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基于IMA-SURBF的任务行程时间预测方法用于异质AGV系统的任务调度
Jingjing Zhai1, Xing Wu1, Qiang Fu2
1College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Yudao Street, Nanjing 210016, China.
Biomimetics (Basel, Switzerland)
|August 27, 2025
概括
准确的任务行程时间预测 (T3P) 对异质自动引导车辆 (AGV) 系统至关重要. 拟议的IMA-SURBF框架通过使用仿生算法动态调整RBF神经网络参数来提高T3P的准确性.
科学领域:
- 运营研究
- 人工智能
- 机器人技术
背景情况:
- 不同类型的自动引导车辆 (AGV) 系统提供灵活性,但需要精确的任务行程时间预测 (T3P) 以提高效率.
- 由于任务相关性和动态输入/输出维度,现有的T3P方法面临挑战.
研究的目的:
- 在异质AGV系统中开发一种以仿生学为灵感的新型框架,用于准确的T3P.
- 解决T3P中的输入/输出维度和任务相关性的动态性质.
主要方法:
- 对于T3P,使用一个辐射基函数 (RBF) 神经网络,其输入/输出维度是动态决定的.
- 一个改进的mayfly算法 (IMA) 优化了RBF的初始参数.
- 选择性更新策略可以改进参数更新.
主要成果:
- 拟议的IMA-SURBF框架显示了T3P的高精度和效率.
- 在复杂的装配线场景中的模拟实验验证了模型的性能.
- 这种方法优于现有的基于深度学习的模型.
结论:
- 在复杂的AGV环境中,IMA-SURBF框架为T3P提供了有效的解决方案.
- 生物仿真方法为优化AGV系统运行提供了一个有希望的方向.
- 准确的T3P对于最大限度地提高异质AGV系统的灵活性和效率至关重要.
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