基于改进鲸鱼优化算法的无人机航路规划

doi:10.7527/S1000-6893.2020.24286

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基于改进鲸鱼优化算法的无人机航路规划

吴坤^1,2, 谭劭昌¹

1. 北京航空航天大学飞行学院, 北京 100083;
2. 北京航空航天大学先进无人飞行器北京高精尖学科中心, 北京 100083

收稿日期:2020-05-26 修回日期:2020-06-03 发布日期:2020-06-18
通讯作者: 吴坤 E-mail:wuukun@buaa.edu.cn
基金资助:
航空科学基金（20185851021）

Path planning of UAVs based on improved whale optimization algorithm

WU Kun^1,2, TAN Shaochang¹

1. Flying College, Beihang University, Beijing 100083, China;
2. Beijing Advanced Discipline Center for Unmanned Aircraft System, Beihang University, Beijing 100083, China

Received:2020-05-26 Revised:2020-06-03 Published:2020-06-18
Supported by:
Aeronautical Science Foundation of China (20185851021)

摘要/Abstract

摘要： 针对复杂地形环境下的无人机航路规划问题，提出一种基于改进的鲸鱼优化算法的航路规划算法。首先，根据起始点和目标点等信息，通过坐标系旋转将二维航路规划问题转化为D维空间下的寻优问题；然后，将灰狼优化算法中的等级制度和微分进化算法中的贪婪策略引入鲸鱼优化算法提出改进的鲸鱼优化算法。在保证算法收敛速度的同时，所提的改进鲸鱼优化算法有效地提高了开发能力和搜索能力。最后，将提出的改进算法应用于无人机的航路问题求解。仿真结果表明，所提的改进鲸鱼优化算法能够有效的获得一条代价最优的、有效的航路结果，其性能优于传统的优化算法。

关键词: 航路规划, 无人机, 鲸鱼优化算法, 等级制度, 贪婪策略

Abstract: A path planning method for Unmanned Aerial Vehicles (UAVs) in complex terrain environment is proposed based on the Improved Whale Optimization Algorithm (IWOA). First, according to information of the starting point and target point, the two-dimensional path planning problem is transformed into the optimization problem in the D-dimensional space by the rotating coordinate system. Then, a novel hybrid algorithm called IWOA is proposed by combining the hierarchy of the Gray Wolf Optimization algorithm (GWO) and the greedy strategy of the Differential Evolution algorithm (DE) into the Whale Optimization Algorithm (WOA). While ensuring the convergence speed, the IWOA efficiently improves the exploration and exploitation abilities. Finally, the improved algorithm is applied to the path planning of UAVs. The simulation results show that the IWOA can effectively obtain a cost optimal and effective path result, with better performance than the traditional optimization algorithm.

Key words: path planning, UAVs, whale optimization algorithm, hierarchy, greedy strategy

中图分类号:

V249

吴坤, 谭劭昌. 基于改进鲸鱼优化算法的无人机航路规划[J]. 航空学报, 2020, 41(S2): 724286-724286.

WU Kun, TAN Shaochang. Path planning of UAVs based on improved whale optimization algorithm[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(S2): 724286-724286.

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E-mail：hkxb@buaa.edu.cn

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于改进鲸鱼优化算法的无人机航路规划

Path planning of UAVs based on improved whale optimization algorithm

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