用于精确园艺的自主机器人导航

T. Hague, J. Marchant, N. Tillett
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引用次数: 26

摘要

某些实验性园艺作业需要一辆能够在农田中导航的自动驾驶车辆。由于车辆必须能够在许多领域进行经济操作,人工导航信标和详细的事先地图是不利的。一种新的导航方案已经被设计出来,它允许作物行本身被用作导航辅助。车辆通过场地的命令路径由路径曲率表示为向前距离的函数;部分路径被标记为与作物行对齐。利用扩展卡尔曼滤波(EKF)将图像分析数据与固态罗盘数据和航位推算相结合。EKF不是在任意笛卡尔坐标系中定位,而是根据指令路径估计位置和方向误差,这可以直接用于车辆运动的反馈控制。该方法已在一辆小型园艺车辆上实施,使其能够完全自主地跟随作物行并在行尾转弯。该系统已在田间进行了测试,结果表明了沿作物行引导的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Autonomous robot navigation for precision horticulture
Certain experimental horticultural operations require an autonomous vehicle capable of navigating through a field of crop plants. Since the vehicle must be able to operate in many fields to be economic, artificial navigation beacons and detailed prior maps are disadvantageous. A novel navigation scheme has been devised, which allows the crop rows themselves to be used as a navigation aid. The commanded path of the vehicle through the field is expressed by path curvature as a function of forward distance; parts of the path are marked as being aligned with crop rows. Data from image analysis is combined with that from a solid state compass and dead reckoning using an extended Kalman filter (EKF). Rather than position in an arbitrary Cartesian coordinate frame, the EKF estimates position and orientation error from the commanded path, which may be used directly for feedback control of vehicle motion. The method has been implemented on a small horticultural vehicle, allowing it to follow crop rows and turn at the end of the rows fully autonomously. The system has been tested in the field, and results showing the accuracy of guidance along crop rows are presented.
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