Path Planning Research for Unmanned Air Vehicle Based on Improved Particle Swarm Algorithm

刘科; 周继强; 郭小和

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Path Planning Research for Unmanned Air Vehicle Based on Improved Particle Swarm Algorithm

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- Path Planning Research for Unmanned Air Vehicle Based on Improved Particle Swarm Algorithm
- 基于改进粒子群算法的无人机路径规划研究
- Vol. 34, Issue 4, Pages: 441-447(2013)
- 作者机构：
  
  1. 南昌航空大学飞行器工程学院
  2. 中航工业洪都航空工业集团650研究所
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2013
- 稿件说明：
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刘科, 周继强, 郭小和. Path Planning Research for Unmanned Air Vehicle Based on Improved Particle Swarm Algorithm[J]. 2013, 34(4): 441-447.
DOI：

刘科, 周继强, 郭小和. Path Planning Research for Unmanned Air Vehicle Based on Improved Particle Swarm Algorithm[J]. 2013, 34(4): 441-447. DOI：

摘要

将复杂网络理论应用于粒子群算法改进

提出了一种改进粒子群算法

通过自适应惯性系数方法自动调节惯性系数

增强算法的收敛性能.仿照Voronoi图理论

用可行路径网络结构图表示已知威胁分布情况

采用Dijkstra算法粗略搜索威胁分布图

求得粗略最短路径.在粗略最短路径的基础上

通过改进粒子群算法和最小二乘法拟合求解最优路径.用MATLAB进行了仿真验证

仿真结果表明了算法的有效性和无人机路径规划的合理性.

Abstract

The complex network theory was applied to the particle swarm algorithm and an improved particle swarm algorithm was proposed

enhancing algorithm's convergence performance by using adaptive inertia coefficient method automatically to adjust the inertia coefficient.Following Voronoi graph theory

feasible path network structure chart was used to express the distribution of known threat

Dijkstra algorithm was applied to roughly seek threat distribution to get rough shorest path

In this paper

according to rough shortest path

improved particle swarm algorithm and least squares method were used for optimizing the initial reference path.The Matlab simulation result demonstrates the algorithm effectiveness and UAV path planning rationality.

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references

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