Journal of Engineering, Project, and Production Management, 2026, 16(4), 2025-213
Dynamic Route Planning for Warehouse Robots Combining A-Star and Dynamic Window Approach
1
Engineer, Alibaba Cainiao Network Company, China, E-mail:
lijianjun_1983@126.com (corresponding author).
2
Professor, College of Electrical Engineering, Zhejiang University,
Hangzhou,310027, China, E-mail: jxiang@zju.edu.cn
Project Management
Received September 23, 2025; revised December 25, 2025; accepted December 25, 2025
Available online May 29, 2026
Abstract: Dynamic route planning for warehouse robots plays an important role in the development of logistics and warehousing. Accurate planning of robot routes can reduce labor costs and decrease energy consumption. However, traditional methods for warehouse robot route planning suffer from low efficiency, route deviations, and inaccurate navigation. This study utilizes the Improved Sparrow Search Algorithm (ISSA) to optimize route planning parameters and fill in missing critical information during the search process. It also combines Multi-Region Biased Sampling and a Key-Node Guidance algorithm (MRBS-KNG-A*) to highlight important areas, ultimately constructing a dynamic route planning model for warehouse robots. Experimental results show that the improved algorithm achieves a route length of 22.5m, a search time of 4.9s, and three turns in high-obstacle environments. These results outperform relative strategy optimization, Fourier Ring Correlation Resolution (FRC), and chaotic Particle Swarm Optimization (PSO) algorithms. Meanwhile, the constructed model significantly reduces the number of branch nodes, achieving 11 turns and a pathfinding success rate of 82.95%. Under the cooperation of multiple robots, the collision times, maximum curvature, iterative optimization time, and actual energy consumption of the research model for moving obstacles are 2 times, 0.72 m-1, 25.69 s, and 2.62 kW/h, respectively. The results indicate that the improved dynamic route planning model enhances both planning efficiency and accuracy. This study contributes to the precise construction of multi-destination route planning models, thereby improving the mobility efficiency of the storage robot.
Keywords: A*, algorithm, dynamic window approach, warehouse robot, route planning, improved sparrow search algorithm.
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Citation: Li, J. and Xiang, J. (2026). Dynamic Route Planning for Warehouse Robots Combining A-Star and Dynamic Window Approach. Journal of Engineering, Project, and Production Management, 16(4), 2025-213.
