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Orchard traveling UGV using particle filter based localization and inverse optimal control

机译:使用基于粒子滤波的定位和逆最优控制的果园UGV

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The authors previously proposed an Unmanned Ground Vehicle (UGV) in an orchard as a base platform for autonomous robot systems for performing tasks such as monitoring, pesticide spraying, and harvesting. To control a UGV in a semi-natural environment, accurate self-localization and a control law that is robust under large disturbances from rough terrain are the first priorities. In this paper, a self-localization algorithm consisting of a 2D laser range finder and the particle filter is proposed. A robust nonlinear control law and a path regeneration algorithm that the authors proposed for underactuated mobile robots are combined with the localization method and applied to a drive-by-wire experimental vehicle. Excellent experimental results were obtained for traveling through a real orchard. The standard deviation of the control error in the lateral direction was less than 15cm.
机译:作者之前曾在果园中提出无人地面车辆(UGV),作为自主机器人系统执行诸如监视,农药喷洒和收获等任务的基础平台。为了在半自然环境中控制UGV,首先要考虑的是精确的自我定位和在恶劣地形引起的强烈干扰下具有鲁棒性的控制律。本文提出了一种由二维激光测距仪和粒子滤波器组成的自定位算法。作者针对欠驱动的移动机器人提出了一种鲁棒的非线性控制律和路径再生算法,并将其与定位方法结合起来,并应用于电传驾驶实验车辆。通过真实的果园获得了出色的实验结果。横向控制误差的标准偏差小于15cm。

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