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      Hidden Traction AGV Helps Intelligent Production in Automotive Parts Manufacturing Industry

      The automotive industry is the most mature field for AGV applications and also one of the largest markets for AGV currently. With the arrival of the Industry 4.0 era and the increasing cost of labor, the automotive manufacturing industry has encountered many pain points and is facing intelligent reforms. At the same time, the continuous acceleration of production pace and the demand for small batches, multiple varieties, and personalized automotive parts have made it difficult for traditional automotive manufacturing factories to meet the demand. However, upgrading production lines requires a lot of renovation time, and the manufacturing industry has high reliability requirements for production lines. The intelligent transformation of factories has become an extremely challenging problem, and intelligent handling is of utmost importance.

      2020052250239693

        A large enterprise in Ningbo is an automobile parts manufacturer, and frequent logistics operations consume a lot of manpower and have low efficiency. It urgently needs to improve the level of workshop automation. Quick robots provide intelligent handling solutions based on customer needs. The use of a one-way latent traction AGV car based on natural navigation has helped the enterprise achieve logistics automation between warehouses and production lines.

      2020052250401489

        Unidirectional latent traction AGV

        1. Optimize task allocation and path planning for AGVs, ultimately completing material transportation between production lines and storage locations;


        2. When the AGV battery is low and does not affect normal operation, the scheduling system will dispatch the robot to the designated charging station location for charging, ensuring uninterrupted logistics in the workshop;


        3. Compact structure, flexible walking, and traction capacity of over 1 ton, widely used in the automotive manufacturing industry.

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