基于WOA-KAN的茶摘机器人敏捷手的阻抗控制方法
Xin Wang1, Shaowen Li1, Junjie Ou1
1Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
本研究介绍了使用鱼优化算法 (WOA) 和科尔摩戈罗夫-阿诺德网络 (KAN) 的机器人茶手的自适应阻抗控制. 这种方法提高了精确的力量跟踪,用于微妙的茶叶收获.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 机器人茶叶采摘需要精确的机械控制来收获微妙的茶蕾.
- 现有的阻抗控制方法经常与动态变化和参数优化作斗争.
研究的目的:
- 开发一种适应性阻抗控制方法,用于机器人巧妙的手来挑茶.
- 为了提高茶叶收获期间阻抗参数的准确性和实时调节性.
主要方法:
- 使用Kolmogorov-Arnold网络 (KAN) 实现了适应性阻抗控制框架,并具有立方B线激活功能.
- 使用鱼优化算法 (WOA) 优化了KAN的B-splines,以改善非线性拟合和全局优化.
- 集成的触觉传感器可以实时跟踪茶叶相互作用期间的力.
主要成果:
- 与动态条件下的固定参数控制相比,拟议的WOA-KAN方法显著减少了14.2%的超越和99.89%的稳定状态误差.
- 实验验证显示,在采茶过程中,在50Hz的控制频率下,最大超越约为6%.
- 证明了动态映射和实时阻抗参数调整,用于准确的茶叶芽接触力跟踪.
结论:
- 适应性阻抗控制方法有效地提高了机器人茶叶采摘的精度和稳定性.
- 在复杂的机器人任务中,WOA和KAN的集成提供了一种强大的方法来优化控制参数.
- 开发的算法显示了在自动化农业收获中实际应用的巨大潜力.
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