sis 22(2): e5

Research Article

Dynamic Weighted and Heat-map Integrated Scalable Information Path-planning Algorithm

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  • @ARTICLE{10.4108/eetsis.v9i5.1567,
        author={Shuhui Bi and Zhihao  Li and Mackenzie Brown and Lei Wang and Yuan Xu},
        title={Dynamic Weighted and Heat-map Integrated Scalable Information Path-planning Algorithm},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        keywords={Improved A* algorithm, Improved Reservation Form, Dynamic Weighted Table, Heat Map Algorithm},
  • Shuhui Bi
    Zhihao Li
    Mackenzie Brown
    Lei Wang
    Yuan Xu
    Year: 2022
    Dynamic Weighted and Heat-map Integrated Scalable Information Path-planning Algorithm
    DOI: 10.4108/eetsis.v9i5.1567
Shuhui Bi1, Zhihao Li1, Mackenzie Brown2, Lei Wang3,*, Yuan Xu1
  • 1: University of Jinan
  • 2: Edith Cowan University
  • 3: Intelligent Equipment Research Institute, Shandong, China
*Contact email:


Smart storage is widely used for its efficient storage and applications. For making dynamic decisions when robots conflict and eliminating robot conflicts and improving efficiency from a global perspective, path-planning Algorithm will be analyzed and improved by integrating dynamic weighted and heat-map algorithm based on the scalable information of multi-robot in this paper. Firstly, a small storage grid model applicable to a variety of storage modes is established. Second, in order to solve the frontal collision problem of robots, an improved reservation table is established, which greatly reduces the storage space occupied by the reservation table while improving the operation efficiency; the A* algorithm is improved to achieve the purpose of avoiding vertex conflict and edge conflict at the same time; dynamic weighting table is added to solve the multi-robot driving strategy of intersection conflict and ensure that the most urgent goods are out of the warehouse firstly; the heat map algorithm is appended to reasonably allocate tasks, avoiding congested areas and realizing the dynamic assignment of tasks. Finally, the simulation was done by the proposed path planning method, the average transportation time was reduced by 14.97% comparing with the traditional path algorithm.