Deep Learning-Augmented AGV Navigation and Coordination for Efficient Warehouse Operations

Authors

  • Chandra Jaiswal  Independent Researcher, USA.

DOI:

https://doi.org/10.32628/IJSRSET214415

Keywords:

Automated Guided Vehicles, Navigation, Warehouse Automation, Deep Learning, Artificial Intelligence, Coordination, Logistics Optimization

Abstract

In this modern world of Industry 4.0, meeting the high rise demand of the market as well as maintaining efficiency of the supply chain, it has become an essential part to undertake automated warehouse systems. Automated Guided Vehicles (AGVs) play a crucial role in enhancing internal transportation operations by increasing both precision and efficiency. Nevertheless, traditional AGVs frequently face challenges in dynamic and unpredictable warehouse settings due to their reliance on inflexible, rule-based navigation systems. To address these limitations, researchers and industry experts have begun to utilize deep learning, a branch of artificial intelligence recognized for its ability to recognize patterns and make decisions. By integrating deep learning algorithms, AGVs are now capable of processing real-time sensory information, allowing them to navigate autonomously, adjust to changes, and collaborate with other units more effectively. This concentrated research has highlighted the importance and effectiveness of AI and its role in enhancing logistics performance by incorporating deep learning in AGV systems.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2022-12-30

Issue

Volume 9, Issue 6, November-December-2022

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Chandra Jaiswal "Deep Learning-Augmented AGV Navigation and Coordination for Efficient Warehouse Operations" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 6, pp.411-421, November-December-2022. Available at doi : https://doi.org/10.32628/IJSRSET214415