基于改进蚁群-动态窗口融合算法的室内服务机器人路径规划

马伟杰; 姚舜才; 马敏毓; 蔚鹏飞

doi:10.11959/j.issn.2096-6652.ZNKX26031

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基于改进蚁群-动态窗口融合算法的室内服务机器人路径规划

学术论文 | 更新时间：2026-07-29

- 基于改进蚁群-动态窗口融合算法的室内服务机器人路径规划
- Path planning for indoor service robots using an improved ACO-DWA hybrid algorithm
- 智能科学与技术学报 2026年8卷第2期页码：219-236
- 作者机构：
  
  中北大学电气与控制工程学院，山西太原 030000
- 作者简介：
  
  马伟杰（2001- ），男，中北大学电气与控制工程学院硕士生，主要研究方向为机器人路径规划算法。
  姚舜才（1974- ），男，博士，中北大学电气与控制工程学院副教授，主要研究方向为电机电磁场分析、电机与电器技术、非线性系统动态建模、统计学习与智能控制。yaoshuncai@nuc.edu.cn
  马敏毓（2001- ），女，中北大学电气与控制工程学院硕士生，主要研究方向为图像处理与深度学习。
  蔚鹏飞（2001- ），男，中北大学电气与控制工程学院硕士生，主要研究方向为智能机器人导航、目标识别与抓取。
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.ZNKX26031
  中图分类号： TP242.6
- 收稿：2026-03-11，
  
  修回：2026-06-17，
  
  录用：2026-06-27，
  
  网络首发：2026-07-27，
  
  纸质出版：2026-06-15
- 稿件说明：
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马伟杰,姚舜才,马敏毓等.基于改进蚁群-动态窗口融合算法的室内服务机器人路径规划[J].智能科学与技术学报,2026,08(02):219-236.

Ma Weijie,Yao Shuncai,Ma Minyu,et al.Path planning for indoor service robots using an improved ACO-DWA hybrid algorithm[J].Chinese Journal of Intelligent Science and Technology,2026,08(02):219-236.
马伟杰,姚舜才,马敏毓等.基于改进蚁群-动态窗口融合算法的室内服务机器人路径规划[J].智能科学与技术学报,2026,08(02):219-236. DOI： 10.11959/j.issn.2096-6652.ZNKX26031.

Ma Weijie,Yao Shuncai,Ma Minyu,et al.Path planning for indoor service robots using an improved ACO-DWA hybrid algorithm[J].Chinese Journal of Intelligent Science and Technology,2026,08(02):219-236. DOI： 10.11959/j.issn.2096-6652.ZNKX26031.

摘要

针对无法规避动态障碍的全局规划及可能陷入局部最优解的局部规划问题，提出一种应用于室内服务机器人的改进蚁群（ant colony optimization，ACO）-动态窗口（dynamic window approach，DWA）融合的路径规划算法。首先，针对蚁群算法搜索速度较慢的缺陷，对启发因子进行优化，并基于差速机器人运动模型优化搜索邻域，以提高搜索效率；其次，针对蚁群算法形成路径无法保证安全距离等缺陷，引入新的避障策略，对路径进行折线优化操作；最后，融合改进蚁群算法与动态窗口算法，确保机器人实现精确动态避障。实验结果证明，改进后的融合算法相较于蚁群-动态窗口算法，能够更好地保证机器人在有随机障碍物的路段安全通行。

Abstract

To address the limitation that global path planning algorithms struggle with dynamic obstacle avoidance and local path planning algorithms were susceptible to local optima

an improved hybrid algorithm was proposed combining the ant colony optimization (ACO) and dynamic window approach (DWA) for indoor service robots. Firstly

aiming at the low search efficiency of the traditional ACO algorithm

the heuristic factors were optimized

and the search neighborhood was reconstructed based on the differential drive robot motion model to enhance the global searching efficiency. Secondly

a novel obstacle avoidance strategy and path polyline optimization method were adopted to solve the deficiency that the original ACO-generated paths fail to guarantee reliable safety distances around obstacles. Finally

the improved ACO algorithm was integrated with the dynamic window approach to achieve accurate and stable dynamic obstacle avoidance. Experimental results demonstrate that compared with the conventional ACO-DWA algorithm

the proposed hybrid algorithm enables service robots to travel safely in complex scenarios with random dynamic obstacles.

关键词

Keywords

references

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