Volume 26 Issue 6
Jun.  2026
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LIU Zhi-shuo, XU Jun-zhe, LI Yan-hua, LI Xin. Dynamic path planning method considering load balancing of road network for AGV sorting system[J]. Journal of Traffic and Transportation Engineering, 2026, 26(6): 209-220. doi: 10.19818/j.cnki.1671-1637.2026.120
Citation: LIU Zhi-shuo, XU Jun-zhe, LI Yan-hua, LI Xin. Dynamic path planning method considering load balancing of road network for AGV sorting system[J]. Journal of Traffic and Transportation Engineering, 2026, 26(6): 209-220. doi: 10.19818/j.cnki.1671-1637.2026.120
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Dynamic path planning method considering load balancing of road network for AGV sorting system

doi: 10.19818/j.cnki.1671-1637.2026.120

  • School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China

Funds:

National Natural Science Foundation of China U2333206

More Information
  • Corresponding author: LIU Zhi-shuo, associate professor, PhD, E-mail: zhsliu@bjtu.edu.cn
  • Received Date: 2025-05-19
  • Accepted Date: 2025-11-27
  • Rev Recd Date: 2025-10-11
  • Publish Date: 2026-06-28
    Abstract
  • To solve the path planning and road network congestion problems of automated guided vehicles (AGVs) in large-scale automatic sorting systems, by considering the load balancing and utilization rate of the road network, a path planning framework based on a sliding time window that integrated task-level global planning and action-level local adjustment was proposed. A multi-endpoint A* (A*-Ⅰ) algorithm considering the number of turns was designed to plan the global paths of AGVs entering the system. Based on the sliding time window framework, the position information of running AGVs in the system was updated to help them select actions to avoid path conflicts. By calculating the average passing speed of each road section in the road network every certain period, the road resistance factor matrix of each region in the road network was updated, and an A*-Ⅱ algorithm considering the road resistance factor was designed based on the A*-Ⅰ algorithm to adjust the local paths of AGVs. By combining the A*-Ⅰ and A*-Ⅱ algorithms, the conflict-free path planning of multiple AGVs in the automatic sorting system was realized, and the road network balancing and utilization rate of the sorting system were improved. Based on the cellular automata method, the operation and status update rules of AGVs in the system were determined, and a simulation framework for a large-scale AGV sorting system was constructed. Research results indicate that compared with the traditional global path planning method, the proposed path planning method reduces the load ratio of high-load nodes in the road network by 17% and the standard deviation of the overall road network load by 6.7%, effectively increasing the sorting quantity of goods per unit time. The road resistance factor weight and the road network status update cycle are the main factors affecting the system operation efficiency, and the system sorting efficiency is the best when road resistance factor weight is greater than 4, and road network status update cycle takes five time steps. The proposed method can effectively alleviate the road network congestion of large-scale AGV sorting systems, improve the system throughput and operation stability, and provide a feasible technical scheme for real-time path scheduling in intelligent warehousing and automatic sorting scenarios.

     

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