融合Transformer与GCN的机器人路径规划算法，提升物流效率。
Intelligent logistics management robot path planning algorithm integrating transformer and GCN network
- 用图结构建模地理、货物与机器人动态，结合Transformer和GCN优化路径。
- 实测显示旅行距离减少15%，时间效率提升20%，能耗降低10%。
- 适合智能物流、机器人调度领域研究者参考，尤其关注路径优化者。
本研究针对智能物流中机器人路径优化问题，融合Transformer架构、图神经网络（GNNs）与生成对抗网络（GANs）。通过图结构表示包含地理数据、货物分配及机器人动力学的信息，同时考虑空间与资源约束，以提升路径规划效率。在真实物流数据集上进行大量测试，结果表明该方法使旅行距离减少15%，时间效率提升20%，能耗下降10%。实验验证了算法在智能物流系统中的有效性，推动了机器人作业性能的整体优化。
原文摘要 · Abstract (English)
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.
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