[1]姜海猛,张志安,潘孝斌.基于 A * 与 DWA 算法的融合优化策略研究[J].机械与电子,2024,42(10):15-21.
　JIANG Haimeng,ZHANG Zhi an,PAN Xiaobin.Research on Fusion Optimization Strategy Based on A* and Dynamic Window Approach Algorithms[J].Machinery & Electronics,2024,42(10):15-21.
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基于 A * 与 DWA 算法的融合优化策略研究()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
42
期数:
2024年10期
页码:
15-21
栏目:
研究与设计
出版日期:
2024-10-30

文章信息/Info

Title:
Research on Fusion Optimization Strategy Based on A* and Dynamic Window Approach Algorithms
文章编号:
1001-2257 ( 2024 ) 10-0015-07
作者:
姜海猛; 张志安; 潘孝斌
南京理工大学机械工程学院,江苏 南京 210094
Author(s):
JIANG Haimeng ;  ZHANG Zhi ’an ;  PAN Xiaobin
( School of Mechanical Engineering , Nanjing University of Science and Technology , Nanjing 210094 , China )
关键词:
移动机器人; 路径规划; 动态权重因子; 改进 A * 算法; 融合算法
Keywords:
mobile robot ;  path planning ;  dynamic weighting factor ;  improved A* algorithm ;  fusion algorithm
分类号:
TP242 ; TP18
文献标志码:
A
摘要:
针对单独使用 A * 或 DWA 算法难以同时实现全局路径最优和动态避障的问题,提出了一种基
于 A * 算法与 DWA 算法的优化融合策略。通过引入环境复杂度动态权重因子,优化 A * 算法评价函数,提高算法的适应性;采用冗余点去除策略对 A * 算法生成的全局路径进行优化,以提高路径效率;考虑移动机器人周围环境状况,引入距离自适应系数对 DWA 算法的评价函数进行优化,提高了局部路径规划的性能;以优化后 A * 算法生成的全局路径中的关键节点作为 DWA 算法的临时目标点进行路径规划,实现全局路径最优化与实时避障的兼顾。最后,通过多组仿真实验验证了改进算法的可行性。
Abstract:
Aiming to address the challenge of achieving both global path optimality and dynamic obstacle avoidance when using A* or DWA algorithms individually , a novel optimization fusion strategy based on the integration of A* and DWA algorithms is proposed.The approach involves introducing a dynamic weight factor for environmental complexity to optimize the A* algorithm ’s evaluation function and enhance its adaptability.Redundant point removal strategy is employed to optimize the global path generated by the A* algorithm , thereby improving path efficiency.Considering the surrounding environment of the mobile robot , a distance-adaptive coefficient is introduced to optimize the evaluation function of the DWA algorithm , enhancing the performance of local path planning.The optimized key nodes from the A* algorithm’s generated global path are used as temporary target points for the DWA algorithm , achieving a balance between global path optimality and? -time obstacle avoidance.Finally , the feasibility of the improved algorithm is validated through multiple sets of simulation experiments.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2024-03-08
作者简介:姜海猛 ( 2000- ),男,河南开封人,硕士研究生,研究方向为移动机器人路径规划;张志安 ( 1979- ),男,江苏南京人,博士,副教授,研究方向为多编队机器人控制技术、飞行器控制和智能控制算法;潘孝斌 ( 1979- ),男,福建福鼎人,博士,副教授,研究方向为气压传动与控制、机械结构设计与分析、气动发动机。
更新日期/Last Update: 2024-10-30