Skip to main content
SpringerLink
Log in

Research Summary of Intelligent Optimization Algorithm for Warehouse AGV Path Planning

  • Conference paper
  • First Online:
LISS 2021

Part of the book series: Lecture Notes in Operations Research ((LNOR))

Abstract

Automated Guided Vehicle (AGV) path planning is the core technology of warehouse AGV. Reasonable path planning is helpful to maximize the benefits of warehouse space and time. Scholars at home and abroad have already made extensive and in-depth research on warehouse AGV path planning, and have achieved fruitful research results. In this paper, the models and environmental modeling methods of warehouse AGV path planning are summarized. It turned out that the cell method is intuitive and easy to model, the geometric method is safe, but difficult to update, and the artificial potential field method is easy to solve, but easy to fall into local optimum. The optimization methods of genetic algorithm, ant colony algorithm and particle swarm optimization algorithm in AGV path planning are emphatically summarized. It is found that genetic algorithm is suitable for complex and highly nonlinear path planning problems, ant colony algorithm is suitable for discrete path planning problems, and particle swarm algorithm is suitable for real number path planning problems. The research summary of this paper provides reference value for the research of intelligent optimization algorithm of AGV path planning and new ideas for broadening the application field of AGV path planning.

Funding source of this paper-project to design and develop an intelligent book management platform in the physical bookstore scene (27170121001/025).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Fetanat, M., Haghzad, S., Shouraki, S.B., Optimization of dynamic mobile robot path planning based on evolutionary methods. AI & Robotics (IRANOPEN), IEEE, pp. 1–7 (2015)

    Google Scholar 

  2. Lv, Z., Yang, L., He, Y., Liu, Z., Han, Z.: 3D Environment Modeling with Height Dimension Reduction and Path Planning for UAV, Kunming University of Science and Technology, IEEE Control System Society Beijing Chapter, IEEE Beijing Section, Proceedings of 2017 9th International Conference on Onnd Technology. IEEE, Beijing Section, p. 6 (2017)

    Google Scholar 

  3. Liu, X.L., Jian, L., Jin, Z.F.: Mobile robot path planning based on environment modeling of grid method in unstructured environment. Mach. Tool Hydraulics 44(17), 1–7 (2016)

    Google Scholar 

  4. Dang, V.-H., Viet, H.H., Thang, N.D., Vien, N.A., Tuan, L.A.: Improving path planning methods in 2D grid maps. J. Comput. 1, 15 (2020)

    Google Scholar 

  5. Xiao, S., Tan, X., Wang, J.: A simulated annealing algorithm and grid map-based UAV coverage path planning method for 3D reconstruction. Electronics 10 (2021)

    Google Scholar 

  6. Xiong, C.: Improvement of ant colony algorithm and its application in path planning. Chongqing University of Posts and telecommunications (2020)

    Google Scholar 

  7. Lu, Z., Yang, L.Y., He, Y.Q.: 3D environment modeling with height dimension reduction and path planning for UA. In: The 2017 9th International Conference on Modelling, Identifification and Control, Kunming, China, pp. 734–739. IEEE (2017)

    Google Scholar 

  8. Wang, W.F., Wu, Y.C., Zhang, X.: Research of the unit decomposing traversal method based on grid method of the mobile robot. Tech. Autom. Appl. 32, 34–38 (2013)

    Google Scholar 

  9. Dai, G.: Algorithm research on obstacle avoidance path planning, Huazhong University of science and technology (2004)

    Google Scholar 

  10. Liu, Y.: Obstacle avoidance path generation and optimization based on visual graph method. Kunming University of science and technology (2012)

    Google Scholar 

  11. Sheng, J.: Virtual human path planning method and its application in virtual environment. East China University of science and technology (2011)

    Google Scholar 

  12. Junlan, N., Qingjie, Z., Yanfen, W.: Flight path planning of UAV based on weighted voronoi diagram. Flight Dyn. 33(4), 339–343 (2015)

    Google Scholar 

  13. Feng, C.: Application of improved immune algorithm in multi robot formation control. Guangxi University of science and technology (2019)

    Google Scholar 

  14. Chen, X., Wu, Y.: Research on path planning algorithm of UAV attacking multiple moving targets based on Voronoi diagram. Inf. Commun. 06, 36–37 (2020)

    Google Scholar 

  15. Shao, W., Luo, Z.: Application of improved visual graph method in path planning. J. Nanyang Normal Univ. 17(04), 38–42 (2018)

    Google Scholar 

  16. Feng, H., Bao, J., Jin, Y.: Generalized Voronoi diagram for multi robot motion planning. Comput. Eng. Appl. 46(22), 1–3 + 19 (2010)

    Google Scholar 

  17. Haibin, W., Yi, L.: Online path planning of mobile robot based on improved Voronoi diagram. Chinese J. Constr. Mach. 01, 117–121 (2007)

    Google Scholar 

  18. Wang, H., Hao, C.E., Zhang, P., Zhangmingquan, yinpengheng, zhangyongshun, Path planning of mobile robot based on a~* algorithm and artificial potential field method, vol. 30, pp. 2489–2496 (2019)

    Google Scholar 

  19. Wang, Y.: Improvement of artificial potential field algorithm for robots in different environments, Nanjing University of information engineering (2020)

    Google Scholar 

  20. Di, W., Caihong, L., Na, G., Tengteng, G., Guoming, L.: Local path planning of mobile robot based on improved artificial potential field method. J. Shandong Univ. Technol. (NATURAL SCIENCE EDITION) 35, 1–6 (2021)

    Google Scholar 

  21. Huang, L., Geng, Y.: Research on mobile robot path planning based on dynamic artificial potential field method. Comput. Meas. Control 25, 164–166 (2017)

    Google Scholar 

  22. Zhang, Y.L., Liu, Z.H., Chang, L.: A new adaptive artificial potential field and rolling window method for mobile robot path planning. In: Editorial Department of control and decision making, 2017 29th Chinese Control and Decision Conference (CCDC), Chongqing, pp. 7144–7148. IEEE (2017)

    Google Scholar 

  23. Abdalla, T.Y., Abed, A.A., Ahmed, A.A.: Mobile robot navigation using PSO-optimized fuzzy artificial potential field with fuzzy control. J. Intell. Fuzzy Syst. 32, 3893–3908 (2016)

    Article  Google Scholar 

  24. Ying, Z., Yuanpeng, L., Yawan, Z., Weijian, L.: Path planning of handling robot based on improved artificial potential field method. Electron. Meas. Technol. 43, 101–104 (2020)

    Google Scholar 

  25. Liu, Z.: Research and application of AGV path planning based on particle swarm optimization and artificial potential field method. Shenzhen University (2018)

    Google Scholar 

  26. Xu, Y.: Hybrid path planning for mobile robot based on particle swarm optimization and improved artificial potential field method. Zhejiang University (2013)

    Google Scholar 

  27. Zhong, M., Yang, Y., Dessouky, Y., Postolache, O.: Multi-AGV scheduling for conflict-free path planning in automated container terminals. Comput. Ind. Eng. 142, 106371 (2020)

    Google Scholar 

  28. Zhijun, W.: Dynamic refinement of robot navigation path and planning of flower pollination algorithm. Mech. Des. Manuf. 03, 288–292 (2021)

    Google Scholar 

  29. Thi Thoa Mac: A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization. Appl. Soft Comput. 59, 68–76 (2017)

    Article  Google Scholar 

  30. Xuan, Y.: Laser ablation manipulator coverage path planning method based on an improved ant colony algorithm. Appl. Sci. 10, 8641 (2020)

    Article  Google Scholar 

  31. Zhang, Z., zhangbohui, representative contention, “multi AGV conflict free path planning based on dynamic priority strategy,” Computer application research, pp. 1–5. https://doi.org/10.19734/j.issn.1001-3695.2020.08.0221

  32. Xue, Y., Jian-Qiao, S.: Solving the path planning problem in mobile robotics with the multi-objective evolutionary algorithm. Appl. Sci. 8, 9 (2018)

    Google Scholar 

  33. Liao, K.: Research on multi AGV path planning optimization algorithm and scheduling system. Hefei University of technology (2020)

    Google Scholar 

  34. Jie, W.: Research on path planning and collision avoidance strategy of multi AGV in intelligent warehouse, Shandong University of science and technology (2020)

    Google Scholar 

  35. Hu, Z., Cheng, L., Zhang, J., Wang, C.: Path planning of mobile robot based on improved genetic algorithm under multiple constraints. J. Chongqing Univ. Posts Telecommun. 06, 1–8 (2021)

    Google Scholar 

  36. Kim, K.H., Bae, J.W.: A Look-Ahead Dispatching Method for Automated Guided Vehicles in Automated Port Container Terminals. Inforvis (2004)

    Google Scholar 

  37. Lopes, T.C., Sikora, C., Molina, R.G.: Balancing a robotic spot welding manufacturing line: an industrial case study. Eur. J. Oper. Res. 263, 1033–1048 (2017)

    Article  Google Scholar 

  38. Yuan, R., Dong, T., Li, J.: Research on the collision-free path planning of multi-AGVs system based on improved A*algorithm, Inventi Impact - Algorithm (2017)

    Google Scholar 

  39. Chen, Q.: Research on optimal path planning combined with obstacle avoidance and its application in delivery car. Guangdong University of technology (2019)

    Google Scholar 

  40. Gao, Y., Wei, Z., Gong, F.: Dynamic path planning for underwater vehicles based on modified artificial potential field method. In: Proceeding of 2013 Fourth International Conference on Digital Manufacturing and Automation (ICDMA), Shinan. IEEE (2013)

    Google Scholar 

  41. Jiao, C., Jia, C., Qing, L.: Path planning of mobile robot based on improved a * and dynamic window method. Computer integrated manufacturing system, pp. 1–17 (2021). http://kns.cnki.net/kcms/detail/11.5946.TP.20201026.1053.026.html

  42. He, R.: Research on vehicle routing planning algorithm based on genetic algorithm. Beijing Jiaotong University (2020)

    Google Scholar 

  43. Guo, E., Liu, N., Wu, L., Wu, Z.: An AGV path planning method based on genetic algorithm. Sci. Technol. Innov. Prod. 08, 87–88 + 91 (2016)

    Google Scholar 

  44. Dang, H., Sun, X.: Research on AGV path optimization based on genetic algorithm. Electron. Products World 27, 48–51 + 73 (2020)

    Google Scholar 

  45. Gu, Y., Duan, J., Yuan, Y., Su, Y.: Multi objective path planning method for storage robot based on genetic algorithm. Logistics Technol. 39, 100–105 (2020)

    Google Scholar 

  46. Li, Q.: Genetic algorithm for path planning of AGV. Guangdong University of technology (2011)

    Google Scholar 

  47. Li, M.: Research on path planning of mobile robot based on improved genetic algorithm. Anhui Engineering University (2017)

    Google Scholar 

  48. Lamini, C., Benhlima, S., Elbekri, A.: Genetic algorithm based approach for autonomous mobile robot path planning. Procedia Comput. Sci. 127, 127 (2018)

    Google Scholar 

  49. Nazarahari, M., Khanmirza, E., Doostie, S.: Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm. Expert Syst. Appl. 115, 106–120 (2019)

    Article  Google Scholar 

  50. Liang, C.: Path planning and navigation based on improved genetic algorithm under multiple constraints. Chongqing University of Posts and Telecommunications (2020)

    Google Scholar 

  51. Yang, C., Zhang, T., Pan, X., Hu, M.: Multi-objective mobile robot path planning algorithm based on adaptive genetic algorithm. Technical Committee on control theory, Chinese Association of Automation, pp. 7 (2019)

    Google Scholar 

  52. Crossland, A.F., Jones, D., Wade, N.S.: Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing. Int. J. Electr. Power Energy Syst. 59, 103–110 (2014)

    Google Scholar 

  53. Bo, S., Jiang, P., Genrong, Z., Dianyong, D.: AGV path planning based on improved genetic algorithm. Comput. Eng. Des. 41, 550–556 (2020)

    Google Scholar 

  54. Yang, L.: Research on Robot Path Planning Based on Genetic Algorithm. Yunnan University (2019)

    Google Scholar 

  55. Deng, X., Zhang, L., Lin, H.: Pheromone mark ant colony optimization with a hybrid node-based pheromone update strategy. Neurocomputing 143, 46–53 (2015)

    Article  Google Scholar 

  56. Chen, C.-C., Shen, L.P.: Improve the accuracy of recurrent fuzzy system design using an efficient continuous ant colony optimization. Int. J. Fuzzy Syst. 20, 817–834 (2018)

    Article  Google Scholar 

  57. Yuanyi, C., Xiangming, Z.: Path planning of robot based on improved ant colony algorithm in computer technology. J. Phys. Conf. Ser. 1744, 4 (2021)

    Google Scholar 

  58. Zohreh, M., Van John, G., Abolghasem, S.N.: An improved ant colony optimization-based algorithm for user-centric multi-objective path planning for ubiquitous environments. Geocarto Int. 36, 137–154 (2021)

    Google Scholar 

  59. Anwar, A.Z., Han, Z., Bo, H.W.: Cooperative path planning of multiple UAVs by using max–min ant colony optimization along with cauchy mutant operator. Fluctuation Noise Lett. 20, 01 (2021)

    Google Scholar 

  60. Li, X.: Research on the application of improved ant colony algorithm in intelligent car path planning, Anhui Engineering University (2020)

    Google Scholar 

  61. Hsu, C.-C., Wang, W.-Y., Chien, Y.-H., Hou, R.-Y.: FPGA implementation of improved and colony optimization algorithm based on pheromone diffusion mechanism for path planning. J. Marine Sci. Technol. 26, 170–179 (2018)

    Google Scholar 

  62. Sangeetha, V., Krishankumar, R., Ravichandran, K.S., Kar, S.: Energy-efficient green ant colony optimization for path planning in dynamic 3D environments. Soft Comput. 25, 1–21 (2021)

    Google Scholar 

  63. Boxin, G., Yuhai, Z., Yuan, L.: An ant colony optimization based on information entropy for constraint satisfaction problems. Entropy (Basel, Switzerland) 21, 8 (2019)

    Google Scholar 

  64. Jing, Y.: Mobile robot path planning based on improved ant colony optimization algorithm. In: Proceedings of the 39th China Control Conference, vol. 2 (2020)

    Google Scholar 

  65. Deqiang, J., Che, L., Zerui, L., Dinghao, W.: An improved ant colony algorithm for TSP application. J. Phys: Conf. Ser. 1802, 3 (2021)

    Google Scholar 

  66. Jiang, C., Fu, J., Liu, W.: Research on vehicle routing planning based on adaptive ant colony and particle swarm optimization algorithm. Int. J. Intell. Transp. Syst. Res. 19, 1–9 (2020)

    Google Scholar 

  67. Wang, Y., Feng, X., Yulei, L., Xiang, Z.: Research on path planning of autopilot car based on improved potential field ant colony algorithm. Manuf. Autom. 41, 70–74 (2019)

    Google Scholar 

  68. Mandava, R.K., Bondada, S., Vundavilli, P.R.: An optimized path planning for the mobile robot using potential field method and PSO algorithm. Soft Computing for Problem Solving, pp. 139–150. Springer, Berlin (2019)

    Google Scholar 

  69. Song, B., Wang, Z., Zou, L.: On global smooth path planning for mobile robots using a novel multimodal delayed PSO algorithm. Cogn. Comput. 9, 5–17 (2017)

    Article  Google Scholar 

  70. Li, G., Chou, W.: Path planning for mobile robot using self-adaptive learning particle swarm optimization. Sci. China (Inf. Sci.) 61, 267–284 (2018)

    Google Scholar 

  71. Zeng, N.: Path planning for intelligent robot based on switching local evolutionary PSO algorithm. Assembly Autom. 36, 120–126 (2016)

    Article  Google Scholar 

  72. Thanmaya, P., Veeramachaneni, K., Mohan, C.K.: Fitness-distance-ratio based particle swarm optimization. In: Proceedings of the IEEE Congress on Swarm, Intelligence Symposium, vol. 2, pp. 174–181 (2003)

    Google Scholar 

  73. Liang, J.J., Qin, A.K., Suganthan, P.N.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10, 281–295 (2006)

    Article  Google Scholar 

  74. Shao, P., Wu, Z.: An improved particle swam optimization algorithm based on trigonometric sine factor. J. Chinese Comput. Syst. 36, 156–161 (2015)

    Google Scholar 

  75. Jialin, C., Guoliang, W., Tian, X.: Smooth path planning of mobile robot based on improved particle swarm optimization algorithm. Miniature Microcomput. Syst. 40, 2550–2555 (2019)

    Google Scholar 

  76. Ma, Y., Wang, H., Xie, Y., Guo, M.: Path planning for multiple mobile robots under double-warehouse. Inf. Sci. 278, 357–379 (2014)

    Article  Google Scholar 

  77. Das, P.K., Jena, P.K.: Multi-robot path planning using improved particle swarm optimization algorithm through novel evolutionary operators. Appl. Soft Comput. J. 92, July 2020

    Google Scholar 

  78. Ma, Y., Li, C.: Path planning and tracking for multi-robot system based on improved PSO algorithm. In: 2011 International Conference on Mechatronic Science, Electric Engineering and Computer, Jilin, pp. 1667–1670 (2011)

    Google Scholar 

  79. Zhang, Y., Lu, G.: Research on logistics distribution path optimization based on hybrid particle swarm optimization. Packag. Eng. 05, 10–12 (2007)

    Google Scholar 

  80. Mousavi, M., Yap, H.J., Musa, S.N., Tahriri, F., Dawal, S.Z.M.: Multi-Objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization. PLoS ONE 12, 16–17 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Beijing Institute of Graphic Communication, Beijing, China

    Ye Liu, Yanping Du, Shuihai Dou, Lizhi Peng & Xianyang Su

Authors
  1. Ye Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanping Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuihai Dou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lizhi Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xianyang Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuihai Dou .

Editor information

Editors and Affiliations

  1. Beijing Jiaotong University, Beijing, China

    Xianliang Shi

  2. Budapest University of Technology and Economics, Budapest, Hungary

    Gábor Bohács

  3. Beijing Jiaotong University, Beijing, China

    Yixuan Ma

  4. Beijing Jiaotong University, Beijing, China

    Daqing Gong

  5. Beijing Jiaotong University, Beijing, China

    Xiaopu Shang

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, Y., Du, Y., Dou, S., Peng, L., Su, X. (2022). Research Summary of Intelligent Optimization Algorithm for Warehouse AGV Path Planning. In: Shi, X., Bohács, G., Ma, Y., Gong, D., Shang, X. (eds) LISS 2021. Lecture Notes in Operations Research. Springer, Singapore. https://doi.org/10.1007/978-981-16-8656-6_9

Download citation

Publish with us

Policies and ethics

Search

Navigation