[1]王秀芬,杨盛毅.基于优化的元胞蚁群算法的无人飞行器动态路径规划方法[J].机械与电子,2018,(12):69-72.
　WANG Xiufen,YANG Shengyi.Dynamic Path Planning for UAV Based on Optimized Cellular Ant Colony Algorithm[J].Machinery & Electronics,2018,(12):69-72.
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基于优化的元胞蚁群算法的无人飞行器动态路径规划方法()
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机械与电子[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2018年12期
页码:
69-72
栏目:
智能工程
出版日期:
2018-12-24

文章信息/Info

Title:
Dynamic Path Planning for UAV Based on Optimized Cellular Ant Colony Algorithm
文章编号:
1001-2257(2018)12-0069-04
作者:
王秀芬; 杨盛毅
(贵州民族大学数据科学与信息工程学院,贵州 贵阳 550025)
Author(s):
WANG Xiufen; YANG Shengyi
(School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025，China)
关键词:
元胞蚁群算法; 信息素; 圈形轨迹识别算法; 尖角优化策略
Keywords:
ant colony algorithm;  pheromone; loop trajectory identification algorithm; sharp angle optimization strategy
分类号:
TP18; TP24
文献标志码:
A
摘要:
针对无人飞行器路径规划蚁群算法收敛速度慢，提出了一种新的蚁群算法。首先，以栅格为环境地图，在蚁群算法搜索过程中加入了圈形轨迹识别算法，避免了无人飞行器出现折返跑的现象。其次，采用最优路径进行信息素更新，减少了蚁群算法在搜索过程中产生的盲目交叉和“蚂蚁遗失”现象。最后，引入了无人飞行器轨迹的尖角优化策略，更好地模拟了无人飞行器的飞行特征。仿真实验表明，新算法具有更好的全局搜索能力。
Abstract:
In view of the slow convergence rate of path planning ant colony algorithm for UAV, a new ant colony method was proposed. Firstly, using the grid Instead of the environmental map, the loop trajectory identification algorithm was added in the search process of ant colony algorithm, which avoids the phenomenon of reentry run of unmanned aerial vehicle. Secondly, the optimal path was used to update pheromone which reduces the blind crossover and "ant loss" in the search process. Finally, the sharp angle optimization strategy of UAV was introduced, and the flight characteristics of UAV were simulated better. Simulation experiments show that the optimal path obtained by the new algorithm has better global search ability.

参考文献/References:

[1]杨盛毅,柳阳阳,杨伟力.一种未知环境下的局部动态概率路线图法[J].航空科学技术,2016，27(4):69-73.
[2]刘洲洲.基于改进人工势场法的智能无人车路径规划仿真研究[J].计算技术与自动化,2013,32(2):133-136.
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[4]曾明如,徐小勇，刘亮，等.改进的势场蚁群算法的移动机器人路径规划[J].计算机工程与应用,2015,51(22):33-37.
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[1]王 铁,胡 泓.基于 Ｋ- ｍｅａｎｓ信息挥发速率动态调整的 改进蚁群算法[J].机械与电子,2020,(02):25.
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备注/Memo

备注/Memo:
收稿日期：2018-08-01
基金项目: 贵州省教育厅自科(黔教合KY字[2015]428)；贵州省科学技术基金(黔科合J字LKM[2012]09，黔科合LH字[2016]7081)
作者简介：王秀芬(1983-)，女，山东聊城人，助理实验师，硕士研究生，研究方向为智能控制,路径规划。
更新日期/Last Update: 2019-10-29