基于改进A^( *)与人工势场法相融合的路径规划算法研究  

A Path Planning Method Integrating an Enhanced A^(*) Algorithm with the Artificial Potential Field Approach

作　　者：唐颖 温秀兰[1] 王直荣 雷志发 杨宇华 王晓飞[3] TANG Ying;WEN Xiulan;WANG Zhirong;LEI Zhifa;YANG Yuhua;WANG Xiaofei(School of Automation,Nanjing Institute of Technology,Nanjing 211167,China;Changzhou Insitute of Inspection Testing Standardization and Certification,Changzhou 213164,China;Jiangsu Institute of Metrology,Nanjing 210002,China)

机构地区：[1]南京工程学院自动化学院,江苏南京211167 [2]常州检验检测标准认证研究院,江苏常州213164 [3]江苏省计量科学研究院,江苏南京210002

出　　处：《南京工程学院学报(自然科学版)》2024年第3期83-90,共8页Journal of Nanjing Institute of Technology(Natural Science Edition)

基　　金：江苏省市场监督管理局重大科技项目(KJ2024020,KJ2025020);南京工程学院大学生科技创新基金项目(TB202417013)。

摘　　要：为了提升移动机器人全局路径规划能力,提出一种融合改进A*算法与人工势场法的混合算法.首先,通过角度定义改进启发函数,实现更优的路径评估,并结合当前点与目标点的距离优化评价函数,缩短路径规划时间;然后,利用人工势场法分析当前点与目标点之间的相对位置,调整斥力函数的生成方向,解决目标不可达问题;最后,利用改进A*算法生成的最优节点作为人工势场法中的虚拟目标点,完成路径规划.仿真试验结果表明,该融合算法能有效避开局部陷阱区域、提升搜索效率,保证在实现动态避障的同时路径的全局最优性.To enhance the global path planning capability of mobile robots,a hybrid algorithm integrating an improved A^(*)algorithm with the artificial potential field method has been proposed.Initially,an enhanced heuristic function is introduced by defining angle-based parameters,enabling more precise path evaluation.This approach is further refined by incorporating the distance between the current and target points to optimize the evaluation function,thereby reducing path-planning time.Additionally,within the framework of artificial potential fields,the direction of repulsive forces is adjusted by analyzing the relative positions between the current point and the target,effectively addressing challenges associated with unreachable targets.Ultimately,the optimal nodes generated by the modified A^(*)algorithm serve as virtual targets within the artificial potential field,thus completing the path-planning process.Simulation results demonstrate that this integrated approach effectively avoids local trap areas,while enhancing search efficiency,and achieving global optimality in the planned paths.Furthermore,it allows for dynamic obstacle avoidance.

关 键 词：移动机器人 路径规划 改进A*算法 人工势场法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]