自动引导车辆路径规划的生物灵感反应方法:一篇评论
Shiwei Lin1, Jianguo Wang2, Xiaoying Kong3
1School of Computer Engineering, Jimei University, Xiamen 361000, China.
Biomimetics (Basel, Switzerland)
|January 27, 2026
概括
本综述分析了自动引导车辆 (AGV) 路径规划的反应算法. 人工智能算法在动态环境中表现出色,而群体智能在更简单的设置中是有效的.
科学领域:
- 机器人和自动化机器人与自动化
- 人工智能的人工智能
- 运营研究 运营研究
背景情况:
- 自动引导车辆 (AGV) 路径规划对于优化物流和制造业务至关重要.
- 反应式算法提供动态路径寻找功能,与经典方法形成鲜明对比.
- 需要对最近反应式AGV路径规划方面的进展进行全面审查.
研究的目的:
- 审查和分类2019年至2025年期间发表的AGV路径规划的反应算法.
- 分析群体智能和基于人工智能的方法的趋势和应用.
- 在各种环境中确定不同反应方法的优点和局限性.
主要方法:
- 对123篇相关论文进行系统的文献审查.
- 将算法分类为群集智能 (例如PSO,ACO,GA) 和人工智能 (例如NN,RL,模糊逻辑).
- 对实施特征的分析,包括在线能力和多AGV系统应用.
主要成果:
- 反应式算法越来越多地被研究,有81篇论文专注于这个类别.
- 群集智能算法 (44篇论文) 对于具有快速融合的静态环境是有效的.
- 人工智能算法 (37篇论文) 在动态环境和在线实施中表现出有效性.
结论:
- 集群智能算法非常适合静态或简化动态环境,因为其复杂度低,并快速融合.
- 人工智能算法显示出复杂的动态环境和实时应用程序的前景,尽管存在强度挑战.
- 未来的研究应该解决模拟到真实的差距,并增强基于AI的AGV路径规划的稳定性.
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