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基于圆排斥范围边界约束潜力场方法的果园割草机器人的本地路径规划研究
Wenyu Zhang1, Ye Zeng1, Sifan Wang1
1College of Engineering, South China Agricultural University, Guangzhou, China.
Frontiers in plant science
|August 7, 2023
概括
这项研究介绍了一种改进的人工潜力场算法,用于果园割草机器人. 改进的算法通过防止机器人越过果树边界来确保更安全的导航,从而导致更短,更流的路径.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 农业工程 农业工程
- 人工智能的人工智能
背景情况:
- 果园环境为自主割草机器人带来了复杂的地形挑战.
- 运营安全受到不可预测的障碍和避免损害果树的必要性所影响.
研究的目的:
- 开发一个改进的本地路径规划算法,用于人工潜在的基于现场导航.
- 在复杂的果园地形中提高割草机器人的安全性和效率.
主要方法:
- 引入了一个圆的排斥潜力场来定义边界.
- 利用了一个改进的可变多项式潜在场函数,具有距离因子.
- 整合了一个水果树边界潜力场,以防止穿越指定区域.
主要成果:
- 改进的算法有效地解决了不可达到的目标和局部最小值的问题.
- 通过将排斥场范围修改为圆,减少了环境潜在场的复杂性.
- 与传统的人工潜能方法相比,实现了6.78%更短的路径长度.
- 证明了更短,更光滑的路径,具有卓越的避障能力.
结论:
- 增强的人工潜力场算法显著提高了果园中自动割草机器人的安全性和效率.
- 该算法能够先发制人地避开障碍物,并尊重果树边界的能力使其适合于独立的机器人操作.
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