KEYWORDS: Particles, Intelligence systems, Mobile robots, Particle swarm optimization, Simulations, Industry, Design and modelling, Information technology, Systems modeling, Automation
Traditional warehouse logistics has faced huge challenges with the development of China’s information technology industry. Intelligent logistics, especially intelligent warehousing, has become a hot research topic worldwide. Amazon Kiva system, a representative of intelligent warehouse handling systems based on multiple mobile robots, has revolutionized the warehousing industry with its “goods-to-man” picking model, which reduces the time and labor costs of finding goods. However, this model still has many limitations, such as low efficiency and scalability, when dealing with large-scale picking tasks. In this paper, we propose a novel intelligent warehouse handling system that overcomes these limitations and improves the system performance. We present the system architecture, design principles, and key algorithms of our system, and evaluate its effectiveness and robustness through simulations and experiments. Specifically, we propose an order task scheduling algorithm based on order adaptation and discrete particle swarm optimization, and a path planning algorithm based on 2D planar graph for the warehouse scenario. We show that our integrated algorithm can improve the system efficiency by solving the two key problems of order scheduling and route planning in the smart storage system.
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