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AGVs are driverless robotic vehicles that picks up and delivers materials. How to improve the efficiency while preventing deadlocks is the core issue in designing AGV systems. In this paper, we propose an approach to tackle this problem.The proposed approach includes a traditional AGV scheduling algorithm, which aims at solving deadlock problems, and an artificial neural network based component, which predict future tasks of the AGV system, and make decisions on whether to send an AGV to the predicted starting location of the upcoming task,so as to save the time of waiting for an AGV to go to there first when the upcoming task is created. Simulation results show that the proposed method significantly improves the efficiency as against traditional method, up to 20% to 30%.
The traditional production paradigm of large batch production does not offer flexibility towards satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multi-variety and small-batch customi
The ability to use symbols is the pinnacle of human intelligence, but has yet to be fully replicated in machines. Here we argue that the path towards symbolically fluent artificial intelligence (AI) begins with a reinterpretation of what symbols are,
This article reviews the Once learning mechanism that was proposed 23 years ago and the subsequent successes of One-shot learning in image classification and You Only Look Once - YOLO in objective detection. Analyzing the current development of Artif
As artificial intelligence (AI) systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination. Within the AI research community, this topic remains less familiar to many researche
Meta-learning, or learning to learn, has gained renewed interest in recent years within the artificial intelligence community. However, meta-learning is incredibly prevalent within nature, has deep roots in cognitive science and psychology, and is cu