在仓库机器人中,用于集群顺序选择的路径优化,使用混合符号控制和生物启发的元启发方法.
Mete Özbaltan1, Serkan Çaşka2, Merve Yıldırım3
1Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, İzmir Bakırçay University, 35665 İzmir, Türkiye.
Biomimetics (Basel, Switzerland)
|October 28, 2025
概括
本研究介绍了一种混合模型,用于使用符号控制和自然启发的算法优化仓库机器人路径. 这种方法提高了效率,减少了集群内和整个仓库内的旅行路径.
科学领域:
- 机器人和自动化机器人与自动化
- 运营研究 运营研究
- 人工智能的人工智能
背景情况:
- 仓库订单采购在优化机器人路径查找方面面临着挑战.
- 现有的方法在大规模的集群机器人系统中往往缺乏效率.
- 将符号控制与元启发学相结合,提供了一个新的解决方案.
研究的目的:
- 为集群仓库机器人设计中路径优化的架构模型提出建议.
- 开发一种混合方法,结合符号控制和生物启发的元启发算法.
- 为了提高效率并减少仓库机器人的行程距离.
主要方法:
- 一种融合符号控制和元启发技术的混合方法.
- 使用生物启发的算法:海象优化算法 (WOA),熊猫优化算法 (POA) 和飞算法 (FFA).
- 制定集群管理作为安全和优化目标的离散合成任务.
主要成果:
- 与传统的元启发方法相比,混合方法实现了高达3.01%的较短集群内路径.
- 观察到的平均集群内路径改善率为1.2%.
- 该系统显示整个仓库的平均路径缩短了2.05%,超过了现有方法.
结论:
- 拟议的混合模型显著改善了仓库机器人中的路径优化.
- 象征性控制和自然灵感算法的集成提供了一个强大的和有效的解决方案.
- 实验结果验证了该方法的实际有效性和一致性.
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