Global Dynamic Path Planning Based on Fusion of Improved A<sup>*</sup> Algorithm and Dynamic Window Approach

doi:10.7652/xjtuxb201711019

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Global Dynamic Path Planning Based on Fusion of Improved A^* Algorithm and Dynamic Window Approach

更新时间：2025-07-09

- Global Dynamic Path Planning Based on Fusion of Improved A^* Algorithm and Dynamic Window Approach
- Vol. 51, Issue 11, Pages: 137-143(2017)
- 作者机构：
  
  信息工程大学导航与空天目标工程学院,郑州,450001
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201711019
  CLC： TP242.6
- Online First：10 November 2017，
  
  Published：2017
- 稿件说明：
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Global Dynamic Path Planning Based on Fusion of Improved A^* Algorithm and Dynamic Window Approach[J]. 2017, 51(11): 137-143. DOI: 10.7652/xjtuxb201711019.
DOI：

Global Dynamic Path Planning Based on Fusion of Improved A^* Algorithm and Dynamic Window Approach[J]. 2017, 51(11): 137-143. DOI: 10.7652/xjtuxb201711019. DOI：

摘要

针对移动机器人路径规划全局最优、实时避障的需求

提出了一种融合改进A

算法和动态窗口法的全局动态路径规划方法。首先

基于传统A

算法

结合Manhattan和Euclidean距离

设计了一种优化的启发搜索函数; 然后

利用关键点选取策略

剔除冗余路径点和不必要的转折点; 最后

融合动态窗口法

构造了顾及全局最优路径的评价函数

基于该评价函数

应用动态窗口法

进行实时动态路径规划

在保证规划路径全局最优性的基础上

提高了平滑性及路径规划的局部避障能力。实验结果表明:与传统A

算法相比

所提算法规划的路径更平滑

可实时动态避障

且能输出控制参数

这利于机器人的自动控制; 与动态窗口法相比

所提算法能够保证规划路径的全局最优性

路径长度由28.879 m缩短为22.285 m。该研究对于移动机器人自主导航的应用具有重要的参考价值。

Abstract

To meet the requirements of global optimal and real-time obstacle avoidance in mobile robot path planning

a novel method based on the fusion of improved A

algorithm and dynamic window approach is proposed. Combining Manhattan distance with Euclidean distance

a more appropriate heuristic function is designed for A

algorithm. Then a key node culling scheme is introduced into the traditional A

algorithm to remove the redundant nodes. An evaluation function considering globally optimal path is constructed. The dynamic window approach based on the evaluation function is applied to perform real-time dynamic path planning to guarantee the sm

oothness of path and the local obstacle avoidance ability as holding the global optimality of path. The experimental results demonstrate that compared with traditional A

algorithm

the smoother path is found

the ability of dynamic obstacle avoidance is more obvious and the control parameters for robots are obtained. The proposed method outperforms traditional dynamic window approach in guaranteeing the global optimality of path planning

and the path distance reduces from 28.879 m to 22.285 m.

关键词

Keywords

references

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Global Dynamic Path Planning Based on Fusion of Improved A* Algorithm and Dynamic Window Approach

DOI：10.7652/xjtuxb201711019

摘要

Abstract

关键词

Keywords

references

Global Dynamic Path Planning Based on Fusion of Improved A^* Algorithm and Dynamic Window Approach