mlsanityData to 7 Oct 2026

Intelligent logistics management robot path planning algorithm integrating transformer and GCN network

arXiv 2501.02749 ↗First posted 6 Jan 2025cs.RO cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 41 of its 91 references that are in the map. First posted on arXiv 6 Jan 2025.

Builds on

41 of 91 references.

References · 41

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29 more
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Citing papers per year: 2025: 4.

4 papers.

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