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AgriPath:一个强大的多目标路径规划框架,用于农业机器人在动态的田野环境中
Chenghan Yang1, Dingkun Zheng1, Siming Chen2
1Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, Kazakhstan.
Frontiers in plant science
|December 15, 2025
概括
本研究介绍了AgriPath,这是农业机器人路径规划的新框架. 通过使用先进的AI算法,AgriPath提高了复杂领域的导航效率和障碍回避.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 精准农业 精准农业 精准农业
背景情况:
- 农业机器人需要有效的路径规划,以进行复杂的野外导航.
- 传统的算法与动态障碍,密集的植被和非结构化的地形作斗争.
- 现有的方法缺乏在精准农业环境中的效率和适应性.
研究的目的:
- 推出AgriPath,一个强大的农业机器人的多目标路径规划框架.
- 加强在具有挑战性的农业环境中寻找路径,提高融合效率和避免障碍.
- 为准确农业中自主导航提供更具适应性的解决方案.
主要方法:
- AgriPath集成了改进的卷积神经网络 (CNN) 与因果卷积和自我注意力,用于轨迹预测.
- 一个改进的A*算法利用动态启发函数 (NDVI) 和卡尔曼过来实现全局路径适应性.
- 一个改进的鱼优化算法 (IWOA) 平衡了路径长度,流性和规划时间,并补充了道格拉斯-皮克克和B-spline平滑.
主要成果:
- 在实验中,AgriPath表现出比SBREA*,殖民地A*,果园A*和贪A*更高的性能.
- 该框架实现了更好的路径长度,流性,规划时间和动态避障成功率.
- 结果表明,农业机器人导航的优越多目标优化平衡.
结论:
- AgriPath显著提高了农业机器人路径规划的效率和稳定性.
- 该框架为精密农业中的自主导航提供了更具适应性的解决方案.
- 这项研究为农业机器人中的路径规划提供了新的理论和实践方向.
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