Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Dynamic chain for scheduling of the multi-AGV systems with load-aware motion profiling
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 31 January 2026

Dynamic chain for scheduling of the multi-AGV systems with load-aware motion profiling

  • Thanh Phuong Nguyen1,
  • Hung Nguyen1,
  • Duc Minh Phan2,3 &
  • …
  • Ha Quang Thinh Ngo4 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Mathematics and computing

Abstract

In dynamic warehouse environments, conventional multi-AGV systems would adopt fixed-motion planning that does not take into account the physical constraints of vehicles and products, and therefore results in random collisions and inefficient routing. In order to deal with this, this investigation introduces a novel two-stage scheduling approach for ensuring sustainable and collision-free AGV coordination. First, the Dynamic Traveling Time estimation for AGV (DTT-AGV) algorithm estimates real travel times among neighbouring nodes accounting for acceleration, deceleration, and load conditions. Second, the Arrival Time Chaining for AGV path (ATC-AGV) scheme schedules dynamically the arrival time of each AGV at future nodes based on real-time system states and minimum safe-distance conditions to avoid conflicts. Both methods are validated on a grid-based warehouse layout with a hybrid simulation framework integrating kinematic modelling and system-level control. From these experiments, its results demonstrate that our innovative approach increases time accuracy, reduces tracking errors, and ensures safety in multi-AGV operation conditions. This method possesses practical value for scalable deployment in logistics and smart manufacturing systems.

Data availability

All data generated or analysed during this study are included in this published article and its figures/tables.

References

  1. Aravindaraj, K. & Chinna, P. R. A systematic literature review of integration of industry 4.0 and warehouse management to achieve Sustainable Development Goals (SDGs). Cleaner logistics and supply chain, 5, 100072. (2022).

  2. Masuduzzaman, M., Nugraha, R. & Shin, S. Y. UAV-AGV cooperated remote toxic gas sensing and automated alarming scheme in smart factory. Comput. Commun. 226, 107923 (2024).

    Google Scholar 

  3. Zhou, Y. & Huang, N. Airport AGV path optimization model based on ant colony algorithm to optimize Dijkstra algorithm in urban systems. Sustainable Computing: Inf. Syst. 35, 100716 (2022).

    Google Scholar 

  4. Ma, E., Bao, Y., Huang, L., Wang, D. & Kim, M. When a robot makes your dinner: a comparative analysis of product level and customer experience between the US and Chinese robotic restaurants. Cornell Hospitality Q. 64 (2), 184–211 (2023).

    Google Scholar 

  5. Ito, N., Okuda, H. & Suzuki, T. Configuration-aware model predictive motion planning for Tractor–Trailer mobile robot. Adv. Robot. 37 (5), 329–343 (2023).

    Google Scholar 

  6. Le, T. S., Nguyen, T. P., Nguyen, H. & Ngo, H. Q. T. Integrating both routing and scheduling into motion planner for multivehicle system. IEEE Can. J. Electr. Comput. Eng. 46 (1), 56–68 (2023).

    Google Scholar 

  7. Nguyen, T. L., Ngo, H. Q. T., Nguyen, T. P. & Nguyen, H. A Novel Platform of Autonomous Vehicle in Multi-Disciplinary Industry. In 2019 International Conference on System Science and Engineering (ICSSE) (pp. 517–521). IEEE. (2019), July.

  8. Ellithy, K., Salah, M., Fahim, I. S. & Shalaby, R. AGV and industry 4.0 in warehouses: a comprehensive analysis of existing literature and an innovative framework for flexible automation. Int. J. Adv. Manuf. Technol. 134 (1), 15–38 (2024).

    Google Scholar 

  9. Reis, W. P. N. D., Couto, G. E. & Junior, O. M. Automated guided vehicles position control: a systematic literature review. J. Intell. Manuf. 34 (4), 1483–1545 (2023).

    Google Scholar 

  10. Nguyen, T. P., Nguyen, H. & Ngo, H. Q. T. Towards sustainable scheduling of a multi-automated guided vehicle system for collision avoidance. Comput. Electr. Eng. 120, 109824 (2024).

    Google Scholar 

  11. Sun, Y., Fang, M. & Su, Y. AGV path planning based on improved Dijkstra algorithm. In Journal of Physics: Conference Series (Vol. 1746, No. 1, p. 012052). IOP Publishing. (2021).

  12. Li, S., Zhou, Q., Jiang, J., Lu, X. & Yu, Z. MPC-based motion control of AGV with improved A* and artificial potential field. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 09544070241264360. (2024).

  13. Deng, R., Yan, R., Huang, P., Shi, Z. & Zhong, Y. A distributed auction algorithm for task assignment with robot coalitions. IEEE Trans. Robotics. 40, 4787–4804 (2024).

  14. Verma, P., Olm, J. M., Suárez, R. & Toldrá, P. Improved dynamic resource reservation-based AGV traffic control with optimized task allocation. In 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1–4). IEEE. (2024), September.

  15. Yuan, M. et al. Research on flexible job shop scheduling problem with AGV using double DQN. J. Intell. Manuf. 36 (1), 509–535 (2025).

    Google Scholar 

  16. Szczepanski, R., Tarczewski, T. & Erwinski, K. Energy efficient local path planning algorithm based on predictive artificial potential field. IEEE Access. 10, 39729–39742 (2022).

    Google Scholar 

  17. Li, L., Li, Y., Liu, R., Zhou, Y. & Pan, E. A Two-stage stochastic programming for AGV scheduling with random tasks and battery swapping in automated container terminals. Transp. Res. E. 174, 103110 (2023).

    Google Scholar 

  18. Niu, H., Wu, W., Xing, Z., Wang, X. & Zhang, T. A novel multi-tasks chain scheduling algorithm based on capacity prediction to solve AGV dispatching problem in an intelligent manufacturing system. J. Manuf. Syst. 68, 130–144 (2023).

    Google Scholar 

  19. Maoudj, A., Kouider, A. & Christensen, A. L. The capacitated multi-AGV scheduling problem with conflicting products: model and a decentralized multi-agent approach. Robot. Comput. Integr. Manuf. 81, 102514 (2023).

    Google Scholar 

  20. Qiuyun, T., Hongyan, S., Hengwei, G. & Ping, W. Improved particle swarm optimization algorithm for AGV path planning. Ieee Access. 9, 33522–33531 (2021).

    Google Scholar 

  21. Chen, Y., Zhu, Y. & Lee, K. Y. AGV path planning based on dynamic priority method and environmental weight A-star. J. Control Decis. 1–14 (2024).

  22. Yang, M., Bian, Y., Ma, L., Liu, G. & Zhang, H. Research on traffic control algorithm based on multi-AGV path planning. In 2021 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 697–702). IEEE. (2021), October.

  23. Zeng, C., Tang, J. & Fan, Z. P. Auction-based Cooperation mechanism for cell part scheduling with transportation capacity constraint. Int. J. Prod. Res. 57 (12), 3831–3846 (2019).

    Google Scholar 

  24. Quinton, F., Grand, C. & Lesire, C. Market approaches to the multi-robot task allocation problem: a survey. J. Intell. Robotic Syst. 107 (2), 29 (2023).

    Google Scholar 

  25. Nguyen, H., Nguyen, T. P. & Ngo, H. Q. T. Using EtherCAT technology to launch online automated guided vehicle manipulation with unity-based platform for smart warehouse management. IET Control Theory Appl. 18 (2), 229–243 (2024).

    Google Scholar 

  26. Wu, X., Zhang, Q., Bai, Z. & Guo, G. A self-adaptive safe A* algorithm for AGV in large-scale storage environment. Intel. Serv. Robot. 17 (2), 221–235 (2024).

    Google Scholar 

  27. Riazi, S., Bengtsson, K. & Lennartson, B. Energy optimization of large-scale AGV systems. IEEE Trans. Autom. Sci. Eng. 18 (2), 638–649 (2020).

    Google Scholar 

  28. Yang, X., Hu, H. & Cheng, C. Collaborative scheduling of handling equipment in automated container terminals with limited AGV-mates considering energy consumption. Adv. Eng. Inform. 65, 103133 (2025).

    Google Scholar 

  29. Korayem, A. H. et al. Hitch angle Estimation of a towing vehicle with arbitrary configuration. IEEE Trans. Intell. Transp. Syst. 23 (7), 7535–7546 (2021).

    Google Scholar 

  30. Shi, Y. & Li, M. Shortest path planning for electric vehicles considering load. In Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021) (Vol. 12058, pp. 345–350). SPIE. (2021), December.

  31. Tsiogas, E., Kleitsiotis, I., Kostavelis, I., Kargakos, A., Giakoumis, D., Bosch-Jorge,M., … Tzovaras, D. (2021, September). Pallet detection and docking strategy for autonomous pallet truck AGV operation. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3444–3451). IEEE.

  32. Rolander, B., Forsman, M., Ghafouri, B., Abtahi, F. & Wåhlin, C. Measurements and observations of movements at work for warehouse forklift truck operators. Int. J. Occup. Saf. Ergon. 28 (3), 1840–1848 (2022).

    Google Scholar 

  33. Zhou, B. & He, Z. A novel hybrid-load AGV for JIT-based sustainable material handling scheduling with time window in mixed-model assembly line. Int. J. Prod. Res. 61 (3), 796–817 (2023).

    Google Scholar 

  34. Ngo, H. Q. T., Nguyen, H. & Nguyen, T. P. Fenceless collision-free avoidance driven by visual computation for an intelligent cyber–physical system employing both single-and double-S trajectory. IEEE Trans. Consum. Electron. 69 (3), 622–639 (2023).

    Google Scholar 

  35. Chen, J., Zhang, X., Peng, X., Xu, D. & Peng, J. Efficient routing for multi-AGV based on optimized Ant-agent. Comput. Ind. Eng. 167, 108042 (2022).

    Google Scholar 

  36. Wang, Y., Hao, Y., Qu, Y. & Ma, Y. A Collaborative Method for Multi AGVs Scheduling and Path Planning Considering Dynamic Arrival of Task. In Proceedings of the 2024 3rd International Symposium on Intelligent Unmanned Systems and Artificial Intelligence (pp. 1–7). (2024), May.

  37. Fan, G. & Jiang, Z. Approach for scheduling automatic guided vehicles considering equipment failure and power management. J. Mar. Sci. Appl. 22 (3), 624–635 (2023).

    Google Scholar 

  38. Hu, Y., Yang, H. & Huang, Y. Conflict-free scheduling of large-scale multi-load AGVs in material transportation network. Transp. Res. E. 158, 102623 (2022).

    Google Scholar 

Download references

Funding

The authors received no funding for this work.

Author information

Authors and Affiliations

  1. HUTECH Institute of Engineering, HUTECH University, Ho Chi Minh City, Vietnam

    Thanh Phuong Nguyen & Hung Nguyen

  2. Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, Dien Hong Ward, Ho Chi Minh City, Vietnam

    Duc Minh Phan

  3. Vietnam National University-Ho Chi Minh City (VNU-HCM), Linh Xuan Ward, Ho Chi Minh City, Vietnam

    Duc Minh Phan

  4. FPT University, Ho Chi Minh City, Vietnam

    Ha Quang Thinh Ngo

Authors
  1. Thanh Phuong Nguyen
    View author publications

    Search author on:PubMed Google Scholar

  2. Hung Nguyen
    View author publications

    Search author on:PubMed Google Scholar

  3. Duc Minh Phan
    View author publications

    Search author on:PubMed Google Scholar

  4. Ha Quang Thinh Ngo
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Ha Quang Thinh Ngo, Thanh Phuong Nguyen: Propose ideas and requirements for the review article.; support collecting articles on the related fields; supervise the synthesis process; edit the manuscript. Duc Minh Phan: build a table of contents for the article; collect documents, read, analyze, synthesize data; write the manuscript. Hung Nguyen: Support data collection and data synthesis. All authors reviewed the results and approved the final version of the manuscript.

Corresponding author

Correspondence to Ha Quang Thinh Ngo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nguyen, T.P., Nguyen, H., Phan, D.M. et al. Dynamic chain for scheduling of the multi-AGV systems with load-aware motion profiling. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37083-z

Download citation

  • Received: 21 August 2025

  • Accepted: 19 January 2026

  • Published: 31 January 2026

  • DOI: https://doi.org/10.1038/s41598-026-37083-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Multi-AGV system
  • Robotics
  • Collision avoidance
  • Smart logistics
  • Motion profile
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on Twitter
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics