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https://ela.kpi.ua/handle/123456789/43234
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Перегляд Статті (ІПІ) за Автор "Likhouzova, Tetiana"

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    Robotic Warehouse Management System
    (International Review of Automatic Control, 2021) Likhouzova, Tetiana; Demianova, Yuliia
    The research task is to find a solution to the problem of reducing the risk of collisions while managing a set of auxiliary robots. This task can be solved in two ways. The first approach is the development of algorithms for choosing the motion trajectory of robots. The second one is to reduce the number of auxiliary robots to the minimum required in a certain amount of time. The study focuses on the second approach. Rigidly programmed systems, although they do the job, are not always flexible and adaptive. Systems that can independently analyze the state of certain data, find patterns, and predict, are more efficient and necessary for the further development of the industry. The study proposes a solution based on the use of a neural network in the management system of auxiliary work. An analytical unit was added to the control system to predict the optimal number of robots needed on the line by the number of applications. This gives the system a high level of flexibility in the overall loading and shipping process. Control systems with and without the analytical unit in two different scenarios are studied, both in the constant and randomized increment of applications. In both cases, the use of the analytical block in the control system allowed reducing the number of auxiliary robots in production. The experimental results show that the proposed solution gives the same amount of applications to be completed by fewer auxiliary robots in less time, and it results in reduction of the number of collisions during the movement of robots. The emergence of this structure improves navigation efficiency and allows reducing production maintenance costs. In addition, the results showed scalability for production. A well-established system for managing robotic devices can guarantee the efficiency of the production process.