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Accurate GPS-free Positioning of Utility Vehicles for Specialty Agriculture

机译:精确的GPS无用的公用事业车辆用于特种农业的定位

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This paper presents methods for determining the position of a robotic utility vehicle to sub-meter accuracy without the use of GPS. The approach we use is ideally suited for specialty agriculture applications such as orchards, where commercially available high-accuracy GPS systems are cost-prohibitive and GPS signal interference due to tree canopy can produce unreliable results. Solving the positioning problem provides a foundation for other tasks in precision agriculture that can be conducted with autonomous orpartially-Mitomated vehicles. Our algorithms use an Extended Kalman Filter with a suite of sensors. Given an initial estwate of vehicle position, sensors on the wheels and steering linkage are used to predict the path traveled, and then a scanning laser range finder is used to correct this predicted position by measuring the relative position between the vehicle and landmarks in the field. We have experimented with intentionally placed landmarks that use reflective tape, which can easily beidentified with the laser. In this paper we present the motivation behind our techniques, the specifics of the algorithms we use, the experimental setups, and the resu'ts of field tests conducted during the summer of 2009 from apple orchards in Pennsylvania. Our results provide sub-meter accuracy, and suggest strong promise for reliable localization solutions for commercial applications.
机译:本文介绍了在不使用GPS的情况下确定机器人型多功能车辆到亚米精度的方法。我们使用的方法非常适合果园等特种农业应用,其中商业上可获得的高精度GPS系统是由于树冠而导致的高精度和GPS信号干扰可以产生不可靠的结果。解决定位问题为可用自主或部分典型车辆进行的精密农业中的其他任务提供了基础。我们的算法使用延长的卡尔曼滤波器,具有一套传感器。给定车辆位置的初始源泉,车轮上的传感器和转向连杆用于预测行进的路径,然后通过测量车辆和领域的地标之间的相对位置来校正扫描激光测距仪。 。我们已经尝试了有意地放置使用反射胶带的地标,这可以轻松地用激光替代。在本文中,我们提出了我们使用的技术背后的动机,我们使用的算法,实验设置以及2009年夏季宾夕法尼亚州苹果园进行的现场测试的裁定。我们的结果提供了次尺度的准确性,并为商业应用的可靠本地化解决方案提出了强烈的承诺。

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