Industrial production cannot be separated from product transportation, and transportation of products cannot be separated from transportation vehicles. The pathfinding of transportation vehicles is different from ordinary vehicles, and the factors to be considered are complex and not intuitive, and often difficult to quantify. Drivers of these transportation vehicles are often unable to accurately describe their needs. Sometimes, drivers also need to transport goods to multiple locations once in sequence, making transport line selection less easy. At this point, it is usually helpful to refer to the practices of people who are similar in all aspects to oneself. This paper attempts to integrate the experience of others with specific equations, and based on graph convolutional neural network recommendation algorithm and A* algorithm, proposed a pathfinding algorithm - ASGCN, which is suitable for unclear requirements, multiple targets in sequence, and reflects user preferences. This algorithm in order to achieve a balance between group selection, user preferences, and road cost savings. And finally achieve better results in terms of comprehensiveness than traditional pathfinding algorithm.


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    Titel :

    Pathfinding Algorithm for Transportation Vehicles Based on Recommended


    Beteiligte:
    Tian, Songlin (Autor:in) / Yang, Lei (Autor:in)


    Erscheinungsdatum :

    25.07.2023


    Format / Umfang :

    2196170 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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