Improved A<sup>*</sup> ant colony algorithm for robot path planning problem solving

WAN Yifei; PENG li

doi:10.13873/j.1000-9787(2019)12-0153-04

Chinese

您当前的位置：

首页 >

文章列表页 >

Improved A^* ant colony algorithm for robot path planning problem solving

更新时间：2025-09-11

- Improved A^* ant colony algorithm for robot path planning problem solving
- Issue 12, Pages: 153-156+160(2019)
- 作者机构：
  
  江南大学物联网应用技术教育部工程中心
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-9787(2019)12-0153-04
  CLC： TP18;TP242
- Published：2019
- 稿件说明：
移动端阅览
WAN Yifei, PENG li. Improved A^* ant colony algorithm for robot path planning problem solving[J]. 2019, (12): 153-156+160.
DOI：

WAN Yifei, PENG li. Improved A^* ant colony algorithm for robot path planning problem solving[J]. 2019, (12): 153-156+160. DOI： 10.13873/j.1000-9787(2019)12-0153-04.

摘要

针对蚁群算法收敛速度慢、效率低以及易陷入局部最优的一系列问题

提出改进的A

蚁群算法。为降低蚁群死锁、停滞的概率

先将栅格地图进行处理。其次为了提高蚁群的效率

引进A

算法确定蚁群的初始信息素

同时改进蚁群信息素更新方式

从而提高算法的收敛速度;针对局部最优的问题

提出将蚁群中的启发函数进行改进

不仅考虑到可行栅格中的最短距离

还考虑到目标点的位置

并且引入简化算子对蚁群的路径进行优化。通过4组仿真对比

改进的A

蚁群算法效果显著。

Abstract

An improved A

ant colony algorithm is proposed

aiming at a series of problems such as slow convergence rate

low efficiency and easy to be trapped in local optimum. In order to reduce the probability of colony deadlock and stagnation

the raster map is processed first. Secondly

in order to improve the efficiency of ant colony

algorithm is introduced to determine the initial pheromone of ant colony

and the pheromone update mode is improved

so as to improve the convergence speed of the algorithm. Aiming at the problem of local optimum

improving heuristic function of ant colony is proposed

not only consider shortest distance of the possible raster

but also consider the target position

and introduce simplified ope

rator for optimization of the path of ant colony. Through the comparison of four groups of simulation

the improved A

ant colony algorithm has a remarkable effect.

关键词

Keywords

references

Views

703

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Optimized multi-step ant colony algorithm for robot path planning problem solving

Enhanced egret swarm optimization algorithm for robot path planning

Path planning for guide robot based on improved bitterling fish optimization algorithm

Improved RRT algorithm for manipulator path planning based on dynamic probability sampling

Multi-objective 3D UAV motion planning in WSN based on particle swarm optimization

Related Author

HU Jiabin

WANG Xiangshu

ZHANG Qi

QUAN Ruikun

ZHAO Zhengjun

HU Likun

LIU Qingqing

WU Hengfei

Related Institution

CQU-UC Joint Co-op Institute, Chongqing University

School of Electrical Engineering, Chongqing University

School of Electrical Engineering,Guangxi University

2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET),Nanjing University of Information Science & Technology

School of Automation,Nanjing University of Information Science & Technology

⁰

Improved A* ant colony algorithm for robot path planning problem solving

DOI：10.13873/j.1000-9787(2019)12-0153-04

摘要

Abstract

关键词

Keywords

references

Improved A^* ant colony algorithm for robot path planning problem solving