基于A^(*)算法优化AGV/机器人路径规划的研究进展  

Research Progress in Optimizing Automated Guided Vehicle/Robot Path Planning Based on A^(*)Algorithm

作　　者：李艳珍 詹浩 钟鸣长[1] LI Yanzhen;ZHAN Hao;ZHONG Mingchang(Business School,Liming Vocational University,Quanzhou 362007,China;Business School,City University of Macao,Macao 999078,China;College of Logistics and Transportation,Guangzhou Railway Polytechnic,Guangzhou 510430,China)

机构地区：[1]黎明职业大学商学院,福建泉州362007 [2]澳门城市大学商学院,中国澳门999078 [3]广州铁路职业技术学院运输物流学院,广东广州510430

出　　处：《常州信息职业技术学院学报》2024年第1期29-36,共8页Journal of Changzhou College of Information Technology

基　　金：2023年泉州市社会科学规划项目“‘双碳’视域下泉州市生鲜电商冷链物流绿色发展研究”(2023H13);2021年福建省科协科技创新智库课题““双碳”背景下福建绿色经济高质量发展机制创新与路径选择研究”(FJKX-A2122)。

摘　　要：智能仓储、智能工厂等是未来的重要发展方向,自动导引运输车(AGV)/移动机器人在其中发挥着重要作用,而路径规划是其核心技术之一,被广泛研究。A^(*)算法在全局路径规划中具有独特的优势而备受关注,针对其存在的冗余点多、路径不平滑等问题,研究人员对A^(*)算法进行了大量的改进及优化研究。对国内近年来的研究主要从优化A^(*)算法的评价函数、A^(*)算法与其他算法融合两个方面进行梳理、归纳,基于当前的研究进展和趋势,为后期的研究方向提出一些建议。Intelligent warehousing and intelligent factory are important development directions in the future,in which automated guided transport vehicles(AGVs)/mobile robots play an important role.Path planning is one of the core technologies for AGV/mobile robots,which has been widely studied.A^(*)algorithm has attracted much attention because of its unique advantages in global path planning.In view of its problems such as redundant points and unsmooth path,researchers have made a lot of improvements and optimization researches on A^(*)algorithm.This paper mainly summarizes the research progress in recent years mainly from two aspects of optimizing the evaluation function of A^(*)algorithm and the fusion of A^(*)algorithm with other algorithms.Based on the current research progress and trend,some suggestions are put forward for the future research.

关 键 词：智能仓储 自动导引运输车 搬运机器人 路径规划 A^(*)算法优化 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]