. H., Wu, J. P., & Chen, J. (2011). Three-dimensional multi-constraint route planning of unmanned aerial vehicle low-altitude penetration based on coevolutionary multi-agent genetic algorithm. Journal of Central South University of Technology (English Edition), 18(5), 1502-1508. https://doi.org/10.1007/s11771-011-0866-4