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Intelligent Logistics Management Robot Path Planning Algorithm Integrating Transformer and GCN Network
1
Department of Computer Science, Rutgers University, NJ 08854, United States
2
Department of Computer Science and Engineering, Washington University in St. Louis, St Louis, United States
3
Duke Electrical & Computer Engineering, Duke University, Durham, NC 27708, United States
4
Stevens Institute of Technology, Newark, CA 94560, United States
5
University of Michigan-Dearborn, MI 48128, United States
6
Northeastern University, Seattle, WA 98109, United States
* Corresponding Author:
Ruxue Jiang,
[email protected]
Volume 2, Issue 4
2024
Received: 22 November 2024
Accepted: 24 December 2024
Published: 31 December 2024
Article Information
Published in
ICCK Transactions on Internet of Things
Volume/Issue
Volume 2, Issue 4, 2024
Pages
95-112
Abstract
This study focuses on optimizing multimodal robot route planning in intelligent logistics management by integrating Transformer models, Graph Neural Networks (GNNs), and Generative Adversarial Networks (GANs). Using a graph structure representing map information, cargo distribution, and robot states, spatial and resource constraints are considered to optimize paths. Extensive simulations based on real logistics datasets demonstrate significant improvements over traditional methods, with an average 15\% reduction in path length, 20% improvement in time efficiency, and 10% reduction in energy consumption. These results underscore the effectiveness and superiority of the proposed multimodal path planning algorithm, offering robust support for advancing intelligent logistics management.
Graphical Abstract
Keywords
multimodal robots
deep path planning
transformer model
graph neural network
generative adversarial network
Funding
This work was supported without any funding.
References
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APA Style
Luo, H., Wei, J., Zhao, S., Liang, A., Xu, Z., & Jiang, R. (2024). Intelligent Logistics Management Robot Path Planning Algorithm Integrating Transformer and GCN Network. ICCK Transactions on Internet of Things, 2(4), 95–112. https://doi.org/10.62762/TIOT.2024.918236
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TY - JOUR AU - Luo, Hao AU - Wei, Jianjun AU - Zhao, Shuchen AU - Liang, Ankai AU - Xu, Zhongjin AU - Jiang, Ruxue PY - 2024 DA - 2024/12/31 TI - Intelligent Logistics Management Robot Path Planning Algorithm Integrating Transformer and GCN Network JO - ICCK Transactions on Internet of Things T2 - ICCK Transactions on Internet of Things JF - ICCK Transactions on Internet of Things VL - 2 IS - 4 SP - 95 EP - 112 DO - 10.62762/TIOT.2024.918236 UR - https://www.icck.org/article/abs/TIOT.2024.918236 KW - multimodal robots KW - deep path planning KW - transformer model KW - graph neural network KW - generative adversarial network AB - This study focuses on optimizing multimodal robot route planning in intelligent logistics management by integrating Transformer models, Graph Neural Networks (GNNs), and Generative Adversarial Networks (GANs). Using a graph structure representing map information, cargo distribution, and robot states, spatial and resource constraints are considered to optimize paths. Extensive simulations based on real logistics datasets demonstrate significant improvements over traditional methods, with an average 15\% reduction in path length, 20% improvement in time efficiency, and 10% reduction in energy consumption. These results underscore the effectiveness and superiority of the proposed multimodal path planning algorithm, offering robust support for advancing intelligent logistics management. SN - pending PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Luo2024Intelligen,
author = {Hao Luo and Jianjun Wei and Shuchen Zhao and Ankai Liang and Zhongjin Xu and Ruxue Jiang},
title = {Intelligent Logistics Management Robot Path Planning Algorithm Integrating Transformer and GCN Network},
journal = {ICCK Transactions on Internet of Things},
year = {2024},
volume = {2},
number = {4},
pages = {95-112},
doi = {10.62762/TIOT.2024.918236},
url = {https://www.icck.org/article/abs/TIOT.2024.918236},
abstract = {This study focuses on optimizing multimodal robot route planning in intelligent logistics management by integrating Transformer models, Graph Neural Networks (GNNs), and Generative Adversarial Networks (GANs). Using a graph structure representing map information, cargo distribution, and robot states, spatial and resource constraints are considered to optimize paths. Extensive simulations based on real logistics datasets demonstrate significant improvements over traditional methods, with an average 15\\% reduction in path length, 20\% improvement in time efficiency, and 10\% reduction in energy consumption. These results underscore the effectiveness and superiority of the proposed multimodal path planning algorithm, offering robust support for advancing intelligent logistics management.},
keywords = {multimodal robots, deep path planning, transformer model, graph neural network, generative adversarial network},
issn = {pending},
publisher = {Institute of Central Computation and Knowledge}
}
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