由多策略协作进化蜂算法驱动的农业机器人的基于网格的路径规划
1School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330000, China.
Biomimetics (Basel, Switzerland)
|August 27, 2025
概括
这项研究介绍了多策略协作进化蜂算法 (MCEHBA),用于改进农田中的移动机器人路径规划. MCEHBA提高了优化和路径的效率,在模拟和工程应用中展示了卓越的性能.
科学领域:
- 机器人和人工智能
- 优化算法
- 农业工程
背景情况:
- 在复杂的农田环境中,移动机器人面临着路径规划的挑战.
- 现有的算法往往缺乏农业应用的效率和适应性.
研究的目的:
- 开发一种先进的优化算法,即多策略协作进化蜂算法 (MCEHBA),用于在农田中增强移动机器人路径规划.
- 提高全球勘探,当地开发,人口多样性和寻找路径的效率.
主要方法:
- 集成基于正弦函数的非线性收因子,用于动态勘探-开发平衡.
- 纳入差异进化和以重力为中心的基于对立的学习,以增强人口多样性和搜索效率.
- 使用良好的点设置初始化和分散的边界约束处理以提高准确性和速度.
主要成果:
- 在Friedman和Nemenyi测试中,MCEHBA在CEC2017基准函数集上展示了卓越的优化能力.
- 该算法在三个工程应用问题中实现了最小的目标函数值,超过其他六种算法.
- 在农田模拟中,MCEHBA生成了总成本最小的路径,显示出卓越的全球趋同性和实际适用性.
结论:
- MCEHBA为农业环境的移动机器人路径规划提供了显著的进步.
- 该算法的多策略方法提高了其稳定性,效率和适用于现实世界的工程挑战.
- MCEHBA为优化复杂的大型农田的自主导航提供了一个有前途的解决方案.
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