Automated guided vehicles (AGVs), as key components of intelligent manufacturing and logistics systems, have their path planning technology directly impacting the efficiency and reliability of the system. This paper systematically reviews current AGV path planning methodologies, categorizing them into six paradigms: rule-based, sampling-based, search-based, optimization-based, learning-based, and local obstacle avoidance approaches. Each method is analyzed for its computational efficiency, solution quality, and applicability across scenarios. The study finds that balancing dynamic environmental adaptability, multi-objective collaborative optimization, and computational efficiency remain core challenges. Future directions emphasize real-time multi-layer planning, adaptive multi-objective optimization, cross-scenario generalization, and edge computing integration. This review provides a foundation for advancing AGV path planning technologies, enabling safer, more efficient, and more scalable autonomous operations in industrial systems.
TongXYuSLiuG, et al. A hybrid formation path planning based on A* and multi-target improved artificial potential field algorithm in the 2D random environments. Adv Eng Inform2022; 54: 101755.
5.
De RyckMVersteyheMDebrouwereF. Automated guided vehicle systems, state-of-the-art control algorithms and techniques. J Manuf Syst2020; 54: 152–173.
6.
HowdenWE. The sofa problem. Comput J1968; 11(3): 299–301.
7.
WangWMDuJL. Agent path planning based on regular hexagon grid JPS algorithm. Syst Eng Electron2021; 43(12): 3635.
8.
LiuGCJinSPLiZP, et al. Path planning of urban manned electric vertical take-off and landing vehicle based on improved artificial electric field algorithm. Sci Technol Eng2025; 25(1): 238–244.
9.
BernuyFRuiz-del-SolarJ. Topological semantic mapping and localization in urban road scenarios. J Intell Robot Syst2018; 92: 19–32.
10.
SousaYCNBassaniHF. Topological semantic mapping by consolidation of deep visual features. IEEE Robot Autom Lett2022; 7(2): 4110–4117.
11.
FathanAMJatiANSaputraRE. Mapping algorithm using ultrasonic and compass sensor on autonomous mobile robot. In: 2016 International conference on control, electronics, renewable energy and communications (ICCEREC), Bandung, Indonesia, 13–15 September 2016, pp.86–90. New York: IEEE.
12.
TakeuchiETsubouchiT. Multi sensor map building based on sparse linear equations solver. In: 2008 IEEE/RSJ international conference on intelligent robots and systems, IEEE, 2008, pp. 2511–2518.
13.
ZhangXYXieZWWuH. Obstacle avoidance path planning method of power grid inspection robot based on time grid method and optimal search. Mach Electron2021; 39(9): 71–75.
14.
FoxDBurgardWThrunS. Probabilistic methods for mobile robot mapping. In: Proceedings of the IJCAI-99 workshop on adaptive spatial representations of dynamic environments, 1999, pp. 565–283.
15.
LiuHHuangHYeungSK, et al. 360ST-mapping: an online semantics-guided topological mapping module for omnidirectional visual SLAM. In: 2022 IEEE/RSJ international conference on intelligent robots and systems (IROS), Kyoto, Japan, 23–27 October 2022, pp.802–807. New York: IEEE.
16.
TodaYMasuyamaN. Adaptive resonance theory-based global topological map building for an autonomous mobile robot. IEEE Access2024; 12: 111371–111385.
17.
ZhaoJMengCYWangXB, et al. Modeling and analysis of AGV grid method based on feature points extraction. J Comput Eng Appl2022; 58(8): 156.
18.
Garcia-FidalgoEOrtizA. Hierarchical place recognition for topological mapping. IEEE Trans Robot2017; 33(5): 1061–1074.
19.
LiuYWangLLiuM. Real time probabilistic mapping for sonar sensor by optimization. In: 2018 IEEE international conference on robotics and biomimetics (ROBIO), Kuala Lumpur, Malaysia, 12–15 December 2018, pp.838–843. New York: IEEE.
20.
LuJRenCShaoY, et al. An automated guided vehicle conflict-free scheduling approach considering assignment rules in a robotic mobile fulfillment system. Comput Ind Eng2023; 176: 108932.
21.
ZhangZZhangBDaiZ, et al. Multi-AGV conflict-free path planning based on dynamic priority strategy. Appl Res Comput2021; 38(7): 2108–2111.
22.
LiTDingPZhangR. Multi-AGVs conflict-free path planning based on dynamic obstacle avoidance priority and prediction. Syst Eng2024; 42(3): 50–62.
23.
TianSHFanL. Multi-AGV path planning for express distribution center under dynamic priority strategy. J Comput Eng Appl2023; 59(14): 32–56.
24.
WuNZhouM. Modeling and deadlock control of automated guided vehicle systems. IEEE/ASME Trans Mechatron2004; 9(1): 50–57.
25.
ZhangDSunXFuS, et al. Cooperative path planning in multi-robots for intelligent warehouse. Comput Integr Manuf Syst2018; 24(2): 410–418.
26.
ZhongMYangYDessoukyY, et al. Multi-AGV scheduling for conflict-free path planning in automated container terminals. Comput Ind Eng2020; 142: 106371.
27.
YangMBianYMaL, et al. Research on traffic control algorithm based on multi-AGV path planning. In: 2021 IEEE international conference on systems, man, and cybernetics (SMC), Melbourne, VIC, Australia, 17–20 October 2021, pp.697–702. New York: IEEE.
28.
LiQPogromskyAAdriaansenT, et al. A control of collision and deadlock avoidance for automated guided vehicles with a fault-tolerance capability. Int J Adv Robot Syst2016; 13(2): 64.
29.
LeeKParkS. Geometric zone-control algorithm for collision and deadlock avoidance in AGV system. IEEE Access2023; 11: 131289–131301.
30.
LanPZChenJWCaoSL. An AGV control algorithm in automated terminal based on Ant-agent. J Transp Syst Eng Inf Technol2020; 20(1): 190–197.
31.
ZengQCLiMZXueGS. Multi-AGV conflict-free dynamic path planning model for automated terminals considering congestion factors. Journal of Dalian Maritime University2019; 45(4): 35–44.
32.
YangHJZhaoGWLiHB, et al. Path planning of a multiple AGV system in unmanned warehouse with bottom passable shelves. J Natl Univ Def Technol2024; 46(4): 104–113.
33.
ChenXXingZFengL, et al. An ETCEN-based motion coordination strategy avoiding active and passive deadlocks for multi-AGV system. IEEE Trans Autom Sci Eng2022; 20(2): 1364–1377.
34.
ZhaoYLiuXWuS, et al. Spare zone based hierarchical motion coordination for multi-AGV systems. Simul Model Pract Theory2021; 109: 102294.
35.
ZhouYLuYLvL. GVP–RRT: a grid based variable probability Rapidly-exploring Random Tree algorithm for AGV path planning. Complex Intell Syst2024; 10(6): 8273–8286.
36.
ZhouYLuYLvL. Grid-based non-uniform probabilistic roadmap-based AGV path planning in narrow passages and complex environments. Electronics2024; 13(1): 225.
37.
WuBZhangWChiX, et al. A novel AGV path planning approach for narrow channels based on the Bi-RRT algorithm with a failure rate threshold. Sensors2023; 23(17): 7547.
38.
WangJLuoYTanX. Path planning for automatic guided vehicles (AGVs) fusing MH-RRT with improved TEB. Actuators2021; 10(12): 314.
39.
RavankarAARavankarAEmaruT, et al. HPPRM: hybrid potential based probabilistic roadmap algorithm for improved dynamic path planning of mobile robots. IEEE Access2020; 8: 221743–221766.
40.
GargD. Dynamizing Dijkstra: a solution to dynamic shortest path problem through retroactive priority queue. J King Saud Univ Comput Inf Sci2021; 33(3): 364–373.
41.
GongHNiCWangP, et al. Smooth path planning method based on Dijkstra algorithm. J Beijing Univ Aeronaut Astronaut2022; 50(2): 535–541.
42.
ChenYLZhuLYZhuLB, et al. Research on path planning of parking system based on improved Dijkstra algorithm. Modern Manufacturing Engineering2017(8): 63–67.
43.
KimSJinHSeoM, et al. Optimal path planning of automated guided vehicle using Dijkstra algorithm under dynamic conditions. In: 2019 7th International conference on robot intelligence technology and applications (RiTA), Daejeon, South Korea, 1–3 November 2019, pp.231–236. New York: IEEE.
HeLNLouPHQianXM, et al. Collision-free path planning for automated guided vehicles based on time windows. Comput Integr Manuf Syst2010; 16(12): 2630–2634.
46.
JiangCKLiZPanSB, et al. Collision-free path planning for AGVs based on the improved Dijkstra algorithm. Computer Science2020; 47(8): 272–277.
47.
GuoKZhuJShenL. An improved acceleration method based on multi-agent system for AGVs conflict-free path planning in automated terminals. IEEE Access2020; 9: 3326–3338.
48.
FanHMMuSYueLJ. Collaborative optimization of automated guided vehicle scheduling and path planning considering conflicts and congestion. J Comput Appl2022; 42(7): 2281–2291.
49.
ZhangXFHuWPShiJH, et al. Research on path planning of mobile robots based on improved bidirectional A* algorithm. J Chongqing Technol Bus Univ (Nat Sci Ed)2025; 11: 1–13.
50.
LiuWChenLWangR, et al. Trajectory planning for AGV based on the improved artificial potential field-A* algorithm. Meas Sci Technol2024; 35(9): 096312.
51.
LaiRWuZLiuX, et al. Fusion algorithm of the improved A* algorithm and segmented Bezier curves for the path planning of mobile robots. Sustainability2023; 15(3): 2483.
52.
ZhangYMWangJFuCX. Research on AGV path planning based on improved A* algorithm. J Qingdao Univ (Eng Technol Ed)2024; 39(3): 13–19.
53.
LiuJYSunWTLiuM, et al. Research on path planning of automated guided vehicles in warehouse logistics. Modular Mach Tool Autom Manuf Tech2018(12): 150–154.
54.
FransenKJCVan EekelenJPogromskyA, et al. A dynamic path planning approach for dense, large, grid-based automated guided vehicle systems. Comput Oper Res2020; 123: 105046.
55.
ZhangXYZouSS. Collision-free path planning for automated guided vehicles based on an improved A* algorithm. Syst Eng Theory Pract2021; 41(1): 240–246.
56.
ZhangYHuangH. Multi-AGVs pathfinding based on improved jump point search in logistic center. In: International conference on algorithmic applications in management. Springer International Publishing, Cham, 2020, pp. 358–368.
57.
ZhangYLuoJCaiX, et al. AGV path planning for logistics warehouse by using an improved D* Lite algorithm. In: International conference on the efficiency and performance engineering network. Springer Nature Switzerland, Cham, 2022, pp. 1018–1027.
58.
QinXHuangXHMaDM, et al. Path planning of inspection robot based on improved D* algorithm. Modular Mach Tool Autom Manuf Tech2022; 7(6): 10–13.
59.
LiuJFengSRenJH. Directed D* algorithm for dynamic path planning of mobile robots. J Zhejiang Univ (Eng Sci)2020; 54(2): 291–300.
60.
LeeJTKangTWChoiYS, et al. Clearance-based performance-efficient path planning using generalized Voronoi diagram. Int J Fuzzy Log Intell Syst2023; 23(3): 259–269.
61.
WuXLiYZZangTG, et al. Fusion path planning of mobile robots based on Voronoi framework. J Mech Eng 2025; 61: 165–177.
62.
HuangSKWangWJSunCH. A path planning strategy for multi-robot moving with path-priority order based on a generalized Voronoi diagram. Appl Sci2021; 11(20): 9650.
63.
KimDHHaiNTJoeWY. A guide to selecting path planning algorithm for automated guided vehicle (AGV). In: International Conference on Advanced Engineering Theory and Applications. Springer International Publishing, Cham, 2017, pp. 587–596.
64.
YuJQLiY. Research on AGV scheduling based on point-to-point mode. In: 2020 5th International conference on mechanical, control and computer engineering (ICMCCE), IEEE, 2020, pp. 1943–1947.
65.
LiuEHYaoXFLanHY, et al. Dynamic path planning and implementation of automated guided vehicles based on an improved genetic algorithm. Comput Integr Manuf Syst2018; 24(6): 1455–1467.
66.
ZhangYJWuJChengJQ, et al. Path planning of AGVs considering collision avoidance under multiple handling tasks. Appl Res Comput2024; 41(5): 1462–1469.
67.
NiuYYLiB. Path planning of omnidirectional AGVs based on simulated annealing genetic algorithm. Comput Integr Manuf Syst2024; 30(10): 3730–3741.
68.
LinYSLiQSPengHH, et al. Collaborative optimization algorithm for intelligent warehouse slot planning and AGV path planning. J Softw2020; 31(9): 2770–2784.
69.
NazarahariMKhanmirzaEDoostieS. Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm. Expert Syst Appl2019; 115: 106–120.
70.
DuanPYuZGaoK, et al. Solving the multi-objective path planning problem for mobile robot using an improved NSGA-II algorithm. Swarm Evol Comput2024; 87: 101576.
71.
WangQTangS. Improved human optimisation algorithm for global path planning in AGV. In: 2024 5th International conference on computer engineering and application (ICCEA), Hangzhou, China, 12–14 April 2024, pp.56–62. New York: IEEE.
72.
LinSWangJHuangB, et al. Bio particle swarm optimization and reinforcement learning algorithm for path planning of automated guided vehicles in dynamic industrial environments. Sci Rep2025; 15(1): 463.
73.
DaoTKNgoTGPanJS, et al. Enhancing path planning capabilities of automated guided vehicles in dynamic environments: multi-objective PSO and dynamic-window approach. Biomimetics2024; 9(1): 35.
74.
KimMLeeMKimB, et al. Development of local path planning using selective model predictive control, potential fields, and particle swarm optimization. Robotics2024; 13(3): 46.
75.
PoyYLDarmarajuSKwanBH. Multi-robot path planning using modified particle swarm optimization. In: 2023 IEEE international conference on automatic control and intelligent systems (I2CACIS), Shah Alam, Malaysia, 17 June 2023, pp.225–230. New York: IEEE.
76.
QiuKWangZChenG, et al. Collaborative path planning arithmetic for multi robot system of power intelligent inspection based on particle swarm optimization. In: 2023 International conference on networking, informatics and computing (ICNETIC), Palermo, Italy, 29–31 May 2023, pp.639–644. New York: IEEE.
77.
LinSLiuAWangJ, et al. An improved fault-tolerant cultural-PSO with probability for multi-AGV path planning. Expert Syst Appl2024; 237: 121510.
78.
WangWLiJBaiZ, et al. Toward optimization of AGV path planning: an RRT*–ACO algorithm. IEEE Access2024; 12: 18387–18399.
79.
MaY. Path planning based on improved ant colony optimization in congestion. In: 2021 11th IEEE international conference on intelligent data acquisition and advanced computing systems: technology and applications (IDAACS), Cracow, Poland, 22–25 September 2021, vol. 2, pp.1030–1035. New York: IEEE.
80.
YuJQLiRLZhaoAJ, et al. Research on path planning of warehouse AGVs using the parallel sorting ant colony algorithm. Mech Sci Technol Aerosp Eng2021; 40(4): 609–618.
81.
SunJXinL. Research on path planning of AGV based on improved ant colony optimization algorithm. In: 2021 33rd Chinese control and decision conference (CCDC), Kunming, China, 22–24 May 2021, pp.7567–7572. New York: IEEE.
82.
YuanSHeZHeP, et al. An improved ant colony optimization algorithm for multi-robot path planning under complex tasks. In: 2024 7th International conference on intelligent robotics and control engineering (IRCE), Xi’an, China, 7–9 August 2024, pp.95–99. New York: IEEE.
83.
SuYYLiZY. AGV autonomous obstacle avoidance based on improved ant colony algorithm. J Shenyang Univ (Nat Sci)2024; 36(04): 289–296, 340.
84.
XueXZhuQXZhuYH. Robot path planning based on an improved ant colony algorithm. J Xi’an Polytech Univ2024; 38(6): 59–66.
85.
HuHYangXXiaoS, et al. Anti-conflict AGV path planning in automated container terminals based on multi-agent reinforcement learning. Int J Prod Res2023; 61(1): 65–80.
86.
YeXDengZShiY, et al. Toward energy-efficient routing of multiple AGVs with multi-agent reinforcement learning. Sensors2023; 23(12): 5615.
87.
NieJZhangGLuX, et al. Reinforcement learning method based on sample regularization and adaptive learning rate for AGV path planning. Neurocomputing2025; 614: 128820.
88.
LeeDKangYSDo NohS, et al. Digital twin-driven reinforcement learning for dynamic path planning of AGV systems. In: IFIP international conference on advances in production management systems, 2024, pp.351–365. Cham: Springer Nature Switzerland.
89.
WenSWenZZhangD, et al. A multi-robot path-planning algorithm for autonomous navigation using meta-reinforcement learning based on transfer learning. Appl Soft Comput2021; 110: 107605.
90.
LiKLiuTKumarPNR, et al. A reinforcement learning-based hyper-heuristic for AGV task assignment and route planning in parts-to-picker warehouses. Transp Res E Logist Transp Rev2024; 185: 103518.
91.
LowESOngPLowCY. A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot. Comput Ind Eng2023; 181: 109338.
92.
LiaoXWangYXuanY, et al. AGV path planning model based on reinforcement learning. In: 2020 Chinese automation congress (CAC), Shanghai, China, 6–8 November 2020, pp.6722–6726. New York: IEEE.
93.
LiFChengJMaoZ, et al. Enhancing safety and efficiency in automated container terminals: route planning for hazardous material AGV using LSTM neural network and Deep Q-Network. J Intell Connect Veh2024; 7(1): 64–77.
94.
ChenXLiuSZhaoJ, et al. Autonomous port management based AGV path planning and optimization via an ensemble reinforcement learning framework. Ocean Coast Manage2024; 251: 107087.
95.
GuoDJiSYaoY, et al. A decentralized path planning model based on deep reinforcement learning. Comput Electr Eng2024; 117: 109276.
96.
XiaoZLiPLiuC, et al. MACNS: a generic graph neural network integrated deep reinforcement learning based multi-agent collaborative navigation system for dynamic trajectory planning. Inf Fusion2024; 105: 102250.
97.
ShenGMaRTangZ, et al. A deep reinforcement learning algorithm for warehousing multi-AGV path planning. In: 2021 International conference on networking, communications and information technology (NetCIT), Manchester, UK, 26–27 December 2021, pp.421–429. New York: IEEE.
98.
ZhangLCaiZYanY, et al. Multi-agent policy learning-based path planning for autonomous mobile robots. Eng Appl Artif Intell2024; 129: 107631.
99.
XiaoQPanTWangK, et al. Research on AGV path planning based on improved DQN algorithm. Sensors2025; 25(15): 4685.
100.
LiHZhongPLiuL, et al. Robot dynamic path planning based on prioritized experience replay and LSTM network. IEEE Access2025; 13: 22283–22299.
101.
LinYHueGWangL, et al. A multi-AGV routing planning method based on deep reinforcement learning and recurrent neural network. IEEE/CAA J Autom Sin2023; 11(7): 1720–1722.
102.
ValdiviaJPHataATerraA. Safe and robust simulation-to-reality transfer for mobile robots. In: 2024 IEEE 29th international conference on emerging technologies and factory automation (ETFA), Padova, Italy, 10–13 September 2024, pp.1–8. New York: IEEE.
103.
KhatibO. Real-time obstacle avoidance for manipulators and mobile robots. Int J Robot Res1986; 5(1): 90–98.
104.
JinXLiZOpinat IkielaNV, et al. An efficient trajectory planning approach for autonomous ground vehicles using improved artificial potential field. Symmetry2024; 16(1): 106.
105.
ShenWWuD. Path planning of an AGV based on artificial potential field and model predictive control. In: 2021 33rd Chinese control and decision conference (CCDC), Kunming, China, 22–24 May 2021, pp.6925–6930. New York: IEEE.
106.
ChenHWangQYuM, et al. Path planning for multi-robot systems in intelligent warehouse. In: Internet and distributed computing systems: 11th international conference, IDCS 2018, Tokyo, Japan, 11–13 October 2018, Proceedings 11, pp.148–159. Cham: Springer International Publishing.
107.
WangBZhouKQuJ. Research on multi-robot local path planning based on improved artificial potential field method. In: The Euro-China Conference on Intelligent Data Analysis and Applications, 2018, pp.684–690. Cham: Springer International Publishing.
108.
ZhaoTLiHDianS. Multi-robot path planning based on improved artificial potential field and fuzzy inference system. J Intell Fuzzy Syst2020; 39(5): 7621–7637.
109.
FoxDBurgardWThrunS. The dynamic window approach to collision avoidance. IEEE Robot Autom Mag1997; 4(1): 23–33.
110.
XuZYuanW. Mobile robot path planning based on fusion of improved A* algorithm and adaptive DWA algorithm. J Phys Conf Ser2022; 2330(1): 012003.
111.
KimJYangGH. Improvement of dynamic window approach using reinforcement learning in dynamic environments. Int J Control Autom Syst2022; 20(9): 2983–2992.
