Intelligent logistics management robot path planning algorithm integrating transformer and GCN network
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917953088258048 |
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| author | Luo, Hao Wei, Jianjun Zhao, Shuchen Liang, Ankai Xu, Zhongjin Jiang, Ruxue |
| author_facet | Luo, Hao Wei, Jianjun Zhao, Shuchen Liang, Ankai Xu, Zhongjin Jiang, Ruxue |
| contents | This research delves into advanced route optimization for robots in smart logistics, leveraging a fusion of Transformer architectures, Graph Neural Networks (GNNs), and Generative Adversarial Networks (GANs). The approach utilizes a graph-based representation encompassing geographical data, cargo allocation, and robot dynamics, addressing both spatial and resource limitations to refine route efficiency. Through extensive testing with authentic logistics datasets, the proposed method achieves notable improvements, including a 15% reduction in travel distance, a 20% boost in time efficiency, and a 10% decrease in energy consumption. These findings highlight the algorithm's effectiveness, promoting enhanced performance in intelligent logistics operations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02749 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Intelligent logistics management robot path planning algorithm integrating transformer and GCN network Luo, Hao Wei, Jianjun Zhao, Shuchen Liang, Ankai Xu, Zhongjin Jiang, Ruxue Robotics Artificial Intelligence This research delves into advanced route optimization for robots in smart logistics, leveraging a fusion of Transformer architectures, Graph Neural Networks (GNNs), and Generative Adversarial Networks (GANs). The approach utilizes a graph-based representation encompassing geographical data, cargo allocation, and robot dynamics, addressing both spatial and resource limitations to refine route efficiency. Through extensive testing with authentic logistics datasets, the proposed method achieves notable improvements, including a 15% reduction in travel distance, a 20% boost in time efficiency, and a 10% decrease in energy consumption. These findings highlight the algorithm's effectiveness, promoting enhanced performance in intelligent logistics operations. |
| title | Intelligent logistics management robot path planning algorithm integrating transformer and GCN network |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2501.02749 |