基于CiteSpace的AGV路径规划研究热点分析

摘要/Abstract

摘要： AGV路径规划对于提升物料搬运效率起到了重要作用，随着技术的发展与AGV应用范围的逐步扩大，路径规划问题得到了许多学者的研究与关注。本文利用文献统计分析法与可视化工具CiteSpace软件，以中国知网与Web of Science为对象，对中外文文献分析。首先通过关键词词频与中心性排序得出研究热点，中文文献研究热点包含：路径规划算法、AGV调度、时间窗、激光导航；外文文献研究热点包含：flexible manufacturing system、material handling system、layout、AGV system、algorithm，接着对热点逐一述评。然后将中外文文献进行对比，分析异同之处。最后对AGV路径规划的研究进行展望，明确未来的研究将朝着以下几点展开：算法的组合优化、应用场景的针对性建模、相关问题的协同研究。

关键词: 自动导引车, 路径规划, CiteSpace, 可视化分析

Abstract: AGV path planning plays an important role in improving the efficiency of material handling. With the development of technology and the gradual expansion of the application scope of AGV, the problem of path planning has attracted the attention of scholars. This paper analyzes Chinese and foreign literatures based on CNKI and WOS by using the method of literature statistical analysis and visualization tool CiteSpace software. First, the research hotspots are obtained by keyword frequency and centralized sorting. The Chinese literature research hotspots include path planning algorithm, AGV scheduling, time window and laser navigation. The foreign literature research hotspots include flexible manufacturing system, material handling system, layout, AGV system and algorithm. Secondly, the hotspots are reviewed one by one. Then the Chinese and foreign literatures are compared and the similarities and differences of the Chinese and foreign literatures are analysed. Finally, the research of AGV path planning is prospected, and the future research will focus on the following aspects: combinatorial optimization of algorithms, targeted modeling of application scenarios and collaborative research on related issues.

Key words: AGV, path planning, CiteSpace, visualized analysis

刘羽飞, 张绪美, 梁晓磊. 基于CiteSpace的AGV路径规划研究热点分析[J]. 计算机与现代化, 2021, 0(05): 112-119.

LIU Yu-fei, ZHANG Xu-mei, LIANG Xiao-lei. Research Hotspot Analysis of AGV Path Planning Based on CiteSpace[J]. Computer and Modernization, 2021, 0(05): 112-119.

参考文献

［1］ LE-ANH T, DE KOSTER M B M. A review of design and control of automated guided vehicle systems［J］. European Journal of Operational Research, 2006,171(1):1-23.
［2］ VIS I F A. Survey of research in the design and control of automated guided vehicle systems［J］. European Journal of Operational Research, 2006,170(3):677-709.
［3］王娟,陈世超,王林丽,等. 基于CiteSpace的教育大数据研究热点与趋势分析［J］. 现代教育技术, 2016,26(2):5-13.
［4］ YU D J. A scientometrics review on aggregation operator research［J］. Scientometrics, 2015,105(1):115-133.
［5］ YOSHIKANE F. Multiple regression analysis of a patent’s citation frequency and quantitative characteristics: The case of Japanese patents［J］. Scientometrics, 2013,96(1):365-379.
［6］ EBRAHIM N A, SALEHI H, EMBI M A, et al. Effective strategies for increasing citation frequency［J］. International Education Studies, 2013,6(11):93-99.
［7］ FREEMAN L C. A set of measures of centrality based on betweenness［J］. Sociometry, 1977,40(1):35-41.
［8］ QIAN G. Scientometric sorting by importance for literatures on life cycle assessments and some related methodological discussions［J］. The International Journal of Life Cycle Assessment, 2014,19(7):1462-1467.
［9］卞永明,马逍阳,高飞,等. 基于改进A-Star算法的AGV全局路径规划［J］. 机电一体化, 2019,25(6):9-15.
［10］王中玉,曾国辉,黄勃,等. 改进A~*算法的机器人全局最优路径规划［J］. 计算机应用, 2019,39(9):2517-2522.
［11］张旭,程传奇,郝向阳,等. 一种兼顾全局与局部特性的机器人动态路径规划算法［J］. 测绘科学技术学报, 2018,35(3):315-320.
［12］苑光明,翟云飞,丁承君,等. 基于改进遗传算法的AGV路径规划［J］. 北京联合大学学报, 2018,32(1):65-69.
［13］王雷,李明. 改进自适应遗传算法在移动机器人路径规划中的应用［J］. 南京理工大学学报, 2017,41(5):627-633.
［14］刘二辉,姚锡凡,蓝宏宇,等. 基于改进遗传算法的自动导引小车动态路径规划及其实现［J］. 计算机集成制造系统, 2018,24(6):1455-1467.
［15］夏谦,雷勇,叶小勇. 遗传算法在AGV全局路径优化中的应用［J］. 四川大学学报(自然科学版), 2008,45(5):1129-1136.
［16］李庆中,顾伟康,叶秀清. 基于遗传算法的移动机器人动态避障路径规划方法［J］. 模式识别与人工智能, 2002,15(2):161-165.
［17］刘二辉,姚锡凡. 基于改进遗传算法的自动导引小车路径规划及其实现平台［J］. 计算机集成制造系统, 2017,23(3):465-472.
［18］陈亮,陈君若. 基于改进遗传算法的移动机器人路径规划［J］. 软件导刊, 2019,18(4):24-27.
［19］郑海丽,谢宏. 基于AGV的智能停车场路径规划与调度算法研究［J］. 现代计算机, 2018(17):23-27.
［20］孟冲,任彧. 基于多种群遗传算法的多AGV调度［J］. 电子科技, 2018,31(11):47-50.
［21］付建林,张恒志,张剑，等. 自动导引车调度优化研究综述［J］. 系统仿真学报, 2020,32(9):1664-1675.
［22］贺丽娜,楼佩煌,钱晓明,等. 基于时间窗的自动导引车无碰撞路径规划［J］. 计算机集成制造系统, 2010,16(12):2630-2634.
［23］贺丽娜. AGV系统运行路径优化技术研究［D］. 南京:南京航空航天大学, 2011.
［24］胡彬,王冰,王春香,等. 一种基于时间窗的自动导引车动态路径规划方法［J］. 上海交通大学学报, 2012,46(6):967-971.
［25］泰应鹏,邢科新,林叶贵,等. 多AGV路径规划方法研究［J］. 计算机科学, 2017,44(11A):84-87.
［26］张峥炜,陈波,陈卫东. 时间窗约束下的AGV动态路径规划［J］. 微型电脑应用, 2016,32(11):46-49.
［27］唐天晓. 激光导航自动导引小车控制系统研制［D］. 淮南:安徽理工大学, 2018.
［28］路程. 激光导引AGV样机研制［D］. 合肥:合肥工业大学, 2016.
［29］ULUSOY G, BILGE U. Simultaneous scheduling of machines and automated guided vehicles［J］. International Journal of Production Research, 1993,31:2857-2873.
［30］BILGE U, ULUSOY G. A time window approach to simultaneous scheduling of machines and material handling system in an FMS［J］. Operations Research, 1995,43(6):1058-1070.
［31］ULUSOY G, SIVRIKAYA-SERIFOGLU F, BILGE U. A genetic algorithm approach to the simultaneous scheduling of machines and automated guided vehicles［J］. Computer and Operations Research, 1997,24(4):335-351.
［32］LYU XF, SONG Y C, HE C Z, et al. Approach to integrated scheduling problems considering optimal number of automated guided vehicles and conflict-free routing in flexible manufacturing systems［J］. IEEE Access, 2019,7:74909-74924.
［33］UMAR U A, ARIFFIN M K A, ISMAIL N, et al. Hybrid multiobjective genetic algorithms for integrated dynamic scheduling and routing of jobs and automated-guided vehicle(AGV) in flexible manufacturing systems(FMS) environment［J］. International Journal of Advanced Manufacturing Technology, 2015,81(9-12):2123-2141.
［34］TUBAILEH A. Layout of flexible manufacturing systems based on kinematic constraints of the autonomous material handing system［J］. International Journal of Advanced Manufacturing Technology, 2014,74(9-12):1521-1537.
［35］TUBAILEH A, SIAM J. Single and multi-row layout design for flexible manufacturing systems［J］. International Journal of Computer Integrated Manufacturing, 2017,30(12):1316-1330.
［36］SEDEHI M S, FARAHANI R Z. An integrated approach to determine the block layout, AGV flow path and the location of pick-up/delivery points in single-loop systems［J］. International Journal of Production Research, 2009,47(11):3041-3061.
［37］YAN R D, DUNNETT S J, JACKSON L M. Novel methodology for optimising the design,operation and maintenance of a multi-AGV system［J］. Reliability Engineering&System Safety, 2018,178:130-139.
［38］DRAGANJAC I, MIKLIC D,KOVACI Z, et al. Decentralized control of multi-AGV systems in autonomous warehousing applications［J］. IEEE Transactions on Automation Science and Engineering, 2016,13(4):1433-1447.
［39］MOUSAVI M, YAP H J, MUSA S N, et al. Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization［J］. PLoS One, 2017,12(3). DOI:10.1371/journal.pone.0169817.
［40］YI G H, FENG Z L,MEI T C, et al. Multi-AGVs path planning based on improved ant colony algorithm［J］. Journal of Supercomputing, 2019,75(9):5898-5913.
［41］SAIDI-MEHRABAB M, DEHNAVI-ARANI S, EVAZABADIAN F, et al. An ant colony algorithm(ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs［J］. Computers and Industrial Engineering, 2015,86:2-13.
［42］HAMZHEEI M, FARAHANI R Z, RASHIDI-BAJGAN H. An ant colony-based algorithm for finding the shortest bidirectional path for automated guided vehicles in a block layout［J］. International Journal of Advanced Manufacturing Technology, 2013,64(1-4):399-409.
［43］ASEF-VAZIRI A, KAZEMI M, ESHGHI K, et al. An ant colony system for enhanced loop-based aisle-network design［J］. European Journal of Operational Research, 2010,207(1):110-120.
［44］LI L L, XU B W, YANG Y S, et al. Three-phase qubits-based quantum ant colony optimization algorithm for path planning of automated guided vehicles［J］. International Journal of Robotics and Automation, 2019,34(2):156-163.
［45］FARAHANI RZ, KARIMI B, TAMADON S. Designing an efficient method for simultaneously determining the loop and the location of the P/D stations using genetic algorithm［J］. International Journal of Production Research, 2007,45(6):1405-1427.
［46］CHIBA R, ARAI T, OTA J. Integrated design for automated guided vehicle systems using cooperative co-evolution［J］. Advanced Robotics, 2010,24(1-2):25-45.
［47］FARAHANI R Z, LAPORTE G, MIANDOABCHI E, et al. Designing efficient methods for the tandem AGV network design problem using tabu search and genetic algorithm［J］. International Journal of Advanced Manufacturing Technology, 2008,36(9-10):996-1009.
［48］HAN Z L, WANG D Q, LIU F, et al. Multi-AGV path planning with double-path constraints by using an improved genetic algorithm［J］. PLoS One, 2017,12(7). DOI:10.1371/journal.pone.0181747.
［49］YANG Y, ZHONG M, DESSOUKY Y, et al. An integrated scheduling method for AGV routing in automated container terminals［J］. Computers and Industrial Engineering, 2018,126:482-493.
［50］CHAWLA V K, CHANDA A K, ANGRA S. Multi-load AGVs scheduling by application of modified memetic partical swarm optimization［J］. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2018,40(9):436.
［51］MEHRABIAN A, TAVAKKOLI-MOGHADDAM R, KHALILI-DAMAGHANI K. Multi-objective routing and scheduling in flexible manufacturing systems under uncertainty［J］. Iranian Journal of Fuzzy Systems, 2017,14(2):45-77.
［52］ZHANG Y, LI L L, LIN H C, et al. Development of path planning approach using improved A-star algorithm in AGV system［J］. Journal of Internet Technology, 2019,20(3):915-924.
［53］ZHANG Z, GUO Q, CHEN J, et al. Collision-free route planning for multiple AGVs in an automated warehouse based on collision［J］. IEEE Access, 2018,6:26022-26035.
［54］VIVALDINI K, ROCHA L F, MARTARELLI N J, et al. Integrated tasks assignment and routing for the estimation of the optimal number of AGVS［J］. International Journal of Advanced Manufacturing Technology, 2016,82(1-4):719-736.

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