融合改进A星算法与人工势场算法的移动机器人路径规划  

Mobile Robot Path Planning by Integrating Improved A-Star Algorithm with Artificial Potential Field Algorithm

作　　者：陈义亮 宁萌 蔡礼扬 王雨芊 马泓睿 CHEN Yiliang;NING Meng;CAI Liyang;WANG Yuqian;MA Hongrui(School of Ineligent Manufacturing,Jiangnan University,Wuxi,Jiangsu 214122,China;Jiangsu Key laboratory of Adr anced Food Manufacturing Equipment&Technology,Jiangnan University,Wuxi,Jiangsu 214122,China)

机构地区：[1]江南大学智能制造学院,江苏无锡214122 [2]江南大学江苏省食品先进制造装备技术重点实验室,江苏无锡214122

出　　处：《轻工机械》2025年第2期62-70,共9页Light Industry Machinery

基　　金：国家自然科学青年科学基金项目(52205015);国家重点研发计划课题(2022YFD2100304);江苏省产业关键技术扶持资金项目(CY202319)。

摘　　要：为提高移动机器人在复杂多变环境中路径规划的效率,课题组提出了一种融合改进A星算法与人工势场算法的路径规划方法。该算法对传统A星算法的代价函数和邻域节点扩展方式进行了改进,利用了曼哈顿距离原理动态调整人工势场系数,并引入比例系数k,有效解决了因势场系数固定而导致的局部路径处理难的问题。仿真实验结果表明该融合算法在多种环境条件下均能高效规划出更优路径,展现了良好的适应性。To enhance the efficiency of path planning for mobile robots in complex and dynamic environments,a path planning strategy integrating improved A-star algorithm with artificial potential field algorithm was proposed.The cost function and neighborhood node expansion method of traditional A-star algorithm had been refined by the fusion algorithm.The artificial potential field coefficients was dynamically adjusted by utilizing the Manhattan distance principle.And the proportional coefficient k was introduced to effectively solve the problem of difficult local path processing caused by fixed potential field coefficients.Simulation experimental results demonstrate that in various complex and dynamic environments,this fusion algorithm can efficiently plan superior paths under multiple environmental conditions,and exhibit good adaptability.

关 键 词：移动机器人 路径规划 A星算法 人工势场算法 曼哈顿距离原理 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TH72[自动化与计算机技术—控制科学与工程]