生物粒子群集优化和强化学习算法用于动态工业环境中自动引导车辆的路径规划
Shiwei Lin1, Jianguo Wang2, Bomin Huang3
1School of Computer Engineering, Jimei University, Xiamen, 361000, Fujian, China. Shiwei.Lin@jmu.edu.cn.
Scientific reports
|January 3, 2025
概括
一个新的生物颗粒群集优化 (BPSO) 算法增强了自动引导车辆 (AGV) 的路径规划. 这种BPSO-RL方法提高了动态环境中的效率和安全,其中包括移动障碍物.
科学领域:
- 机器人和自动化 机器人和自动化
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 在现代物流和制造业中,自动引导车辆 (AGV) 是必不可少的.
- 高效和安全的路径规划对于AGV运营至关重要,特别是在动态环境中.
- 现有的算法如PSO,GA和TS面临着过早融合和实时障碍回避的挑战.
研究的目的:
- 提出一种新的生物粒子群集优化 (BPSO) 算法,用于AGV的全球路径规划.
- 将BPSO与Q-learning集成在一起,以进行可靠的本地路径规划和动态避难障碍.
- 评估结合BPSO-RL算法的性能与已建立的方法相比.
主要方法:
- 开发一个修改后的BPSO算法,结合基于随机角度的速度更新,以提高可搜索性和防止过早的融合.
- 实施Q-learning用于实时本地路径规划,以导航移动障碍.
- 整合BPSO用于全球路径规划和Q-learning用于本地路径规划,以创建BPSO-RL算法.
- 使用基准函数进行比较分析和使用动态障碍的计算实验.
主要成果:
- 与PSO,GA和TS相比,BPSO算法在单模优化问题上表现出卓越的性能,在更少的代和更短的运行时间中实现更好的适应性值.
- 集成的BPSO-RL算法成功生成了具有快速计算速度的全球最佳路径.
- BPSO-RL算法有效地处理了涉及移动障碍物的动态场景,在AGV路径规划实验中表现优于标准PSO算法.
结论:
- 拟议的BPSO-RL算法有效地结合了群体智能和强化学习,以实现高效和安全的AGV路径规划.
- 这种混合方法在处理动态环境和避免复杂障碍方面提供了显著的优势.
- BPSO-RL算法为提高自动引导车辆的操作能力提供了一个有希望的解决方案.
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